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Abstract

Undesirable power quality issues, such as harmonics, can lead to overheating of transformers, malfunctions of sensitive loads, increased power losses, and a significant reduction in the distribution system's efficiency. Nonlinear devices in the power distribution network cause current and voltage harmonics. Consequently, there is a strong need for mitigating harmonics in power networks, which is addressed worldwide through the wide application of shunt active power filters (SAPFs). The SAPF operates using three control techniques: one for regulating the DC-link voltage, another for generating reference current (RCG), and a third for producing switching pulses. These control loops influence the effectiveness of the SAPF in generating adequate compensatory current. However, the behavior of the SAPFs is directly affected by the voltage across the DC link. In SAPF, the voltage across the DC link is typically high, fixed, and power-dependent. In light-load conditions, the DC-link voltage may become high, increasing switching noise and switching losses. Hence, in this article, as a solution to the above problem, a well-managed solar photovoltaic (PV) system is integrated across the DC-link side of the SAPF, which will maintain the DC-link voltage under any load-changing conditions. Similarly, an adaptive hysteresis current control technique is used to manage the voltage source converter switches, and a modified synchronous reference frame scheme is used for reference current generation. This research addresses the shortcomings of the conventional DC-link voltage control method by presenting an advanced PV-SAPF system. Under various load circumstances, the suggested technique's efficacy is assessed in both ideal and non-ideal grid voltage scenarios. A set of simulation-based investigations are used to compare the proposed control approach with other existing techniques. The offered techniques have yielded superior results in terms of total harmonic distortion.

Keywords

Shunt active power filter modified synchronous reference frame adaptive hysteresis current control fuzzy logic controller

Introduction

Energy is a key component of a healthy economy and is the engine that propels a country's social and economic development. The energy source is reliable, safe, and environmentally beneficial. Since most energy comes from traditional sources that increase pollution, renewable energy sources (RESs) are becoming increasingly common as the world's demand for electricity rises (Ahmadizadeh et al., 2024). Thus, the use of RESs is promoted by the government. Integrating several RESs to create a hybrid energy system (HES) would offer a workable way to address the global power demand and the current electricity scarcity. The most popular RESs are fuel cells (FCs), photovoltaic (PV), and wind turbines (WTs) due to their benefits, which include little environmental impact and great overall efficiency (Belkhier and Oubelaid, 2024). Using power electronics-based interfaces or nonlinear loads causes unwanted severe power quality (PQ) concerns. Any anomalous behaviors on a power system that emerge as voltage or current, and issues with the regular functioning of electronics or electrical devices, are referred to as a PQ issue. Voltage swell, voltage sag, voltage flicker, harmonics, and so on, are a few examples of PQ issues (Singh et al., 2022). Out of these, the presence of harmonics results in the failure of highly sensitive loads, transformer overheating, and drastically decreased power distribution efficacy. Hence, harmonics minimization has become essential. Passive filters have historically been used to adjust for reactive power and harmonics. According to the device performance filters used are heavy and difficult to modify. Shunt active power filters (SAPFs) have been used extensively recently to mitigate harmonics in power systems and are now a common method of achieving higher PQ levels in power systems (Popescu M et al., 2024). At the electric power system's common coupling point, the SAPF introduces a suitable compensating current to reduce harmonics in the source current. However, the functioning of three control loops used for reference current generation (RCG), DC-link voltage ( $V_{d c}$ ) regulation, and pulse width modulation (PWM) signal for the voltage source inverter connected to the SAPF is necessary for it to produce the proper compensating current (Behera et al., 2024). With the three control loops of SAPF, researchers around the globe have proposed numerous approaches for each. The individual proposed strategy has its own merits and limitations. However, there is still a lot of scope to either improve the existing methods or explore novel techniques related to the mentioned control loops.

The power balance of the system is maintained by using a capacitor connected across the DC link of the SAPF (Boopathi and Indragandhi, 2024). When the load is greatly increased, the capacitor discharges and supplies power, therefore enabling the source and load power balance to be attained. Likewise, the capacitor overcharges and maintains the power stability when the load is suddenly removed. Modulating the $V_{d c}$ requires a well-thought-out control approach to complete this procedure (Soliman et al., 2024). The works carried out conclude that if the SAPF $V_{d c}$ remains stable, it will operate effectively and deliver precisely the required compensating current. Therefore, the design of $V_{d c}$ the controller is also an important assignment in realizing SAPF. Usually, $V_{d c}$ SAPF is regulated either by a proportional integral (PI) controller or fuzzy logic controller (FLC), where the control approach is based on the direct voltage error management concept (Patnaik and Panda, 2023). Fei et al. (2024) present a technique for detecting harmonics under changing load conditions that combines the PI controller with the sliding mode controller. Subbarao et al. (2023) describe the demerits of using a conventional PI controller in comparison to FLC for control. The performance characteristic of the PI controller under load-changing circumstances exhibits severe overshoot and a protracted settling time. It also has shortcomings, including incompetence under load-changing circumstances and parametric variation. Conversely, FLC is recognized for its quickness, dependability, and efficiency in harmonic monitoring, $V_{d c}$ control, and reactive power compensation. Therefore, FLC's primary disadvantage is that using more membership functions and rules increases its computational load (Tang and Ahmad, 2024). In addition, it operates under the principle of the direct voltage error manipulation technique, which does not fully eliminate the $V_{d c}$ error that occurs during load change conditions (Suprabhath et al., 2024). Due to the aforementioned disadvantages regarding the reported PI and FLC approaches, in this research work, the dynamic performance of SAPF is enhanced by integrating a solar PV system with the DC-link capacitor of the voltage source converter, aimed at tightening the control over the ripples in the $V_{d c}$ waveform, especially when the load is changed (Boopathi and Indragandhi, 2024). It offers superior performance as compared to of existing approaches based on PI and FLC.

The RCG control loop plays a crucial role in generating the desired compensating current to nullify the harmonics present in the system (Barik et al., 2023). Hence, its proper design is crucial from SAPF's point of view. As reported in the literature, there exist many control approaches for RCG, each of them having its unique merit, and all of these are improved versions of two basic control techniques. Instantaneous active and reactive power theory (also known as p–q theory) (Aguilera et al., 2024) and SRF theory, also known as d–q theory (Rai et al., 2024; Boopathi and Indragandhi, 2024), form the foundation of the fundamental control strategies. Because of its up-front design and ease of use, the SRF approach is more commonly recognized than the other one. The need for a reference phase angle produced by a phase-locked loop (PLL) for synchronization, however, is a significant disadvantage of the aforementioned method. The PLL block's design needs to be carefully implemented when the source voltages are distorted and/or unbalanced (Alturki et al., 2024; Patel et al., 2022). Barik et al. (2024) have presented a modified synchronous reference frame (MSRF)-based PLL approach for RCG. The addressed technique helps to adjust the small power losses occurring due to the additional harmonic current. However, the MSRF strategy uses similar methods to the standard SRF system. The calculation of the theta angle required for synchronization, however, is where the biggest difference lies (Zorig et al., 2024). The main advantage of this approach is that the theta angle can be calculated directly from the supply, which makes it frequency-independent and unaffected by changes in load. Because of its inherent benefits, the authors of this work have also incorporated the widely reported MSRF approach for RCG into the SAPF model that is being studied.

The production of appropriate switching pulses for the SAPFs’ voltage source inverter (VSI) is another crucial element in lowering harmonics. The literature reports on a variety of switching pulse-generating strategies, such as hysteresis current controller (HCC) (Chavali et al., 2024) and adaptive hysteresis current controller (AHCC) (Barik et al., 2024; Mohanty et al., 2024). Researchers by Alhamrouni et al. (2024) and Hamouda et al. (2024) suggested a feedback control technique for PWM pulse production that eliminates harmonics of higher orders and requires less time to stabilize voltage during load change. According to Barik et al. (2024) and Chavali et al. (2024), HCC-based approaches are easier to implement than other ones, although they exhibit uneven switching frequency. As reported by Barik et al. (2023), the regulated switching frequency used in the HCC approach results in increased losses during switching and the introduction of noisy frequencies and harmonics into the electrical system current. The AHCC performance, which employs an adjustable hysteresis band (HB) for tracking system current, is suggested as a solution to the drawbacks of HCC. Mohanty et al. (2024) suggested an AHCC technique for SAPF to reduce the THD of the source current, where AHCC amends the bandwidth (BW) as per the $V_{d c}$ profile, frequency of modulation, reference current, and input voltage. Hence, this AHCC technique is implemented here for pulse generation.

In order to increase grid stability, the PV combined with the SAPF's DC-link capacitor lowers the power converter's rating and improves PQ by offsetting the undesirable elements of unbalanced and nonlinear loads (Hamouda et al., 2024). The majority of researchers nowadays are working on SAPF, using various theories and methods created to locate control signals and address problems with PQ. The FLC and AHCC control strategies for SAPFs are developed in this study. The key techniques PV PV-based SAPF are systematically compared in order to determine the main advantages and disadvantages of to that of normal SAPF.

The key contributions to the research article are given below:

To examine the effectiveness of MSRF-based RCG in aligning with FLC and AHCC for voltage across the DC-link management and switching pulse generation strategies of SAPF, respectively, in terms of improving PQ under different source and nonlinear load conditions.

To develop a novel solar PV-based voltage control across the DC-link capacitor approach for maintaining a steady value of voltage across the DC-link capacitor.

The performance of SAPF is evaluated in combination with MSRF and AHCC approaches in extracting the three-phase reference currents for harmonics mitigation under various source and load conditions in comparison with solar PV-based SAPF.

This document offers an exhaustive, methodical process for implementing a PV-integrated SAPF. This article is formatted as follows: Modeling of SAPF, along with its control techniques, is described in the Design of SAPF section. The PV system is explained in the Solar PV System section with a solar-powered integrated SAPF. The findings and discussion are shown in the Results and discussion section. The conclusion drawn by the study is given in the Conclusion section.

Design of SAPF

An active power filter is used to address issues with voltage and current harmonics. They are also able to handle issues with reactive power compensation and low power factor. Shunt, series, and hybrid active filters are the different categories of active filters. Series active filters are used to compensate for voltage harmonics, SAPF recompense harmonics due to current, and hybrid APFs are used to compensate for both voltage and current harmonics (Hamouda et al., 2024). The SAPF will be the main topic of this study to address issues with current harmonics. A fundamental schematic of a SAPF is shown in Figure 1. As seen, a SAPF functions as a current control device source, injecting a compensating current with a 180° phase shift to the source current (Xie et al., 2023).

Figure 1.

Shunt active power filter (SAPF) structure.

We get the utility voltage from the following equation:

V_{s} (t) = V_{m} \sin ω t

(1)

where the instantaneous value of the source voltage

(V_{s} (t))

and the peak value

(V_{m})

are, respectively.

Equation (2) provides the instantaneous value of source currents at the PCC.

i_{s} (t) = i_{L} (t) - i_{c} (t)

(2)

where the content of the source, load, and compensating currents are, respectively,

i_{s} (t), i_{L} (t), and i_{C} (t) .

Equation (3) represents the combination of the elementary and harmonic components that make up the $i_{L}$ [2].

I_{L o a d} (t) = I_{1} \sin (ω t + φ_{1}) + \sum_{n = 3, 5, 7}^{n} I_{n} \sin (n ω t + φ_{n})

(3)

The power delivered by the load can be computed as per the following equations:

P_{L o a d} (t) = V_{s o u r c e} (t) * I_{L o a d} (t)

(4)

P_{L o a d} (t) = V_{m} I_{1} \sin^{2} ω t \cos φ_{1} + V_{m} I_{1} \sin ω t \cos ω t \sin φ_{1} + V_{m} \sin ω t \sum_{n = 3, 5, 7, \dots}^{n} I_{n} \sin (n ω t + φ_{n})

(5)

P_{L o a d} (t) = A c t i v e p o w e r (P_{a c t i v e} (t)) + R e a c t i v e p o w e r (P_{r e a c t i v e} (t)) + H a r m o n i c p o w e r (P_{h a r m o n i c} (t))

(6)

The source current provided by the SAPF after compensation can be given by the following equation:

I_{s a} * = \frac{P_{f} (t)}{V_{s o u r c e} (t)} = I_{m a x} \sin ω t

(7)

SAPF control scheme

The control circuits involved with the SAPF are designed in such a way that their coordinated actions inject a current to compensate for the harmonics present, thereby enhancing the performance of the system (Patnaik and Panda, 2023). As mentioned earlier, the present work focuses on the performance of SAPF under the various combinations of control methods used for RCG, DC-link voltage control, and switching pulse generation. Hence, firstly, the outdated control tactics for RCG are detailed in this section. Thereafter, a fuzzy and PI controller-based strategy for $V_{d c}$ regulation is described, followed by the AHCC technique for switching pulse generation.

Reference current generation (RCG)

Control mechanisms for producing compensation currents can be implemented in either the frequency or time domain (Amini et al., 2023). Fourier analysis of the current signals is the control method for obtaining compensating current under the frequency domain approach. However, this approach requires complex mathematical computations, which might result in a longer turnaround time. However, the time domain approach is easier to use and has a simpler mathematical foundation. The two commonly utilized, simple, and easy-to-implement fundamental techniques, the SRF method and the MSRF method, are the foundation for the control strategy realization in this work. The following subsections provide a brief discussion of each of these topics.

(a) SRF technique: Real-time SAPF control is achieved using the SRF theory-based control technique, which operates in both steady-state and transient modes (Belkhier and Oubelaid, 2024). As determined, the load's current draw is $i_{L a}, i_{L b}, and i_{L c}$ . To transfer these values into the d–q–0 axis, a sine-based park transformation is employed.

Harmonic reduction, power factor correction, and wattless power compensation are the uses of current components on the d-axis. The signals have been synchronized at a point by using a PLL called a common coupling point (PCC). A PLL is needed to obtain the phase information of periodic and physical control (Zorig et al., 2024). To obtain the DC elements of $i_{d}$ and $i_{q}$ . The d–q components are processed through a low-pass filter. A reference voltage ( $V_{d c, r e f}$ ) and the voltage of the intermediate circuit capacitor (DC link) ( $V_{d c}$ ) are compared. Then the controller receives the error and its change. Figure 2 displays the SRF theory block diagram.

(b) MSRF method: MSRF uses a generating strategy of a unit vector to achieve synchronization, as the SRF methodology uses a PLL to achieve synchronization (Gude et al., 2023). MSRF design can be described by using the schematic diagram in Figure 3. Here, $v_{s a b c}$ is first converted to $V_{s α}$ and $V_{s β}$ in the $α - β$ reference frame. Thereafter, the unit vectors ( $\sin θ$ and $\cos θ$ ) are engendered with the following equations:

\cos θ = \frac{V_{α}}{\sqrt (V_{s α}^{2} + V_{s β}^{2})}

(8)

\sin θ = \frac{V_{β}}{\sqrt (V_{s α}^{2} + V_{s β}^{2})}

(9)

Figure 2.

Synchronous reference frame (SRF) method block diagram.

Figure 3.

Modified synchronous reference frame (MSRF) design.

To extract the d–q axis currents ( $I_{d} - I_{q}$ ) from the load current $I_{L a b c}$ These unit vectors are necessary. Through the SAPF, source power is drawn to regulate $V_{d c}$ . To generate the ultimate value of the supply, current $I_{d c}$ the PI/fuzzy controller receives a deviation between the $V_{d c}$ and its reference value ( $V_{d c, r e f})$ after the $V_{d c}$ has been analyzed. To create reference d–q currents, or $I_{d} *$ and $I_{q} *$ , respectively, this $I_{d c}$ is directly added to the d-axis as well as the q-axis harmonics component of the load currents, $i_{L d (h a r)}, and i_{L q (h a r)}$ . Using reference, $I_{α} *$ and $I_{β} *$ components, the desired $I_{s a} *, I_{s b} *, and I_{s c} *$ are determined.

Control technique for DC-link voltage

Because $V_{d c}$ control maintains the system's power balance and produces the required current to offset switching losses in the VSI, SAPF's performance is dependent on it (Boopathi and Indragandhi, 2024). Consequently, one of the most important issues for SAPF performance optimization is $V_{d c}$ regulation. The DC-side voltage of the SAPF is controlled in this chapter using two well-researched methods: the FLC and the PI controller.

(a) PI-controller: A reference voltage is used to compare the voltage sensed from the DC-side capacitor. The PI controller takes input as an error in voltage $E (n) = V_{d c, r e f} - V_{d c}$ at the n number of sampling moments and calculates the evaluation outcome. The error signal's higher-order components are removed using the low-pass filter (Li et al., 2024), leaving only the fundamentals. The definition of its transfer function is given in the following equation:

H (s) = K_{P} + \frac{K_{i}}{s}

(10)

$K_{P} = 2 € ω n V C$ [K_P = 0.2], which determines the DC-side voltage's uncertain responsiveness, is used to calculate the proportional gain. The natural frequency $ω_{n}$ and damping factor € = √2/2 ought to be selected as the fundamental frequency. Similarly, $K_{i} = C ω n V^{2}$ [K_i = 20] is used to derive the integral gain; it defines the settling period and removes steady-state error from the DC-side capacitor voltage. The block diagram is presented in Figure 4. This controller regulates the DC-side capacitor and estimates the greatest possible reference current $I_{d c}$ . The unit current vector's output multiplies this anticipated peak current magnitude to produce the necessary reference currents to balance the harmonic components (Sanjenbam et al., 2024).

(b) Fuzzy logic controller (FLC): Zadeh first presented FLC in 1965, and operational features can be described using fuzzy logic theory (Hosseini et al., 2024). The transitions between association and non-association functions are developed in fuzzy set theory. As a result, the borders or limitations of fuzzy sets might be ill-defined and unclear, which makes them helpful for designing approximate systems. In a closed loop, the observed capacitor voltage of the DC-bus is compared with the required value of reference voltage to carry out the FLC algorithm for a SAPF (Amini et al., 2023). A low-pass filter processes this calculated error signal $E (n) = V_{d c, r e f} - V_{d c}$ . Fuzzy processes by taking the fault signal $E (n)$ and the change in the fault signal $E_{C} (n)$ as inputs, as shown in Figure 5. In this fuzzy logic system, both the input and output variables are divided into seven fuzzy sets: negative high (NH), negative medium (NM), negative low (NL), zero (ZE), positive low (PL), positive medium (PM), and positive high (PH). Triangular membership functions are used to maintain simplicity. The system applies Mamdani-type inference with a minimum operator for implication, and the height method is employed for defuzzification to produce the final crisp output.

Fuzzification: Fuzzification is the process of using language variables in place of mathematical variables in logic (Liu et al., 2024). The inaccuracy between the reference signal and the output signal in a control system can be classified as PH, PM, NL, ZE, PL, NH, or NM. Fuzzification procedures change genuine numerical variables into fuzzy numbers, which are linguistic variables.

Rule elevator: The control gains of conventional controllers, such as PID and PI, are combinations of numerical values. Linguistic variables are used by the fuzzy logic controller in place of numerical ones. To evaluate fuzzy set rules, three fundamental fuzzy logic operations are needed: AND (∩), OR (∥), and NOT (−). It is defined as AND for intersection, OR for union, and NOT for complement functions, respectively.

Defuzzification: The required output in language variables is generated by the fuzzy logic rules in real-time in response to demands (Han, 2024). It is necessary to convert the language variables to clear numerical values. This strategy's choice strikes a balance between computational effort and precision.

Data and rule base: The triangle membership value definition, which is used by the fuzzifier and defuzzifier, is kept in the database. The language control rules that the rule evaluator (decision-making logic) uses are stored in the rule base. This suggested controller makes use of the 49 rules, as listed in Table 1. The peak reference current $I_{d c}$ is estimated from the fuzzy controller's output. The active power requirement for harmonics and reactive power compensation is taken into account by this current $I_{d c}$ . The unit current vector's output multiplied by the expected peak current magnitude yields the necessary reference currents. Using a PWM-current controller, the inverter switching patterns were produced by comparing these reference currents to the actual currents (Barik et al., 2022).

Figure 4.

Schematic diagram of voltage control across the DC link.

Figure 5.

Fuzzy controller design.

Table 1.

Rule for the FLC method.

	ce(n)
	I _dc
e(n)	NB	NM	NS	ZE	PS	PM	PB
NB	NB	NB	NB	NB	NM	NS	ZE
NM	NB	NB	NB	NM	NS	ZE	PS
NS	NB	NB	NM	NS	ZE	PS	PM
ZE	NB	NM	NS	ZE	PS	PM	PB
PS	NM	NS	ZE	PS	PM	PB	PB
PM	NS	ZE	PS	PM	PB	PB	PB
PB	ZE	PS	PM	PB	PB	PB	PB

FLC: fuzzy logic controller; PH: positive high; PM: positive medium; NL: negative low; ZE: zero; PL: positive low; NH: negative high; NM: negative medium.

Switching pulse generation technique

Numerous pulse generation approaches, including space vector PWM, sinusoidal PWM, sinusoidal PWM with instantaneous current control, HCC-based PWM, selective harmonic elimination-based PWM, and others, are accessible in the literature.

Hysteresis band current control (HCC): It is a controller that makes the computed reference current $(i_{s} *)$ and the compensated grid current $(i_{s})$ follow each other. The accuracy of the hysteresis controller is determined by its hysteresis band (HB), which shows the current disruption (Mohapatra and Dey, 2023). However, higher switching losses in SAPF are caused by a narrower HB in HCC. Figure 6 displays the HCC's model layout.

Adaptive hysteresis band current control (AHCC): The present study proposes AHCC as a solution to the fixed-band HCC's shortcomings (Hamouda et al., 2019). By constructing an instantaneous hysteresis bandwidth based on voltage and current variation for compensation, AHCC builds smooth switching speeds and significantly fixes frequency switching. Equation (11) presents the correlation between the HB and f_s based on the system topology, is shown in Figure 7. The switching pattern in this method is determined by the rising and falling currents within the HB. The minute the current error exceeds the top boundary of HB in this system, the bottom adjustment becomes active. When the incorrect current surpasses HB's lower limit, the higher switch is activated. The definite source current is therefore limited to following the reference current within the HB:

H B = \frac{0.125 V_{d c}}{f_{s} L_{i}} [1 - \frac{4 L_{i}^{2}}{V_{d c}^{2}} (\frac{V_{s}}{L_{i}} + \frac{d i_{s a} *}{d t})]

(11)

Figure 6.

Model layout of the hysteresis current controller (HCC) scheme.

Figure 7.

Schematic diagram of the adaptive hysteresis current controller (AHCC) scheme.

Solar PV system

A system that uses photovoltaic cells generates electricity from solar radiation using one or more solar panels. It is composed of several parts, such as the photovoltaic modules, mountings, and connections (both mechanical and electrical), and devices for controlling and/or adjusting the electrical output (Lucchi, 2024).

PV array

As shown in Figure 8, the nonlinear V–I characteristics of PV cells vary with temperature and solar radiation. For this reason, it is crucial to monitor the array's maximum power output. An MPPT-based algorithm is used by the author to regulate the duty cycle of a step-up converter so that most power can be extracted. Power from the PV array is channeled into a step-up converter. The SAPF's DC bus is where the step-up converter's output is attached. The step-up converter gives an increased output voltage of 700 V, whereas the PV array produces an output voltage of 274.4 V. The diode current ( $I_{D}$ ) and load currents ( $I_{L o a d}$ ) for the PV array are provided in the following equations (Srivastava et al., 2024):

I_{D} = I_{s a t} (e^{\frac{Q V_{o c}}{A k T}} - 1)

(12)

I = I_{L o a d} - I_{s a t} (e^{\frac{Q V_{o c}}{A k T}} - 1) - \frac{V_{o c}}{R_{s h}}

(13)

when the value of current I = 0, the maximum voltage obtained from PV during an open circuit can be

V_{o c} = \frac{A k T}{Q} \log_{n} (\frac{I_{L}}{I_{s a t}} + 1)

(14)

Figure 8.

V–I and P–V characteristics of photovoltaic (PV) cells.

Control of step-up converter to achieve MPPT

An MPPT algorithm is used to operate a step-up converter. Power and voltage variations are the proposed MPPT algorithm's inputs, while duty cycle variation is its output. This algorithm produces an appropriate duty cycle that matches MPP. In this work, the MPPT algorithm is used to track MPP. This technique's primary benefit is that it will seek MPP regardless of the surrounding conditions. Every boost has its own MPPT in charge. A 750 V DC bus output is associated with the output of the step-up converter (Yadav et al., 2024). Figure 9 shows the SPV with a boost converter and MPPT.

Figure 9.

SPV with step-up converter and MPPT.

The step-up converter's construction

The step-up converter is required to employ the MPPT algorithm and supply solar PV electricity to the common DC link. Equation (15) is used to construct the step-up converter inductor.

L_{b} = \frac{V_{P V} (V_{d c} - V_{P V})}{V_{d c} f_{s w} Δ I_{L}}

(15)

The voltage associated with the PV array is $V_{P V}$ , the voltage generated across the intermediate circuit capacitor (DC link) is $V_{d c}$ , the ripple current is $Δ I_{L}$ across the inductor, and the switching frequency is $f_{s w}$ (Sbabo et al., 2024).

Selection of DC-link reference voltage

The minimum voltage $V_{d c}$ of the DC connection bus needs to be greater than or have the same value as the maximum voltage between lines to actively regulate the current of the filter, that is, $I_{c}$ . A step-up converter gives the output voltage as DC after that this voltage is transformed to alternating voltage and associated with the grid. Equation (16), when applied to the input of the voltage source converter, yields the DC link of the SAPF (Szromba, 2024).

V_{d c} = \frac{2 \sqrt 2 V_{L L}}{\sqrt 3 m_{i}}

(16)

where the line voltage is denoted by

V_{L L}

and m_i is the modulation index.

Designing an intermediate circuit capacitor (DC link)

The value of capacitors attached to the DC link, that is, $C_{d c}$ must be chosen such that a recommended value is required to sustain the DC capacitor. There are two main purposes for the intermediate circuit capacitor (DC link) design. It supplies actual power differentials and serves as an energy storage component during transitory times. It keeps the DC voltage constant.

The following formula is used by the capacitor attached to the DC link to calculate the variance in stored energy, as given in the following equation:

Δ E = \frac{1}{2} C_{d c} (V_{d c, r e f}^{2} - V_{d c, min}^{2})

(17)

where

V_{d c \min}

is the minimum DC-link voltage (Wang et al., 2024).

Choosing the inductance of the filter

The compensating current of the SAPF includes the reactive and harmonic parts that make up the load current. Harmonic currents of the load ought to be able to enter the PCC through the inductor. Additionally, it will prevent high switching frequency harmonic currents from entering the PCC as a result of voltage source inverter (VSI) switching, as shown by the relationship below; that value may be computed as follows:

L_{f} = \frac{\frac{\sqrt 3}{2} m V_{d c, r e f}}{6 k f_{s w} Δ i}

(18)

where

f_{s w}

is the switching frequency, the amplitude modulation index is represented by m, and Δi is the VSC current ripple (Janardhanan and Mulla, 2024).

Results and discussion

In Figure 10, the proposed configuration is shown below. The PV module and step-up converter are the system's main components. The performance of SAPF with and without SPV on the DC-link side is examined in this section under various source and load-changing scenarios. A SAPF is in addition to the observed control algorithms (i.e. MSRF methods) for RCG to improve harmonic filtering. In addition to the aforementioned, the current FLC techniques and AHCC scheme are used to generate PWM pulses and regulate them, respectively. The combinations of schemes using the traditional MSRF approach (for RCG) in this study, FLC controller (for control), and AHCC (for PWM signal generation) used for producing compensating current are referred to as MSRF-FLC-AHCC techniques without SPV on the DC-link side. On the other hand, the combination MSRF-FLC-AHCC technique is used with SPV on the DC-link side.

Figure 10.

Proposed SPV-based SAPF.

Under various controller actions and loading situations, the performance studies of the system under study are examined both with and without SAPF and are referred to as distinct scenarios to consider. The results of interest under each scenario are highlighted in bold in the respective table.

Scenario 1: Performance analysis without SAPF and with SAPF employing the MSRF-FLC-AHCC scheme under normal source conditions.

Scenario 2: Performance analysis without SAPF and with SAPF employing MSRF-FLC-AHCC scheme under distorted source conditions.

Scenario 3: Performance analysis with SAPF employing MSRF-FLC-AHCC scheme under distorted source condition, implementing SPV across the DC-link side.

Performance study under Scenario 1

First, with a diode bridge-type nonlinear inductive load, we check the THD of the source current (I_s) without SAPF, assuming a normal source voltage. The source voltage (V_s) waveform, the waveform, and THD under this condition are offered in Figure 11(a) to (c), respectively.

Figure 11.

Profiles attained during RL-load without SAPF normal source: (a) $V_{s}$ , (b) $i_{s}$ , and (c) THD of $i_{s}$ (Scenario 1).

It is observed from Figure 11(c) that the wave shape THD of 26.74% for the RL-load. However, in the case of RC-type nonlinear load as viewed from Figure 12(c), the THD of the waveform is very large at 30.05%. Figure 12(a) and (b) shows the corresponding V_s and I_s waveforms, respectively.

Figure 12.

Profiles attained during RC-load without SAPF normal source: (a) $V_{s}$ , (b) $i_{s}$ , and (c) THD of $i_{s}$ (Scenario 1).

To test the competency of the proposed controller, the SAPF will generate the using MSRF-FLC-AHCC techniques under RL and RC load conditions. This, as given in Figures 13(a) and 14(a), reduces the THD of and improves its waveform quality by injecting it at PCC. The waveform and its compensated THD current are illustrated in Figure 13(b) and (c) for the case of RL-load and Figure 14(b) and (c) for RC-load, respectively. It is observed from Figure 13(c) that the THD value of reduced to 2.35% (Liu et al., 2024; Patel et al., 2022) in the RL-load. Similarly, for the case of RC-load, the THD reduces to 2.15% (Liu et al., 2024; Patel et al., 2022) as presented in Figure 14(c).

Figure 13.

Profiles attained with RL-load with SAPF normal source: (a) $i_{c}$ ,(b) $i_{s}$ , and (c) THD of $i_{s}$ (Scenario 1).

Figure 14.

Profiles attained by RC-load with SAPF normal source: (a) $i_{c}$ , (b) $i_{s}$ , and (c) THD of $i_{s}$ (Scenario 1).

Performance study under Scenario 2

In this instance, the THD has also been checked under distorted source conditions, considering both the RL and RC loads. The distorted source wave shape, wave shape, and THD under this condition are presented in Figure 15(a) to (c) and Figure 16(a) to (c), considering without SAPF for R-L and R-C loads, respectively. It is viewed from Figure 15(c), that the THD of I_s the waveform is very large, 38.15% for R-L load and 40.05% for R-C load, as presented in Figure 16(c). To test the performance of the proposed controller under distorted source conditions, the SAPF will be generated using MSRF-FLC-AHCC techniques under RL- and RC-load. This, as given in Figures 17(a) and 18(a), reduces the THD of and improves its waveform quality by injecting it at PCC. The waveform and its compensated THD current are presented in Figure 17(b) and (c) for the case of RL-load and Figure 18(b) and (c) for RC-load, respectively. It is observed from Figure 17(c) that the THD value reduces to 3.42% (Liu et al., 2024; Patel et al., 2022) in the RL-load. Similarly, for the case of RC-load, the THD reduces to 3.12% (Liu et al., 2024; Patel et al., 2022), as presented in Figure 18(c). It is revealed from the results that under distorted source conditions, the THD mitigation of the SAPF is not up to the mark. Hence, the THD may be further reduced by controlling the DC-link voltage of the SAPF by using an SPV system across the DC-link side of the SAPF.

Figure 15.

Profiles attained by RL-load without SAPF distorted source: (a) $V_{s}$ , (b) $i_{s}$ , and (c) THD of $i_{s}$ (Scenario 2).

Figure 16.

Profiles attained by RC-load without SAPF distorted source: (a) $V_{s}$ , (b) $i_{s}$ , and (c) THD of $i_{s}$ (Scenario 2).

Figure 17.

Profiles attained by RL-load with SAPF distorted source: (a) $i_{c}$ , (b) $i_{s}$ , and (c) THD of $i_{s}$ (Scenario 2).

Figure 18.

Profiles attained by RC-load with SAPF distorted source: (a) $i_{c}$ , (b) $i_{s}$ , and (c) THD of $i_{s}$ (Scenario 2).

Performance study under Scenario 3

In this state, the act of the SAPF has been analyzed by integrating SPV across the DC-link side of the SAPF under distorted source conditions. The performance is evaluated considering two parameters, that is, (1) THD comparison and (2) DC-link voltage regulation comparison.

THD analysis

The profile produced by the MSRF-FLC-AHCC-based scheme is revealed in Figure 19(a) for both R-L and R-C-type loads. Figure 19(b) illustrates the waveform obtained after compensation for each type of load. The THD inquiry reports (with SAPF) for individual loads are accessible in Figure 19(c). The results demonstrate that the signal is virtually sinusoidal, exhibiting low THD values of 1.20% and 1.15% obtained for R-L- and R-C-type nonlinear loads, respectively, in the presence of MSRF-FLC-AHCC-based technique under normal source conditions and THD values of 2.22% and 2.15% obtained for R-L- and R-C-type loads, respectively, under distorted source conditions. Figure 20(a) to (c) represents the corresponding waveforms under distorted source conditions. The THD comparison result is presented in Table 2. It is visible from Table 2 that the proposed SPV-based SAPF with MSRF-FLC-AHCC control algorithm gives the best THD result as compared to others, both under normal and distorted source conditions.

Figure 19.

Profiles attained with SPV-based SAPF considering normal source: (a) $i_{c}$ , (b) $i_{s}$ , and (c) THD of $i_{s}$ (Scenario 3).

Figure 20.

Profiles obtained under SPV-based SAPF considering distorted source (a) $i_{c}$ , (b) $i_{s}$ , and (c) THD of $i_{s}$ (Scenario 3).

Table 2.

THD comparison of $i_{s}$ .

Source type	Load type	THD without SAPF	THD with SAPF	THD with SPV-based SAPF
Normal	R-L-load	26.74%	2.35%	1.20%
Normal	R-C-load	30.05%	2.15%	1.15%
Distorted	R-L-load	38.15%	3.42% (Liu et al., 2024; Patel et al., 2022)	2.22% (Liu et al., 2024; Patel et al., 2022)
Distorted	R-C-load	40.05%	3.12% (Liu et al., 2024; Patel et al., 2022)	2.15% (Liu et al., 2024; Patel et al., 2022)

R-L: resistor-inductor; R-C: resistor-capacitor; THD: total harmonic distortion; SPV: solar photovoltaic; SAPF: shunt active power filter.

$V_{dc}$ regulation

In this case, the act of the anticipated SPV-based SAPF with MSRF-FLC-AHCC control strategy is compared without SPV-based SAPF in regulating $V_{d c}$ profile under normal source and distorted source conditions. To analyze the accuracy, a performance indicator called a percentage of accuracy (%ACC) is used. It is defined in the following equation:

% A C C = (1 - \frac{V_{d c, r e f} - V_{d c}}{V_{d c, r e f}}) \times 100

(19)

It is evident from Figure 21 and Table 3 that under normal source conditions, the maximum deviation of 18 and 20 V in the wave shape is perceived under the MSRF-FLC-AHCC approach without SPV-based SAPF for R-L- and R-C-load types, respectively. Also, the %ACC of 97.50% and 97.20% are found for R-L- and R-C-load types, respectively. However, the proposed SPV-based SAPF with MSRF-FLC-AHCC control topology offers the least deviation of 4 and 3 V with further improvement in %ACC to 99.42% and 99.57% for R-L- and R-C-type nonlinear loads, respectively. Similarly, under distorted source conditions, the maximum deviation of 24 and 28 V in the wave shape is observed under the MSRF-FLC-AHCC approach without SPV-based SAPF for RL- and RC-load types, respectively, as given in Figure 22. Also, the %ACC of 96.54% and 96.00% are found for RL- and RC-load types, respectively. However, the proposed SPV-based SAPF with MSRF-FLC-AHCC control topology offers the least deviation of 6 and 7 V with further improvement in %ACC to 99.15% and 99% for RL- and RC-type nonlinear loads, respectively.

Figure 21.

$V_{d c}$ profiles obtained considering RL- and RC-type loads under normal source conditions: (a) MSRF-FLC-AFHCC scheme without SPV-based SAPF and (b) MSRF-FLC-AFHCC scheme with SPV-based SAPF (Scenario 3).

Figure 22.

$V_{d c}$ profiles obtained considering RL- and RC-type loads under distorted source conditions: (a) MSRF-FLC-AFHCC scheme without SPV-based SAPF and (b) MSRF-FLC-AFHCC scheme with SPV-based SAPF (Scenario 3).

Table 3.

Comparison of $V_{d c}$ deviation.

	Voltage deviation (in V)		(%ACC)
Studied control method	RL-load	RC-load	RL-load	RC-load
Normal source condition
MSRF-FLC-AHCC without SPV-based SAPF	18	20	98.51%	98.58%
MSRF-FLC-AHCC with SPV-based SAPF	4	3	99.42%	99.57%
Distorted source condition
MSRF-FLC-AHCC without SPV-based SAPF	22	28	96.54%	96.00%
MSRF-FLC-AHCC with SPV-based SAPF	6	7	99.15%	99.00%

RL: resistor-inductor; RC: resistor-capacitor; %ACC: percentage of accuracy; MSRF: modified synchronous reference frame; FLC: fuzzy logic controller; AFHCC: adaptive hysteresis current controller; SPV: solar photovoltaic; SAPF: shunt active power filter.

Conclusion

This study investigates the system's harmonic mitigation under the influence of the suggested MSRF-FLC-AHCC-based realized SAPF, taking into account both SPV-based SAPF and without SPV-SAPF. In addition, the optimal combination of RCG, regulation, and switching pulse generation mechanisms for VSI has been identified and contrasted. By deploying the SPV system across the DC-link side of the SAPF, the suggested MSRF-FLC-AHCC control technique beats its counterparts in terms of reducing deviation and settling time in the profile and mitigating harmonics, according to the results of sequences of simulation-based experimentations. In light of R-L- and R-C-type nonlinear loads, the comparative numerical findings show that the suggested SPV-based SAPF performance lessens the source current harmonics to 1.20% and 1.15% under normal source conditions, and 2.22% and 2.15% under distorted source conditions, respectively. The best outcomes in terms of mitigating harmonics have thus been provided by the suggested control structure. Furthermore, we intend to integrate additional renewable energy sources, such as wind and fuel cells, to analyze the system's behavior under varying operating conditions. The limitations related to scalability and PV intermittency will also be addressed and discussed in future work to enhance the overall robustness and applicability of the proposed system. As the present work is entirely simulation-based, we acknowledge the importance of experimental validation to strengthen the practical relevance of the proposed control scheme. In our future research, we plan to extend this study by incorporating hardware-in-the-loop testing and small-scale experimental implementation to evaluate real-time performance.

Footnotes

ORCID iDs

Amitkumar V Jha

Bhargav Appasani

Philibert Nsengiyumva

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of conflicting interests

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

References

Aguilera

Acuna

Rojas

, et al. (2024) An instantaneous power theory extension for the interphase power imbalance problem. IEEE Transactions on Power Electronics 39(10): 12261–12270.

Ahmadizadeh

Heidari

Thangavel

, et al. (2024) Technological advancements in sustainable and renewable solar energy systems. In: Verma

Thangavel

Dutt

(eds) Highly Efficient Thermal Renewable Energy Systems. Boca Raton: CRC Press, 23–39. 10.1201/9781003472629-2

Alhamrouni

Abdul

KNH

Salem

, et al. (2024) A comprehensive review on the role of artificial intelligence in power system stability, control, and protection: Insights and future directions. Applied Sciences 14(14): 6214.

Alturki

Alhussainy

Alghamdi

, et al. (2024) A novel point of common coupling direct power control method for grid integration of renewable energy sources: Performance evaluation among power quality phenomena. Energies 17(20): 5111.

Amini

Rastegar

Pichan

(2023) An optimized proportional resonant current controller based genetic algorithm for enhancing shunt active power filter performance. International Journal of Electrical Power & Energy Systems 156: 109738.

Barik

Samal

Gupta

, et al. (2024) Split-source inverter with adaptive control scheme-based shunt active power filter for power quality improvement. IET Power Electronics 17(14): 1893–1910.

Barik

Shankar

Sahoo

, et al. (2023) A novel negative feedback phase locked loop-based reference current generation technique for shunt active power filter. International Journal of Electrical Power & Energy Systems 153: 109389.

Barik

Shankar

Sahoo

(2022) Performance assessment of MSRF-FLC-AFHCC control strategy for shunt active power filters to mitigate power quality issues. AIP Conference Proceedings 2681(1): 020081.

Behera

Panda

VRN

(2024) An adaptive control approach for improved power quality and power ripple mitigation in a self-synchronized grid-tied VSG. IEEE Transactions on Industrial Electronics 72(4): 3664–3675.

10.

Belkhier

Oubelaid

(2024) Novel design and adaptive coordinated energy management of hybrid fuel-cells/tidal/wind/PV array energy systems with battery storage for microgrids. Energy Storage 6(1): e556.

11.

Boopathi

Indragandhi

(2024) Experimental investigations on photovoltaic interface neutral point clamped multilevel inverter-based shunt active power filter to enhance grid power quality. IEEE Access 12: 74482–74498.

12.

Chavali

Dey

Das

(2024) An abc reference frame hysteresis current controller for grid connected converter with constant switching frequency operation. IEEE Transactions on Industrial Electronics 72(2): 1159–1169.

13.

Fei

Wang

Zhang

(2024) Complementary sliding mode control using Petri probabilistic fuzzy recurrent neural network for active power filter. IEEE Transactions on Automation Science and Engineering 22: 1–15.

14.

Gude

Gulipalli

Chen

, et al. (2023) An enhanced performance of stationary reference frame controlled three-phase Vienna rectifier under grid voltage disturbances. In: Industry Applications Society Annual Meeting (IAS), IEEE, Nashville, TN, USA, 2023, pp. 1–10. IEEE. 10.1109/IAS54024.2023.10406791.

15.

Hamouda

Babes

Kahla

, et al. (2019) Predictive control of a grid connected PV system incorporating active power filter functionalities. In: 2019 1st International Conference on Sustainable Renewable Energy Systems and Applications (ICSRESA), Tebessa, Algeria, 4–5 December 2019, pp.1–6, IEEE. 10.1109/ICSRESA49121.2019.9182655.

16.

Hamouda

Zorig

Haddad

, et al. (2024) Enhanced the PQ of NI-grid connected to PV systems using a SAPF controlled by the predictive controller. In: 2024 12th International Conference on Systems and Control (ICSC), Batna, Algeria, 2–5 November 2024, pp.120–125. IEEE. 10.1109/ICSC63929.2024.10928896.

17.

Han

(2024) Analyzing the impact of deep learning algorithms and fuzzy logic approach for remote English translation. Scientific Reports 14: 14556.

18.

Hosseini

Gordan

Kalkan

(2024) Development of Z number-based fuzzy inference system to predict bearing capacity of circular foundations. Artificial Intelligence Review 57: 146.

19.

Janardhanan

Mulla

(2024) Design and development of PV-DVR system for solar photovoltaic energy harvesting and voltage power quality improvement. In: Durra

Arya

Giri

(eds) Custom Power Devices for Efficient Distributed Energy Systems. USA: Elsevier, 79–101. 10.1016/b978-0-443-21491-2.00009-9.

20.

Zhao

, et al. (2024) A voltage sensorless robust control scheme for LCL grid-connected inverters based on GESO approach. IEEE Transactions on Automation Science and Engineering 22: 2433–2444.

21.

Liu

Zhang

, et al. (2024) The fusion of fuzzy theories and natural language processing: A state-of-the-art survey. Applied Soft Computing 162: 111818.

22.

Lucchi

(2024) Conventional and innovative photovoltaic, solar thermal, and hybrid systems. In: Lucchi

(ed) Solar Energy Technologies in Cultural Heritage. Elsevier, 389–412. 10.1016/b978-0-443-23989-2.00014-8.

23.

Mohanty

Ray

Das

, et al. (2024) Enhancing power quality in contemporary utility systems: A comprehensive analysis of active power filters and control strategies. Energy Reports 11: 5575–5592.

24.

Mohapatra

Dey

(2023) The novel algorithm-based hysteresis current control technique (HCCT) to eliminate sub and inter harmonics (SIH) in load current. World Journal of Engineering 21(3): 522–534.

25.

Patel

Samal

Jena

, et al. (2022) Shunt active power filter with MSRF-PI-AHCC technique for harmonics mitigation in a hybrid energy system under load changing condition. Australian Journal of Electrical & Electronics Engineering 20(1): 63–77.

26.

Patnaik

Panda

(2023) Power quality enhancement using a novel SAPF control scheme employing high selectivity filter. International Journal of Emerging Electric Power Systems 25(3): 367–381.

27.

Popescu

Bitoleanu

Suru

, et al. (2024) Shunt Active Power Filters in Three-Phase, Three-Wire Systems: A Topical Review. Energies 17(12): 2867.

28.

Rai

Kumar

Singh

(2024) Three-Phase grid connected shunt active power filter based on adaptive Q-LMF control technique. IEEE Transactions on Power Electronics 39(8): 10216–10225.

29.

Sanjenbam

Singh

Shah

(2024) Power quality enhancement of stand-alone hydro power generation system using UPQC. IEEE Transactions on Industrial Informatics 20(10): 12062–12071.

30.

Sbabo

Menzi

Imperiali

, et al. (2024) Comparative evaluation of three-phase SiC-based voltage/current-source inverter motor drives. In: 2024 IEEE Workshop on Wide Bandgap Power Devices and Applications in Europe (WiPDA Europe), Cardiff, United Kingdom, 2024, pp.1–6. IEEE. 10.1109/WiPDAEurope62087.2024.10797449.

31.

Singh

Kumar

Rathore

(2022) Least mean fourth theory-based dynamic voltage restorer for the mitigation of voltage SAG and harmonics. IETE Journal of Research 70(1): 965–978.

32.

Soliman

Rafin

SMSH

Mohammad

(2024) Enhanced DC voltage and power regulation using intelligent data-driven control for AC/DC converters in DC microgrid applications. IEEE Transactions on Industry Applications 60(6): 8383–8392.

33.

Srivastava

Narayanan

Singh

, et al. (2024) An implementation of a frequency decomposition algorithm for power quality smoothening in a hybrid SPVA-BES microgrid. IEEE Transactions on Industrial Informatics 20(11): 12597–12607.

34.

Subbarao

Dasari

Duvvuri

, et al. (2023) Design, control and performance comparison of PI and ANFIS controllers for BLDC motor driven electric vehicles. Measurement Sensors 31: 101001.

35.

Suprabhath

Prasad

MVS

Madichetty

, et al. (2024) Standalone deployment of two-fold deep neural network in distributed DC microgrid-FDIA detection and mitigation scheme. IEEE Journal of Emerging and Selected Topics in Industrial Electronics 6(1): 204–214.

36.

Szromba

(2024) Improvement of the source current quality for a shunt active power filter operating using hysteresis technique with stabilized switching frequency. Energies 17(20): 5098.

37.

Tang

Ahmad

(2024) Fuzzy logic approach for controlling uncertain and nonlinear systems: a comprehensive review of applications and advances. Systems Science & Control Engineering 12(1): 2394429.

38.

Wang

Garcia

, et al. (2024) Robust sequential model-free predictive control of a three-level T-type shunt active power filter. IEEE Transactions on Power Electronics 39(8): 9505–9517.

39.

Xie

Zhuo

, et al. (2023) Analysis and stabilization for full harmonic compensation oscillation in SAPF system with source current direct control. IET Power Electronics 17(1): 107–120.

40.

Yadav

Hassanpour

Chub

, et al. (2024) Performance evaluation of step-up/down partial power converters based on current-fed DC-DC topologies. IEEE Transactions on Industry Applications 60(5): 7111–7124.

41.

Zorig

Souilah

Hamouda

, et al. (2024) Improving the power quality of the grid-connected PV system using a high selective self tuning filter. In: 2024 2nd International Conference on Electrical Engineering and Automatic Control (ICEEAC), Setif, Algeria. 12–14 May 2024, pp.1–6. IEEE. 10.1109/ICEEAC61226.2024.10576434

Advanced control strategy with SPV integrated shunt active power filter for power quality improvement

Abstract

Keywords

Introduction

Design of SAPF

SAPF control scheme

Reference current generation (RCG)

Control technique for DC-link voltage

Switching pulse generation technique

Solar PV system

PV array

Control of step-up converter to achieve MPPT

The step-up converter's construction

Selection of DC-link reference voltage

Designing an intermediate circuit capacitor (DC link)

Choosing the inductance of the filter

Results and discussion

Performance study under Scenario 1

Performance study under Scenario 2

Performance study under Scenario 3

THD analysis

V dc regulation

Conclusion

Footnotes

ORCID iDs

Funding

Declaration of conflicting interests

References

$V_{dc}$ regulation