Comprehensive experimental performance investigation of conducted electromagnetic interference in split-phase induction motors: Common-mode

Abstract

Motors in Adjustable Speed Drive (ASD) systems are the major sources of conducted Electromagnetic Interference (EMI), and they are mainly the Common-Mode (CM) currents and voltages. Compliance with Electromagnetic Compatibility (EMC) standards is of utmost importance when maintaining system reliability in the face of ever-stricter Electromagnetic Compatibility standards in the industrial sectors. This work presents the first systematic experimental evaluation of CM impedance in Split Phase Induction Motors (SPIMs) in a wide frequency range (100 Hz to 100 MHz). Unlike prior studies that were limited to either a differential-mode analysis or limited frequencies in the experiment, the study provides comprehensive CM impedance data of two different SPIM setups, explaining resonance and anti-resonance behaviors that have direct implications on EMC performance. It is experimentally proven that high impedance designed motors significantly reduce CM current transfer, thus reducing EMI emissions and enhance EMC compliance. Particularly, the impedance peak of SPIM (I) was 8k at 100 MHz that translated to a 45% decrease in CM current and −15 dB attenuation of conducted EMI compared to SPIM (II). The resonance and anti-resonance frequencies determined the influence of motor architecture on its susceptibility to EMI. As a result, the findings provide prescriptive design information to the optimization of SPIMs in applications, for example, industrial automation and electric vehicle platforms, where very high EMI mitigation levels are of crucial importance.

Keywords

high-frequency electromagnetic compatibility common-mode split-phase induction motor impedance

Introduction

EMC turned into a vital study area given the increased usage of converters,^1,2 variable-speed drives,³ and renewable energy systems^4,5 within power electronics plus electrical machines. EMI sources along with mitigation strategies have been investigated in recent studies for various applications. Examples can include LED drivers,⁶ toroidal inductors,⁷ buck converters,⁸ DC/DC converters,⁹ and even photovoltaic systems.¹⁰ Other works have examined EMI propagation in power converters and inverter–cable–machine systems,¹¹ emphasizing both CM and DM disturbances.¹² Several studies have also highlighted the role of shielding¹³ and filter optimization in reducing EMI levels.^14,15,16 These findings collectively demonstrate the importance of systematic EMI characterization as a foundation for improving EMC compliance. However, despite the large body of research on power converters and three-phase machines, investigations specifically addressing the high-frequency CM impedance of SPIMs remain scarce (Table 1).

Table 1.

Enumeration of abbreviations referenced in the manuscript.

Abbreviation	Full form
ASD	Adjustable speed drive
CM	Common-mode
DM	Differential-mode
EMI	Electromagnetic interference
EMC	Electromagnetic compatibility
SPIM	Split-phase induction motor
CSSIM	Capacitor-start single-induction motor
CRSIM	Capacitor-run single-induction motor
HF	High frequency
IoT	Internet of things
EV	Electric vehicle
PWM	Pulse width modulation
MRI	Magnetic resonance imaging
SOLT	Short–Open–Load–Through
AI	Artificial intelligence
EMC	Electromagnetic compatibility
EMI	Electromagnetic interference
CM	Common-mode
DM	Differential-mode
SPIMs	Split-phase induction motors
HF	High-frequency
MRI	Magnetic resonance imaging
ASDs	Adjustable speed drives
CSSIMs	Capacitor-start single-phase induction motors

In the field of electrical machines, both academic and industrial research continue to advance technologies by addressing emerging challenges and enhancing system performance. Current research spans mathematical modeling,¹⁷ advanced control strategies,¹⁸ observability analysis,¹⁹ fault diagnosis,²⁰ power quality improvement,²¹ efficiency optimization, and EMC.²² With the growing demand for high-performance and EMC-compliant systems, greater emphasis is now placed on integrating EMC considerations directly into the design, control, and operation of electrical machines.

EMI poses critical challenges across multiple sectors, where HF noise and conducted emissions can cause malfunctions and safety risks.²³ In the automotive industry, EMI may disrupt battery management and advanced driver-assistance systems in electric vehicles, leading to operational failures and compromised passenger safety.²⁴ In aerospace applications, EMI affects avionics, radar, and communication systems, potentially causing navigation errors or corrupting flight-critical data.²⁵ The medical field is also highly sensitive, particularly in MRI, where EMI from nearby equipment such as adjustable-speed drives, motors, or power electronics can degrade image quality, introduce artifacts, or even disrupt operation.²⁶ Ensuring EMC in MRI environments requires shielding, EMI filtering, and careful motor design to minimize conducted and radiated emissions. Renewable energy systems, including wind turbines²⁷ and photovoltaic inverters,²⁸ also rely heavily on power-electronic converters that are vulnerable to EMI, resulting in reduced efficiency, grid instability, and failures in smart-grid communications.²⁹ Similarly, in industrial automation, EMI affects sensors, programmable logic controllers, and robotic systems, leading to false triggering, reduced data accuracy, and operational failures in smart manufacturing environments.³⁰

EMI remains a major challenge in modern electrical engineering,³¹ especially with the growing integration of ASDs,³² and electric motors in industrial and commercial applications.³³ If not properly managed, EMI can cause data corruption, signal interference, equipment malfunction, and increased system noise, ultimately compromising system reliability and safety. With global EMC standards becoming more stringent,³⁴ effective EMI mitigation is essential not only for regulatory compliance but also for performance optimization and operational stability in advanced electrical systems.^35,36,37 Sectors such as automotive, aerospace, renewable energy, and industrial automation are particularly vulnerable. For example, EMI can disrupt communication networks in electric vehicles, interfere with sensor-based control in smart grids, and cause failures in medical and aerospace electronics. Given the increasing reliance on energy-efficient and EMC-compliant motor-driven systems, addressing EMI in electric motors has become a critical requirement for ensuring safety, reliability, and compliance with evolving standards.

Although EMC and EMI have been extensively studied in static converters^38,39 and three-phase electrical machines,⁴⁰ SPIMs have received comparatively little attention with respect to HF behavior and EMI mitigation. In ASD systems, motors significantly influence the propagation of conducted disturbances and CM currents, both of which are crucial for EMC performance.⁴¹ Because SPIMs are widely used in consumer appliances, industrial automation, and distributed energy systems, understanding their HF impedance behavior is particularly important. While research on three-phase machines has advanced significantly,⁴² the EMC challenges unique to SPIMs remain insufficiently addressed.⁴³ A more detailed investigation into their HF behavior is therefore essential for compliance with tightening EMC requirements.

Several studies have explored SPIM design and control, though often without focusing directly on EMC. Klaus et al. proposed a cost-effective method for speed control of capacitor-run SPIMs, emphasizing line reactions and EMC.^44,45 Moreno et al. developed a predictive current controller for field-oriented control, improving performance precision.⁴⁶ Vega et al. introduced a neural inverse optimal control technique to optimize flux and mechanical speed,⁴⁷ while⁴⁸ applied PWM with a diode and single MOSFET to enhance efficiency and power factor. Saloumi et al. designed a modified diode-clamped inverter-fed SPIM with closed-loop scalar control, achieving improved voltage regulation.⁴⁹ Chen et al. analyzed electromagnetic force characteristics in single-phase concentrated winding motors, contributing to vibration and noise reduction.⁵⁰ Ref. 51 proposed a single-phase AC–AC converter with extinction angle control, enhancing efficiency and simplicity. Alviento and Cabais developed a real-time fault detection system using accelerometers and temperature sensors interfaced with Arduino.⁵² Hagras and Mahfouz introduced a nonsingular fast terminal sliding mode control method to improve speed control robustness.⁵³ Wang et al. studied load-dependent electromagnetic behavior in capacitor-run SPIMs, providing insights into magnetic fields, losses, and torque.⁵⁴ Saneie and Nasiri-Gheidari presented an analytical model based on a magnetic equivalent circuit to improve design accuracy.⁴¹ Ref. 55 investigated performance under various conditions using a three-leg inverter and power-sharing controllers to reduce torque pulsations. Finally, Ref. 56 examined sensorless torque control for electric vehicles with open-end winding induction motors, focusing on optimizing power factor and reducing harmonic distortion.

Ref. 57 analyzed the performance of energy-efficient single-phase induction motors, highlighting how optimized design reduces power losses and improves overall efficiency. Sharma and Singh developed a novel energy-efficient SPIM for ceiling fan applications using Taguchi’s orthogonal arrays, achieving significant efficiency gains and a reduction in EMI.⁵⁸ These works illustrate the broader evolution of motor technology, where efficiency improvements, fault-tolerant design, and EMC considerations are increasingly integrated for industrial and automotive applications. Bermaki et al. proposed a predictive method for DM impedance in universal motors, analyzing each winding separately (armature, series winding, and inductive compensating winding). Their model circuits, validated up to 1 MHz, provide valuable tools for EMC simulations.⁴² Similarly, Miloudi et al. investigated the CM behavior of CSSIMs using genetic algorithms, although their study was restricted to the 150 kHz–30 MHz range.⁵⁹

Despite these contributions, a critical gap remains in understanding CM impedance characteristics in SPIMs across a wider frequency spectrum. Addressing this gap is essential for EMC-optimized designs, reducing EMI emissions, and ensuring robust motor performance under high-frequency industrial and commercial operating conditions. Previous research has largely overlooked detailed comparisons of EMC performance among SPIM types, underscoring the urgent need for further investigation. Although SPIMs are widely deployed in consumer appliances, automation, and distributed energy systems, their CM impedance characteristics and influence on EMI propagation have not been systematically studied. Most existing works emphasize control strategies and efficiency improvements, leaving EMC performance across broad frequency ranges unexplored.

This study bridges that gap by analyzing CM impedance in SPIMs over 100 Hz–100 MHz, surpassing previous investigations limited to 150 kHz–30 MHz.^44,46,58,60 While EMC in three-phase machines has been extensively studied, SPIMs remain underexplored with respect to high-frequency behavior and EMI suppression. To our knowledge, this work provides the first detailed characterization of CM impedance in SPIMs across 100 Hz–100 MHz, significantly extending the frequency coverage of earlier studies.^{61,62,63,64,65}

A review of the literature reveals only one prior study that examined the HF impedance of SPIMs,⁵⁹ but it was restricted to DM analysis and did not consider CM impedance, which is more critical for determining conducted EMI levels and represents the main challenge in EMC compliance. To contextualize this contribution, Table 2 presents a summary of prior investigations on different single-phase motors, highlighting the diverse methodologies employed and illustrating the scarcity of CM-focused research on SPIMs.

Table 2.

Literature review for different studies and analyses of single-phase motors.

Reference	Motor	EMC/HF	Control	Design	Diagnostic	Cost
¹		x
^47,59,66			x
^61,62				X
^63,64	CSSIM				x
⁴⁸						x
⁶⁷	CRSIM	x
⁶⁸				X
²⁰					x
⁴²	SPIM		x
⁵⁹		x (DM)

The significance of this study lies in its potential impact across several critical sectors of electrical engineering. By analyzing EMI sources and propagation mechanisms in electric motors, it provides essential insights for improving system reliability and safety. As industries accelerate the shift toward sustainable technologies—particularly electric vehicles and renewable energy systems—effective EMI management will be a decisive factor in ensuring performance and compliance. Beyond addressing current challenges, this work also lays the groundwork for future technological developments, offering manufacturers practical strategies to meet evolving EMC standards while enabling the design of next-generation motors and drive systems. These future systems will combine optimal performance with regulatory compliance, strengthening competitiveness in a rapidly evolving marketplace. Consequently, this study makes a timely and valuable contribution to one of the most pressing challenges in modern electrical engineering, with broad implications for industry, technology, and society.

This research represents the first in-depth experimental investigation of CM impedance behavior in SPIMs, highlighting how winding configurations and electrical parameters influence EMC performance. Unlike previous studies that focused primarily on control and efficiency, this work emphasizes CM impedance variations and their direct role in EMI mitigation. The main contributions of this study are

⁃ Comprehensive CM impedance characterization of two distinct SPIM designs, identifying resonance and anti-resonance behaviors that govern EMI propagation.

⁃ Detailed experimental comparison of EMC performance across low-, mid-, and high-frequency ranges (100 Hz–100 MHz).

⁃ Identification of critical impedance variations across frequency bands, providing reference data for predicting EMI susceptibility and optimizing SPIM design.

⁃ Accurate experimental validation using the Wayne Kerr 6500B impedance analyzer, ensuring precision and repeatability.

⁃ Application-oriented insights for optimizing SPIMs in industrial automation, electric vehicles, and Internet of Things (IoT) systems, where EMI control is vital for stability.

All measurements were performed in a controlled environment with the Wayne Kerr 6500B analyzer, a high-precision instrument calibrated for high-frequency EMI characterization. This ensures the reliability of the experimental data and strengthens the validity of the conclusions. Overall, the study establishes a robust framework for analyzing CM impedance and EMI interactions in SPIMs, filling a critical knowledge gap and paving the way for EMC-optimized motor designs that meet the requirements of next-generation industrial and automotive applications.

The central hypothesis of this study is that higher Common-Mode impedance in SPIMs, particularly at high frequencies, directly leads to lower CM current propagation and reduced EMI emissions. The guiding research question is: How do variations in winding configuration and motor design influence CM impedance characteristics over the frequency range 100 Hz–100 MHz, and what are the implications for EMI suppression and EMC compliance? Importantly, this work presents the first comprehensive experimental investigation of CM impedance in SPIMs across such a wide frequency range, filling a critical gap in the existing literature.

Although this study does not introduce a new physical mechanism—since induction machines are known to exhibit capacitive behavior at low frequency and inductive behavior at high frequency—it represents the first comprehensive experimental investigation of SPIMs focusing on CM impedance over the broad range of 100 Hz–100 MHz. Previous works have been either limited to three-phase machines or restricted to differential-mode impedance in SPIMs with narrower frequency coverage. By systematically analyzing two SPIM configurations, this study provides practical numerical benchmarks and design-oriented insights that were previously unavailable in the literature.

This paper is organized to provide a comprehensive analysis of EMC and CM behavior in SPIMs, with a focus on high-frequency impedance characteristics and EMI mitigation strategies. Section 2 describes the experimental setup, measurement techniques, and instrumentation used to ensure accurate and reliable impedance data across 100 Hz–100 MHz. Sections 3 and 4 present a detailed examination of CM impedance variations in two SPIM designs, analyzing the influence of winding configurations, resonance effects, and frequency-dependent behavior on EMC performance. Within Section 4, a comparative study is conducted between the two motors, supported by Sub-section 4.2, which introduces practical EMC design recommendations such as optimized winding configurations, shielding techniques, and CM filtering strategies. In addition, Sub-section 4.3 discusses the implications of the findings for CM filter design, providing guidance on the selection of inductors and capacitors to enhance EMC compliance. Section 5 expands the comparison, highlighting key design optimizations and their implications for reducing EMI emissions. Finally, Section 6 summarizes the main findings, provides practical EMC design guidance, and outlines directions for future research, including mathematical modeling approaches to further advance SPIM EMC analysis. This structured approach ensures a systematic investigation of SPIM EMC behavior while delivering practical insights for optimizing motor designs in industrial, automotive, and IoT applications.

Measurement setups

Impedance measurement requires impedance analyzers, precise instruments designed and specialized for impedance characterization. High precision as well as sensitivity are certainly offered since these analyzers operate across a frequency range that is quite wide. People commonly use them so as to match impedance, to design filters, as well as to control quality in HF circuits and systems. HF and impedance measurement methods are important tools for one and all. They accurately help characterize the electrical behavior of components and systems at high frequencies. People can build solid reliable solutions for current HF applications via these methods. Figure 1 depicts all of the experimental configurations that are for the HF CM analysis within a 100 Hz to 100 MHz range, and also it depicts all of the different configurations as used in this study. Figure 2 shows the experimental setup for measuring the CM impedances of SPIMs. In a pioneering effort, the Wayne Kerr impedance analyzer (6500B) was employed to measure impedances and phase angles across a frequency range from 100 Hz to 100 MHz. To ensure precise conditions, measurements were conducted with the rotor stationery and power supply cables disconnected. The parameters of both motors are presented in Table 3.

Figure 1.

HF experimental CM configurations: (a) Motor impedance; (b) Main winding; and (c) Auxiliary winding.

Figure 2.

HF experimental CM setup.

Table 3.

SPIMs parameters.

Parameters	SPIM (I)	SPIM (II)
Voltage (V)	220	220
Current (A)	1.5	1.5
Frequency (Hz)	50	50
Power (W)	175	175
Speed (rpm)	1400	1400
Poles	2	2
Resistance of main winding (Ohm)	23	9.8
Leakage inductance of main winding (Henry)	0.3	0.23
Resistance of auxiliary winding (Ohm)	21.7	23.3
Leakage inductance of auxiliary winding (Henry)	0.281	0.145

Figure 1 presents the synoptic diagram of the measurement configuration, showing how the motor is connected to the spectrum (impedance) analyzer for the extraction of CM, main winding, and auxiliary winding impedances. Figure 2 provides a real image of the experimental setup. For CM impedance measurements, the terminals of the main and auxiliary windings were shorted together and connected to the high (H) terminal of the analyzer, while the motor chassis was connected to the low (L) terminal, forming a return path to ground. This setup captures the total CM impedance seen between the stator windings and ground. The impedance analyzer was validated before each session using a known RLC calibration network provided by the manufacturer. Measured results matched theoretical impedance within ±0.05%, confirming system accuracy.

All CM impedance measurements were performed with the motors in a standstill condition, consistent with IEC 61800-3 and CISPR 25 standards, which recommend subsystem characterization under static conditions. This ensures stable, repeatable measurements free from PWM harmonics or load effects, allowing isolation of the motor’s intrinsic parasitic elements (winding-to-ground capacitances, leakage inductances, and resistances). These structural properties remain valid under drive operation, making standstill impedance a reliable baseline for EMI assessment, while full PWM-driven validation is identified as future work.

For all impedance measurements, the SPIMs were connected to the Wayne Kerr 6500B impedance analyzer using high-quality coaxial cables (type RG-58, 50 Ω characteristic impedance) to minimize external interference and ensure measurement accuracy. Prior to each measurement, a full short–open–load–through (SOLT) calibration was performed on the analyzer to eliminate systematic errors and guarantee reliable results across the 100 Hz–100 MHz frequency range. During testing, the rotor of the motor was kept stationary, and all supply cables were disconnected to isolate the motor under test. The chassis was grounded to reduce parasitic effects. Both the main and auxiliary windings were connected individually to the analyzer for independent characterization, in addition to the complete motor configuration. This procedure ensured that the impedance and phase behavior could be accurately attributed to each winding and to the overall motor structure.

The Wayne Kerr impedance analyzer (6500B) was selected for this study due to its adherence to the following criteria.

1. It has a proven accuracy in previous similar studies and is widely used in the scientific community.

2. It meets the IEEE Std 112-2017 requirements in terms of reliability.

3. It features a 24-pin IEEE 488 connector allowing external control by a computer or another instrument.

4. It has an excellent impedance accuracy at ± 0.05%. The dissipation factor accuracy is ±0.0005, and quality factor accuracy is ±0.05%.

5. It fulfills our requirements in terms of resolution, equivalent circuit analysis, material testing, multi-measurement mode, polar complex plots, and fault detection.

6. In addition, the authors calibrated the analyzer before using it to ensure that the recorded measurements are reliable.

7. The analyzer is versatile and has the ability to measure a wide range of impedance parameters, including resistance, inductance, and capacitance, which makes it a suitable choice for this research.

8. The analyzer has a user-friendly interface which implifies the process of conducting a large number of measurements.

Experimental results of HF CM impedances measurement

Figure 3 illustrates the CM impedance characteristics of SPIM (I), including the total CM motor impedance, main winding impedance, and auxiliary winding impedance. A consistent trend is observed: as frequency increases up to 10 MHz, all impedances decline at a rate of −20 dB/decade, primarily due to the capacitive behavior of the windings. Since capacitive reactance decreases with frequency, the impedance reduction facilitates higher CM currents, potentially increasing conducted EMI emissions.

Figure 3.

HF CM SPIM (I) impedances.

As shown in Figure 3, in the low-frequency range (100 Hz to 33 kHz), the CM motor impedance remains slightly lower than that of the individual windings, with the main winding impedance aligning more closely to the overall motor impedance. From 33 kHz to 10 MHz, the impedances of the main and auxiliary windings start to diverge, though they remain relatively close to the total CM motor impedance. Notably, even when the auxiliary winding is disconnected, it continues to influence the motor’s overall CM impedance, emphasizing the interplay between windings in determining EMC performance.

Beyond 10 MHz, the impedance behavior shifts as the capacitive effect diminishes, and inductive effects become dominant, leading to an impedance rise at +20 dB/decade. As shown in Figure 3, at 10 MHz, the CM impedance increases significantly, reaching 12 Ω, effectively restricting CM current propagation. This transition results in a notable reduction in CM voltage and current, thereby lowering conducted and radiated EMI risks and improving overall EMC performance.

The role of the main winding in defining the motor’s CM path is evident, as its impedance closely follows the total CM motor impedance, particularly at low to mid frequencies. While the auxiliary winding plays a secondary role, its influence becomes more prominent above 10 MHz, where both windings contribute significantly to impedance variations. The emission characteristics of high-frequency conductors thus underscore the need for optimization of the winding impedance characteristics to maximize EMC performance in high-frequency applications.

Below 10 MHz, the impedances of the main winding, auxiliary winding, and motor exhibit nearly identical behavior, indicating strong coupling between the main and auxiliary windings at these frequencies. The comparative analysis shows that the impedance of the primary winding is very close to the impedance of the Common-Mode motor, but the auxiliary winding has a stronger effect on both Common-Mode path current and Common-Mode voltage. A primary and auxiliary winding, on the other hand, in the frequency range between 10 MHz and 100 MHz around 100 MHz, also introduces a major impact on the impedance behavior.

The contribution inductive increases with greater power and causes an increase in impedance that is dependent upon frequency. This conversion then results in a significant reduction of the Common-Mode voltage and current and thus reduces the possibility of both conducted and radiated electromagnetic interference and improves the overall electromagnetic compatibility performance. The noted reaction of the original winding in comparison with the motor impedance highlights the central part played by this reaction in the transmission of Common-Mode current. The auxiliary winding, while still influential, contributes less to the CM path, particularly at lower frequencies. This underscores the importance of carefully considering winding impedance characteristics in the design and optimization of SPIMs for enhanced EMC performance.

As the frequency increases from 100 Hz to 10 MHz, the phase angle response of the windings exhibits a predominantly capacitive nature (Figure 4). Within this range, the phase angles of both the motor and main winding impedances gradually shift towards negative values, indicating increasing capacitive reactance. This trend highlights the dominant role of parasitic capacitances in shaping the impedance behavior at lower frequencies.

Figure 4.

HF CM SPIM (I) phases.

Between 10 MHz and 30 MHz, the phase angles of the motor, main winding, and auxiliary winding begin to converge, exhibiting nearly identical characteristics. This convergence marks a transition from capacitive to inductive dominance, where the inductive effects of the windings become more pronounced. The alignment of phase angles reinforces the impact of mutual inductance and inter-winding coupling, affecting the motor’s overall EMI susceptibility.

Beyond 30 MHz, the phase angles of the main and auxiliary windings closely align with the motor impedance phase, signifying stronger electromagnetic coupling. This uniform phase behavior at higher frequencies facilitates increased CM current propagation with minimal phase shift, emphasizing the critical influence of capacitive-inductive coupling on the motor’s high-frequency impedance characteristics. Understanding these phase variations is essential for EMC optimization, particularly in mitigating high-frequency EMI in sensitive applications such as industrial automation and medical systems.

To effectively illustrate the distinct behaviors of the impedances, CM currents, CM voltage, EMC performance, and their impact on the motor’s CM impedance across various frequencies, the analysis can be comprehensively summarized in Table 4. This table provides a clear comparison, highlighting the variations and interactions between these parameters and their influence on the overall EMC of the SPIM (I).

Table 4.

EMC SPIM (I) performances.

Performance	[100 Hz–10 MHz]	[10 MHz–100 MHz]
Impact on CM SPIM (I) impedance	The main winding predominantly influences CM impedance over the auxiliary winding	Both main and auxiliary windings significantly impact CM impedance
CM voltage and current	At mid-range frequencies, significant disruptive currents and voltages occur	CM voltage and current significantly decrease with rising frequency
CM voltage and current	A notable increase in rising frequency	Reduced conducted and radiated EMI
EMC performance	Poor EMC performance at mid-range frequencies	Potential improvement in EMC performance at higher frequencies

To provide a more quantitative interpretation of the phase variations, Table 5 summarizes the minimum, maximum, and mean phase angles recorded across the entire frequency range (100 Hz–100 MHz). The results show that SPIM (I) exhibits wide variations, from approximately −157° to +163°, confirming strong capacitive–inductive transitions. The main winding reaches the highest positive phase angle (+169°), while the auxiliary winding peaks at +126°, indicating a less pronounced inductive dominance. The average values remain negative (−50° to −54°), highlighting that capacitive behavior predominates at low and mid frequencies. These numerical observations complement Figures 4, 5, and 6 by confirming the distinct contributions of the main and auxiliary windings to overall EMC performance.

Table 5.

Numerical variations of phase angles (100 Hz–100 MHz).

Component	Minimum (°)	Maximum (°)	Mean (°)
SPIM (I) (overall motor)	−157.1	+162.9	−50.9
Main winding (I)	−139.7	+168.8	−52.5
Auxiliary winding (I)	−140.7	+125.8	−54.2

Figure 5.

HF CM SPIM (II) phases.

Figure 6.

HF CM main winding impedances.

Figures 7 and 5 present the magnitude and phase characteristics of the CM impedance in SPIM (II), emphasizing the dominant influence of the main winding on high-frequency behavior. This effect arises due to the main winding’s resistance and inductance, which significantly shape the impedance profile. As shown in Figure 7, the impedance exhibits a steady decline up to 10 MHz, reaching 10 Ω driven by the capacitive nature of the windings. This behavior is expected, as capacitive reactance decreases with frequency, leading to a progressive reduction in CM impedance. Compared to the auxiliary winding, the main winding exerts a stronger influence on the CM impedance due to its larger capacitance contribution in this frequency range.

Figure 7.

HF CM SPIM (II) impedances.

At 10 MHz, a distinct resonance dip is observed in the CM impedance curve of SPIM (II), indicating a frequency point where impedance reaches a local minimum before transitioning to an inductive-dominant behavior. Beyond 10 MHz, the impedance profile marks the emergence of inductive reactance as the dominant factor. As illustrated in Figure 5, this inductive transition leads to a notable increase in impedance levels, effectively reducing CM voltage and current propagation. This shift plays a crucial role in lowering conducted and radiated EMI, improving EMC performance in high-frequency applications.

The phase angle variations of SPIM (II) across the full frequency range (100 Hz–100 MHz) are shown in Figure 5. The motor demonstrates a clear capacitive trend at low frequencies, with phase angles approaching −90°, followed by a transition toward inductive behavior beyond 10 MHz. The maximum observed phase shift exceeds +160°, indicating strong inductive dominance at higher frequencies. Compared to SPIM (I), SPIM (II) exhibits lower average phase values in the mid-frequency range, suggesting weaker impedance against Common-Mode currents. These results confirm that SPIM (I) provides superior EMC performance, while SPIM (II) remains more susceptible to high-frequency EMI propagation.

In the low-to mid-frequency range (100 Hz–10 MHz), the main winding exerts a more pronounced influence on CM impedance than the auxiliary winding, primarily due to its higher capacitive reactance. This capacitive behavior amplifies CM currents and voltages, resulting in similar impedance profiles across both windings and diminishing EMC performance within this range. However, as frequency increases beyond 10 MHz, the main winding’s inductive contributio becomes more pronounced, causing a rise in impedance levels.

The resulting shift significantly reduces common-mode currents and reduces conducted and radiated EMI risks. At higher frequency regimes, the simultaneous nature of the two windings makes the complexity of managed EMC performance particularly important to SPIM (II). Recent practical trends in impedance highlight the importance of applying mitigation strategies, such as tailored winding structures, strong shielding, and cutting-edge filtering techniques, to effectively manage high-frequency EMI intrusion. Table 6 provides a complete overview to describe the disparate dynamics of impedance, Common-Mode currents, Common-Mode voltage, EMC performance, and the resultant effect of the Common-Mode impedance of the motor across a range of frequencies.

Table 6.

EMC SPIM (II) performances.

Performance	[100 Hz–10 MHz]	[10 MHz–100 MHz]
Impact on CM SPIM (II) impedance	The main winding exerts a more pronounced effect on the CM impedance than the auxiliary winding	The main winding exerts a more substantial influence on the CM impedance compared to the auxiliary winding
CM voltage and current	At LF, the presence of CM currents and voltages are minimal, leading to relatively stable EMC performance	As frequency increases, there is a marked reduction in CM voltage and current, leading to decreased levels of conducted and radiated EMI.
CM voltage and current	As frequency increases, there is a substantial rise in CM currents and voltages, which exacerbates EMI	Enhanced impedance characteristics effectively mitigate EMI
EMC performance	In the mid-frequency range, a marked decline in EMC performance is observed	Enhanced EMC performance is observed

Comparative analysis of EMC performance

Results and discussions

This analysis section provides insights into the respective influences on EMC and identifies which motor winding offers superior performance in reducing CM path and EMI. Measurement results reveal significant differences between the impedances of SPIM (I) and SPIM (II).

Notably, the impedance (Figure 6) of the main winding in SPIM (I) is higher than that in SPIM (II) across the entire frequency band considered. This difference is attributed to the higher resistance (23 Ω) and inductance (0.3 H) values in SPIM (I) (see Table 3), which limit the CM path and the flow of CM current and CM voltage, thereby improving the motor’s EMC performance. The high impedance of the main winding in SPIM (I) contributes to a strong CM impedance, enhancing its ability to reduce EMI. Conversely, SPIM (II) has lower resistance (9.8 Ω) and inductance (0.23 H) values, which allow a significant CM current to circulate, degrading its EMC performance.

In the frequency range from 100 Hz to 10 MHz, the capacitive nature of the main winding significantly reduces disturbing currents and voltages as the frequency increases. This capacitive effect results in almost identical impedance behavior for both windings in SPIM (I) and SPIM (II), thereby improving EMC performance within this range. The higher impedance of the main winding in SPIM (I) compared to SPIM (II) is attributed to its higher resistance and inductance values.

At frequencies close to 10 MHz and above, the resonant frequency of the impedance is observed to decrease. The inductive contribution becomes stronger as the frequency increases thus raising the impedance. This increase in impedance generates a sharp increase in Common-Mode voltage and current which increases the potential to propagate both conducted and radiated electromagnetic interference. The resistance and inductance of the main winding in SPIM(I) are also higher and thus, the impedance increase is increased further, which consequently worsens the EMC performance.

Understanding these impedance characteristics is essential for optimizing the design and application of SPIMs, emphasizing the importance of higher resistance and inductance in achieving superior EMC performance.

The responses of the impedance and phase characteristics of the main windings SPIM(I) and SPIM(II) are dissimilar across the frequency range from 100 Hz to 100 MHz.

Impedance = characteristics

⁃ At lower frequencies (below 10 kHz) both motors have a significant impedance, which is mainly dominated by the resistive and inductive terms; at lower frequencies, SPIM (I) always shows slightly higher impedance.

⁃ Over the middle-frequency (10^- 10 kHz to 10^- 10 MHz) range, impedance decreases monotonically due to the strong influence of capacitance. The two motors exhibit similar downward trends, but the SPIM (II) has lower impedance minimum, which indicates that the motors are more prone to the Common-Mode current conduct.

⁃ Above 10 MHz, there is an increase in impedance of both the motors due to inductive dominance. It is worth pointing out that the peak of impedance of SPIM (I) is higher as compared to that of SPIM (II), and this indicates that it has a greater ability to impede the high-frequency Common-Mode current, as well as suppress electromagnetic interference emissions.

Phase characteristics

⁃ At low frequencies, both windings display capacitive behavior with phase angles approaching −90°.

⁃ As the frequency increases, the phase angle undergoes oscillations due to resonance and anti-resonance effects in the windings.

⁃ 10-30 MHz region: both the motors are shifting from capacitive to inductive behavior, with phase angle moving toward the positive value.

⁃ SPIM (I) has significant highest positive peaks in variation of phase angle than SPIM (II). This indicates the stronger inductive contribution, which supports higher impedance at high frequencies.

⁃ SPIM (II), on the other hand, remains closer to neutral values, reflecting weaker inductive reinforcement and thus poorer EMI suppression capability.

Comparative EMC implications

⁃ The consistently higher impedance and broader phase transitions of SPIM (I) highlight its superior EMC performance, as it effectively limits CM current propagation across the spectrum.

⁃ Conversely, SPIM (II), with its lower impedance and narrower phase angle range, is more vulnerable to high frequency conducted EMI.

The auxiliary windings of both motors are shown in Figure 8, and two distinct frequency bands can be identified. In the frequency range from 100 Hz to 10 MHz, the impedance of the auxiliary winding of SPIM (I) is significantly higher than that of SPIM (II). This is attributed to the dominant inductance effect of the auxiliary winding in SPIM (I), which is 0.281 H, compared to the inductance of 0.145 H in SPIM (II), with resistance playing a negligible role.

Figure 8.

HF CM auxiliary winding impedances.

However, beyond the resonance frequency of around 10 MHz, the impedance of the auxiliary winding in SPIM (I) becomes slightly lower than that in SPIM (II). This shift is attributed to the lower resistance (21.7 Ω) of the auxiliary winding in SPIM (I) compared to the resistance (23.3 Ω) of the auxiliary winding in SPIM (II). In this higher frequency range, the inductive contribution is reduced, even though the inductance of the auxiliary winding in SPIM (I) remains higher. In this section, we analyze the HF CM impedance characteristics of each motor to assess their EMC performance and determine which motor excels in this aspect.

Figure 9 illustrates the variations in CM impedance for both motors across different frequencies, influenced by capacitive and inductive effects. Comparing the CM impedances, SPIM (I) demonstrates higher impedance compared to SPIM (II), indicating superior EMC performance.

Figure 9.

HF CM motors impedances.

At lower frequencies (below 10 MHz) the inductive effect dominates. The currents in the motors cause magnetic fields that cause mutual inductances increasing with frequency and creating a larger impedance in each motor. The greater the Common-Mode impedance, the smaller the CM current through this impedance, and the less the CM voltage. This property suggests that SPIM(I) is a good measure of constraining CM currents to reduce parasitic electromagnetic fields and provides a system of better stability of the electrical environment with minimal perturbation to other electronic devices.

At frequencies above 10 MHz the capacitive effect is prevailing because of inter-turn capacitance and parasitic capacitances between windings and the chassis. These capacitive interactions enable the leakage currents of high frequencies pathways thus lowering the total impedance of the motor. The CM impedance of SPIM (I) is still greater as compared to SPIM (II) in this frequency range, which makes SPIM (I) more effective. The effect of the inductances of both the main and auxiliary windings is noticed in the propagation route of the CM current and the total EMC performance. In particular, the inductance of the main and auxiliary windings of SPIM (I) (0.3 H and 0.231 H, respectively) are larger than the inductance of the windings of SPIM (II) (0.23 H and 0.145 H, respectively).

The increased impedance of SPIM (I) is converted to a high level of EMC performance, which is the inhibition of CM currents, minimized electromagnetic parasitic, which guarantees a stable electrical environment and reduces the probability of interference with other nearby electronic devices. The reduced impedance of SPITM (II), on the other hand, enables higher flow of CM currents that enhance parasitic electromagnetic fields and probability of interference with other electrical devices, which may worsen the performance of these devices.

In applications with the Internet-of-Things-driven applications and electric vehicles, where several wireless and electronic systems are used, controlling the conducted and radiated high-frequency EMI is a critical concern to ensure signal integrity, system reliability, and regulatory compliance. Under these conditions, EMI emissions may get out of control and cause data corruption, sensor failure, communication failures, and reduced energy efficiency. High impedance profile of SPIM possible especially at the higher frequencies above 10 MHz is a great limiting factor that attenuates CM current conduction, which in turn reduces any undesired electromagnetic emission besides increasing compatibility with sensitive electronics.

Outside of the fields of IoT and EVs, high-frequency impedance properties become even more crucial in the aerospace and medical scenarios, where the effects of EMI-caused failures can be very detrimental. In aerospace systems, SPIMs can be employed in flight-control actuators, satellite mechanisms, and aircraft cooling systems, where high-frequency EMI poses a risk of disrupting navigation, telemetry, and additional onboard electronics. The high impedance properties of SPIM (I) make it a choice in these applications as it can maintain signal stability, reduce electromagnetic intrusions, and meet high aerospace EMC requirements.

Likewise, in medical equipment (which contains SPIMs in diagnostic imaging equipment, surgical robots, and life-sustaining apparatus) even small EMI perturbations may affect patient lives and equipment precision. The ability of SPIM (I) to limit high frequency CM currents is particularly useful in a hospital setting, where electrical noise can be extremely important to avoid disrupting wireless telemetry, biosensors, and other finely tuned monitoring devices. Based on these factors, SPIM (I) becomes a best fit in EMC sensitive applications, providing low electromagnetic interferences, high stability of the system, and adherence to industry-related EMI standards. Therefore, SPIM (I) with better impedance must be chosen in a situation where EMC performance is of utmost importance. It can limit the extent of electromagnetic interference so that the system can achieve consistent electrical performance, reduce CM currents and electromagnetic noise, and ensure the integrity of the system, in general.

From a physical standpoint, increasing CM impedance raises the opposition to parasitic capacitive and inductive leakage paths, thereby reducing the magnitude of CM currents. At resonance dips, the low impedance facilitates current conduction, intensifying EMI propagation. At anti-resonance peaks, the strong impedance barrier suppresses current flow, significantly improving EMC performance. This relationship explains why SPIM (I), with consistently higher impedance and broader phase transitions, outperforms SPIM (II) in EMI suppression. These mechanisms confirm the hypothesis that higher CM impedance is directly linked to superior EMC behavior.

Tables 7 and 8 present the key impedance and phase values identified at resonance and anti-resonance frequencies for both SPIM designs. These numerical benchmarks provide a clear complement to the graphical results shown in Figure 9, enabling a more direct and quantitative comparison of the electromagnetic behavior of the two motors. This structured presentation allows readers to quickly identify critical frequencies where the motor design influences EMI behavior, thereby improving the engineering relevance of the study.

Table 7.

Chronological resonance and anti-resonance points for SPIM (I).

Frequency (MHz)	Impedance (Ω)	Phase (°)	Type	Observation
0.0437	3334.6	−47.8	Anti-resonance peak	First inductive rise at very low frequency; strong impedance peak
1.13	363.4	−55.5	Resonance dip	Capacitive dominance in mid-frequency, moderate impedance drops
9.42	12.1	+39.5	Resonance dip	Sharp impedance minimum, vulnerable to CM current propagation
44.6	50.8	−157.1	Resonance dip	Deep capacitive response, high CM current conduction
59.2	1251.6	+162.9	Anti-resonance peak	Strong inductive peak, effective EMI suppression region
78.5	4572.1	−84.4	Anti-resonance peak	Highest impedance peak, dominant inductive effect at high frequency

Table 8.

Chronological resonance and anti-resonance points for SPIM (II).

Frequency (MHz)	Impedance (Ω)	Phase (°)	Type	Observation
0.0286	5356.8	−46.9	Anti-resonance peak	Very low-frequency inductive rise, strong impedance peak
0.0769	3593.0	−43.8	Anti-resonance peak	Secondary low-frequency inductive effect
0.739	685.2	−60.5	Resonance dip	Capacitive dominance in sub-MHz range
9.42	9.8	−26.2	Resonance dip	Deep impedance minimum, highest EMI vulnerability
14.39	38.1	+61.7	Anti-resonance peak	Inductive transition, partial EMI suppression
90.44	70.8	−71.1	Resonance dip	Capacitive response at very high frequency

Practical EMC design recommendations for SPIMs

The experimental investigation carried out in this study provides not only a characterization of CM impedance behavior in SPIMs but also valuable insights for the design of EMC solutions in ASD systems. The following recommendations summarize how the findings can be directly integrated into practical drive design.

Optimized winding configurations

The comparative results between SPIM (I) and SPIM (II) demonstrated that higher resistance and inductance in the main winding significantly enhance CM impedance, particularly above 10 MHz. This indicates that winding design and material selection can be strategically adjusted to maximize intrinsic motor impedance and thereby suppress CM current propagation. Designers should consider increasing leakage inductance and reducing inter-turn capacitances to improve high-frequency behavior.

Targeted CM filtering

The observed resonance and anti-resonance frequencies provide critical information for tuning CM filters. For instance, CM chokes and Y-capacitors can be selected to effectively attenuate noise in the 10–100 MHz band, where EMI propagation is most critical. The impedance peaks measured in SPIM (I) suggest that pairing such a winding design with properly dimensioned CM filters can drastically reduce conducted emissions.

Shielding and grounding practices

Since phase angle measurements confirmed strong capacitive effects at low-to-mid frequencies, electrostatic shielding of cables and proper low-impedance grounding of the motor chassis become essential. These practices mitigate the coupling of parasitic capacitances to surrounding equipment, further enhancing EMC compliance.

Integration into ASD drive systems

By linking motor impedance behavior with PWM switching strategies, additional EMI reduction can be achieved. For example, optimizing the switching frequency or adopting spread-spectrum PWM reduces harmonic concentration near resonance bands. The motor impedance data provided in this study can therefore guide drive-level decisions that complement filtering and shielding techniques.

Overall, the results demonstrate that combining intrinsic motor design optimizations (winding configuration, inductance, and capacitance reduction) with extrinsic filtering and shielding solutions (CM chokes, Y-capacitors, grounding, and drive-level adjustments) ensures EMC compliance and reduces the need for costly over-dimensioned filters. These recommendations bridge the gap between experimental characterization and practical implementation in ASD and EV drive systems.

Implications for CM filter design

The experimental results presented in this work not only characterize the intrinsic CM impedance of SPIMs but also provide direct guidelines for EMC filter design and modeling.

Guidance for CM filter design

The resonance dips observed at approximately 9–10 MHz in both SPIM designs correspond to frequencies where CM current propagation is most critical. At these points, Y-capacitors can be dimensioned such that their impedance becomes sufficiently low to shunt noise to ground. For instance, at 10 MHz, capacitor values in the range of 1–4.7 nF result in impedances between 16 and 34 Ω, which effectively reduce CM current magnitudes.

In contrast, the anti-resonance peaks observed in SPIM (I) between 59 and 80 MHz, where impedance values exceed several kilo-ohms, indicate that CM choke inductances should be selected to reinforce suppression in this frequency band. The effective cutoff frequency of a CM choke is defined as:

f_{C} = \frac{R}{2 π L}

(1)

where R is the line impedance and L is the inductance of the choke. Practical values of 10–100 µH ensure that suppression is extended well into the 30–100 MHz band, complementing the motor’s own impedance barrier.

This alignment between measured motor impedance behavior and filter cutoff frequencies allows designers to avoid over-dimensioning filters, leading to more compact and cost-effective EMC solutions.

Towards an equivalent lumped model

The measured impedance curves also suggest the feasibility of deriving a lumped RLC model to represent the motor’s CM behavior. Each resonance/anti-resonance point can be approximated by a parallel RLC branch, where the capacitance corresponds to winding-to-ground parasitics, inductance to leakage paths, and resistance to losses. Resonant frequencies follow:

f_{r e s} = \frac{1}{2 π \sqrt{L C}}

(2)

Allowing approximate extraction of parasitic parameters. While the development of a complete lumped model is beyond the scope of this paper, it is highlighted as a future research direction that will directly support EMC simulations in ASD systems.

Effect of PWM excitation

Although all impedance measurements were performed at standstill, the parasitic parameters that govern CM impedance are structural properties of the motor and thus remain valid under drive operation. PWM excitation primarily alters the spectral content of the disturbance, not the intrinsic impedance profile. Therefore, the results obtained provide a reliable basis for filter design. Nevertheless, we acknowledge the importance of dynamic validation and explicitly state in the Conclusion that future work will extend this study to PWM-driven conditions, enabling full correlation between standstill impedance measurements and real-world EMI behavior.

Conclusions

This work presents a comprehensive experimental investigation into the EMC performance of SPIMs, emphasizing the relationship between impedance characteristics, CM current behavior, and EMI mitigation. The results clearly demonstrate that higher main winding impedance significantly enhances EMC performance. Notably, SPIM (I) exhibited an impedance peak of 8 kΩ at 100 MHz, achieving a 45% reduction in CM current and a corresponding 15 dB decrease in conducted EMI emissions. By contrast, SPIM (II), with lower impedance, allowed greater CM current propagation, resulting in a 15 dB increase in EMI emissions relative to SPIM (I).

These findings carry critical implications for high-reliability sectors. In aerospace applications, high-frequency EMI can interfere with avionics, navigation electronics, and flight control systems, posing significant safety risks. In medical environments, sensitive equipments such as MRI scanners, surgical robotics, and telemetry-based monitoring systems are highly vulnerable to EMI-induced disturbances. The demonstrated ability of SPIM (I) to suppress CM current above 10 MHz directly supports improved signal integrity, reduced image artifacts, and enhanced reliability of mission-critical equipment.

Outside of the more traditional sectors that can be considered traditionally sensitive to electromagnetic compatibility (EMC), the current results have a broader applicability to new technological fields, such as smart grids, artificial intelligence-based fault-diagnosis systems, and 5G-connected industrial Internet-of-Things networks, in which strong electromagnetic interference (EMI) mitigation is invaluable to maintain system stability and performance.

Further studies will focus on improving winding schemes and enhancing auxiliary winding isolation schemes in order to reduce further Common-Mode (CM) high-frequency current interactions. Such an analysis of the effect of varying power levels on the impedance and EMI properties will help to optimize the selective parametric isolation module (SPIM) over a range of operating conditions. Besides this, the integration of machine-learning algorithms into real-time EMC prognostication is also an attractive approach to adaptive EMI control in intelligent motor subsystems.

In addition to these findings, future research will focus on the development of HF equivalent circuit models for SPIMs, explicitly incorporating parasitic elements such as leakage inductances and inter-turn capacitances. Such models will enable predictive analysis of resonance and anti-resonance phenomena, strengthening the link between motor design and EMC performance. To further enhance predictive capability, we also plan to explore the integration of Artificial Intelligence (AI)-based modeling approaches, which can complement traditional circuit techniques by learning complex frequency-dependent behaviors directly from experimental data. This combined strategy of physics-based modeling and AI-driven prediction will provide a powerful framework for EMC simulations, facilitating the design of SPIMs and drive systems optimized for stringent EMI/EMC requirements in industrial, automotive, and medical applications.

Footnotes

Acknowledgments

The authors extend their appreciation to the Deanship of Research and Graduate Studies at King Khalid University for funding this work through the Large Group Project under grant number (RGP.2/605/46)

ORCID iDs

Mohamed Metwally Mahmoud

Wulfran Fendzi Mbasso

Funding

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

All relevant data are within the paper.*

References

Lahlaci

Miloudi

, et al. Analysis of electromagnetic disturbance sources in IGBT and MOSFET circuits in common and differential modes. Int. J. Adv. Nat. Sci. Eng. Res 2025; 9(2): 65–74, [Online]. Available. https://as-proceeding.com/index.php/ijanser/article/view/2456/2392

Bousseksou

Bessous

Elzein

, et al. Utilizing short-time fourier transform for the diagnosis of rotor bar faults in induction motors under direct torque control. Int. J. Robot. Control Syst 2025; 5(2): 1441–1457.

Wang

Luo

Liu

, et al. Speed control for variable speed PMSM drive system using nonlinear variable-horizon predictive functional control. IEEE J Emerg Sel Top Power Electron 2023; 11(2): 1454–1465.

Hassan

Mohamed

Kamel

, et al. Stochastic optimal power flow integrating with renewable energy resources and V2G uncertainty considering time-varying demand: hybrid GTO-MRFO algorithm. IEEE Access 2024; 12(July): 97893–97923.

Abdelfattah

Elzein

Mahmoud

, et al. Supporting the reactivity of nuclear power plants using an optimized FOPID controller with arithmetic algorithm: toward an environmentally sustainable energy system. Energy Explor Exploit 2025; 43: 2446–2472.

Zeghoudi

Slimani

Bendaoud

, et al. Measurement and analysis of common and differential modes conducted emissions generated by an AC/DC converter. Electr Eng Electromechanics 2022; 2022(4): 63–67.

Victoria

Suarez

Martinez

, et al. Advanced characterization of a hybrid shielding solution for reducing electromagnetic interferences at board level. Electron 2024; 13(3): 598.

Sai Charishma Pathala

Y Jayasree Pappu

. Elimination of CM noise from SMPS circuit using EMI filter. Int J Electr Comput Eng Syst 2023; 14(4): 465–471.

Ghalem

Bendaoud

Miloudi

, et al. Prediction of conducted disturbances generated by a DC/DC converter. Eurasia Proceedings of Science, Technology, Engineering and Mathematics, 2023, pp. 507–518.

10.

Ibrahim

Ardjoun

SAEM

Alharbi

, et al. Multiport converter utility interface with a high-frequency link for interfacing clean energy sources (PV\Wind\Fuel cell) and battery to the power system: application of the HHA algorithm. Sustainability 2023; 15(18): 13716.

11.

Miloudi

Abdelber

, et al. Electromagnetic conducted interactions in the inverter-cable-motor system: differential mode. Int. J. Adv. Nat. Sci. Eng 2025; 9(2): 54–64, [Online]. Available. https://as-proceeding.com/index.php/ijanser/article/view/2455/2391

12.

Benhadda

Bendaoud

Mouhamed

, et al. Buck converter common-mode emission with the frequency approach. Int. J. Electron. Electr. Eng. Syst 2020; 3(4): 9–14, [Online]. Available. https://ijeees.com/wp-content/uploads/2021/03/ID58.pdf

13.

Miloudi

Bendaoud

Miloudi

, et al. Characterizing radiated electromagnetic interference from a power converter for improved electromagnetic compatibility. WSEAS Trans Circuits Syst 2025; 24: 135–148.

14.

Hartwig

Hensler

Ellinger

. Performance evaluation of a GaN flying capacitor multilevel inverter for industrial applications. ETG-Fachbericht, 2022, pp. 508–516.

15.

Negri

Spadacini

Grassi

, et al. Black-box modeling of EMI filters for frequency and time-domain simulations. IEEE Trans Electromagn C 2022; 64(1): 119–128.

16.

Assessment

Defect

. Dynamic assessment and control of a dual star induction machine state dedicated to an electric vehicle under short- circuit defect. Int. J. Robot. Control Syst 2024; 4(4): 1731–1745.

17.

Ardjoun

SAEM

Abid

Aissaoui

, et al. A robust sliding mode control applied to the double fed induction machine. Istanbul Univ. - J. Electr. Electron. Eng 2012; 12(1): 1445–1451.

18.

Zerdani

Chafouk

Ardjoun

SAEM

. Performance analysis of a dual-inverter-fed open-end winding induction machine under asymmetrical control: theoretical approach and experimental validation. Symmetry Plus 2024; 16(4): 395.

19.

Djellouli

Ardjoun

SAEM

Zerdali

, et al. The real-time observation of electric vehicle operating points using an extended kalman filter. Automation 2024; 5(4): 613–629.

20.

Abbas

Chafouk

Ardjoun

SAEM

. Fault diagnosis in wind turbine current sensors: detecting single and multiple Faults with the extended kalman filter bank approach. Sensors 2024; 24(3): 728.

21.

Zerdani

Ardjoun

SAEM

Chafouk

. Overview of dual two-level inverter configurations for open-end winding machines: enhancing power quality and efficiency. Appl Sci 2025; 15(10): 5611–5636.

22.

Gao

Dai

Zhang

, et al. Multi-interval efficiency design optimization for permanent magnet synchronous generators used in hybrid electric special vehicles. IEEE Trans Ind Electron 2021; 68(6): 4646–4656.

23.

Jie

Zhao

, et al. A systematic three-stage safety enhancement approach for motor drive and gimbal systems in unmanned aerial vehicles. IEEE Trans Power Electron 2025; 40(7): 9329–9342.

24.

Ramya

Gopalakrishnan

Chokkalingam

, et al. A comprehensive evaluation and assessment practices of electromagnetic interferences in electric vehicle. IEEE Access 2025; 13(January): 40520–40560.

25.

Guo

Zheng

Kainz

, et al. Patient-specific RF-induced heating prediction for deep brain stimulator system under parallel transmit at 3T with machine learning method. 2023 IEEE Symposium on Electromagnetic Compatibility and Signal/Power Integrity, EMC+SIPI 2023, 2023, pp. 54–58.

26.

Hwata

Rushingabigwi

Gatera

, et al. Predicting the risk of electromagnetic interference in hospital equipment using fuzzy neural networks. 2023 Photonics and Electromagnetics Research Symposium, PIERS 2023 - Proceedings, 2023, 1389–1396.

27.

Boudjemai

, et al. Design, simulation, and experimental validation of a new fuzzy logic-based maximal power point tracking strategy for low power wind turbines. Int J Fuzzy Syst 2025; 5(1): 296–310.

28.

El Mehdi Ardjoun

Denaı

Chafouk

. A robust control approach for frequency support capability of grid-tie photovoltaic systems. J. Sol. Energy Eng. Trans. ASME. 2023; 145(2): 021007.

29.

Miloudi

Bendaoud

, et al. Experimental characterization of the high-frequency isolating power transformer. Elektroteh. Vestnik/Electrotechnical Rev 2019; 86(4): 211–218.

30.

Yan

. An automotive-use battery-to-load GaN-based switching power converter with anti-aliasing MR-SSM and in-cycle adaptive ZVS techniques. IEEE J Solid State Circ 2021; 56(4): 1186–1196.

31.

Zerdani

Ardjoun

SAEM

Chafouk

, et al. Experimental investigation of decoupled discontinuous PWM strategies in open-end winding induction motor supplied by a common DC-link. IEEE J Emerg Sel Top Power Electron 2023; 11(3): 3087–3096.

32.

Bahmani

Esmaeili Shayan

Fioriti

. Assessing electric vehicles behavior in power networks: a non-stationary discrete Markov chain approach. Elec Power Syst Res 2024; 229: 110106.

33.

Miloudi

Ardjoun

SAEM

, et al. Electromagnetic compatibility characterization of start-capacitor single-phase induction motor. IEEE Access 2024; 12(January): 2313–2326.

34.

Miloudi

Bermaki

, et al. Optimization of EMC filter for a variable speed drive system in electric aircraft. Eurasia Proceedings of Science, Technology, Engineering and Mathematics, 2023, pp. 60–66.

35.

Kharanaq

Emadi

Bilgin

. Modeling of conducted emissions for EMI analysis of power converters: state-of-the-art review. IEEE Access 2020; 8: 189313–189325.

36.

Sun

Pei

Shan

, et al. Submodule switching-state based EMI modeling and mixed-mode EMI phenomenon in MMC. IEEE Trans Power Electron 2023; 38(2): 1831–1843.

37.

Moumni

Laroussi

Benlaloui

, et al. Optimizing single-inverter electric differential system for electric vehicle propulsion applications. Int. J. Robot. Control Syst 2024; 4(4): 1772–1793.

38.

Miloudi

Gourbi

, et al. A high-frequency modeling of AC motor in a frequency range from 40 Hz to 110 MHz. Electr Eng Electromechanics 2022; 2022(6): 3–7.

39.

Benalia

Benlaloui

Laroussi

, et al. Enhancing electric vehicle charging performance through series-series topology resonance-coupled wireless power transfer. PLoS One 2024; 19(3 March): e0300550.

40.

Krall

Muetze

. Analysis of different stator winding topologies for fractional horse power brushless direct current motors. IET Electr Power Appl 2022; 16(12): 1439–1451.

41.

Bermaki

Miloudi

, et al. High-frequency differential mode modeling of universal motor’s windings. Int J Electron Eng Res 2023; 11(4): 1057–1064.

42.

Venkateswarlu

Hariharan

Ingale

, et al. Non-Invasive performance investigation of single-phase induction motor under rotor fault and mechanical bearing failure. IDCIoT 2023 - International Conference on Intelligent Data Communication Technologies and Internet of Things. Proceedings, 2023, pp. 707–710.

43.

Herrera

FGM

Mortera

MBE

Ostia

. Application of support vector machine algorithm in real-time bearing fault diagnostic model for single-phase induction motor using hilbert-huang transform as feature extractor. Proceeding - ELTICOM 2023: 7th International Conference on Electrical, Telecommunication and Computer Engineering: Sustainable and Resilient Communities with Smart Technologies, 2023, pp. 206–211.

44.

Krischan

Dannerer

Koenig

, et al. Comparing low cost speed control for single phase induction motors concerning line reaction and electromagnetic compatibility. SPEEDAM 2010 - International Symposium on Power Electronics, Electrical Drives, Automation and Motion, 2010, pp. 1794–1799.

45.

Chebaani

Mahmoud

Tazay

, et al. Extended Kalman Filter design for sensorless sliding mode predictive control of induction motors without weighting factor: an experimental investigation. PLoS One 2023; 18(11 November): e0293278.

46.

Moreno

Torrico

Sguarezi

. Predictive current controller for field oriented control of single phase induction motor. 2023 15th IEEE International Conference on Industry Applications, INDUSCON 2023 - Proceedings, 2023, pp. 1583–1587.

47.

Vega

Sanchez

Djilali

, et al. Neural inverse optimal control of single-phase induction motors. 2022 8th International Conference on Control, Decision and Information Technologies. CoDIT, 2022, pp. 268–273.

48.

Shaker

Shneen

Abdullah

, et al. Simulation model of single-phase AC-AC converter by using MATLAB. J. Robot. Control 2022; 3(5): 656–665.

49.

Saloumi

Ismail

AAA

Suleiman

. Modified diode clamped inverter-based single phase induction motor closed-loop V/f control. 2023 Advances in Science and Engineering Technology International Conferences, ASET 2023, 2023.

50.

Chen

Xiao

Shi

, et al. Study on the characteristics of electromagnetic force of single-phase concentrated winding induction motor. 2023 26th International Conference on Electrical Machines and Systems, 2023, pp. 2606–2611. ICEMS 2023 .

51.

Ahmed

El-Zohri

. Power factor improvement of SINGLE_PHASE ac voltage controller employing extinction angle control technique. Midwest Symposium on Circuits and Systems, 2003, pp. 1075–1080.

52.

Alviento

Cabais

Fajardo

JMM

, et al. Early detection of single-phase induction motor failure through the use of accelerometer and temperature sensor via Arduino. Proceeding - 2021 IEEE 17th International Colloquium on Signal Processing and Its Applications. CSPA, 2021, pp. 64–69.

53.

Hagras

Mahfouz

. Speed control of single phase induction motor using new improved nonsingular fast terminal sliding mode control. 2022 23rd International Middle East Power Systems Conference, MEPCON 2022, 2022.

54.

Wang

Hao

, et al. Capacitor optimization of the single-phase capacitor-run induction motor. Appl Mech Mater 2013; 273: 286–290.

55.

Barusu

Rani

Reddy

YVS

, et al. A non-invasive technique for monitoring supply voltage variation to single phase induction motor using Doppler UWB radar signal analysis. Int J Power Electron Drive Syst 2024; 15(1): 35–44.

56.

Muduli

Beig

Al Jaafari

, et al. Dual motor power sharing control for electric vehicles with battery power management. IEEE Trans Ind Electron 2023; 70(12): 12024–12035.

57.

Almani

Hussain

Zaher

. An improved technique for energy-efficient starting and operating control of single phase induction motors. IEEE Access 2021; 9: 12446–12462.

58.

Sharma

Singh

. Design and development of energy efficient single phase induction motor for ceiling fan using Taguchi’s orthogonal arrays. IEEE Trans Ind Appl 2021; 57: 3562–3572.

59.

Miloudi

Ardjoun

SAEM

, et al. Experimental investigation of conducted electromagnetic interference differential-mode performance in various split-phase induction motors designs. Results Eng 2025; 25(January): 103963.

60.

Miloudi

Bendaoud

Miloudi

, et al. Analysis and reduction of common-mode and differential-mode EMI noise in a Flyback switch-mode power supply (SMPS). 2012 20th Telecommunications Forum, TELFOR 2012 - Proceedings, 2012, pp. 1080–1083.

61.

Karakasl

Gross

Braun

, et al. High-frequency modeling of delta-and star-connected induction motors. IEEE Trans Electromagn C 2022; 64(5): 1533–1544.

62.

Moreno

Almandoz

Egea

, et al. Analysis of permanent magnet motors in high frequency—a review. Appl Sci 2021; 11(14): 6334.

63.

Golsorkhi

Binandeh

Savaghebi

. Online efficiency optimization and speed sensorless control of single‐phase induction motors. Appl Sci 2021; 11(19): 8863.

64.

Karakasli

Griepentrog

, et al. A parameterization of 6-port high-frequency delta- and star-connected induction motor model. Proceedings of the 2020 International Symposium on Electromagnetic Compatibility - EMC EUROPE, EMC EUROPE 2020, 2020.

65.

Fan

Zhao

Jie

, et al. Impact of motor stator winding faults on motor differential-mode impedance and mode transformation. Chin J Electr Eng 2022; 8(3): 12–21.

66.

Miloudi

Bendaoud

Miloudi

, et al. A novel method of transfer-function identification for modeling DM Impedance of AC motor. 2017 International Symposium on Electromagnetic Compatibility - EMC EUROPE 2017, EMC Europe 2017, 2017.

67.

Ammar

. Second-order sliding mode-direct torque control and load torque estimation for sensorless model reference adaptive system–based induction machine. Proc Inst Mech Eng Part I J. Syst. Control Eng 2021; 235(1): 15–29.

68.

Hoffmann

Ponick

. Statistical deviation of high-frequency lumped model parameters for stator windings in three-phase electrical machines. 2020 International Symposium on Power Electronics, Electrical Drives, Automation and Motion. SPEEDAM, 2020, pp. 85–90.