Rough signal processing of AC power intelligent sensor under the background of smart grid

Abstract

In the rough signal processing of AC intelligent sensor, the effective value and initial phase of voltage/current determine the test accuracy. To improve the harmonic detection and compensation performance of the existing APF and promote the improvement of power grid power quality. The direct positioning method is used as the comparison method, and the error LMS method is proposed to obtain and test the voltage and current signals of intelligent sensors. The simulation results of error LMS method show that the accuracy of voltage RMS and initial phase value calculated by method 1 increases with the increase of the number of sampling points, while the accuracy of voltage RMS of method 2 and method 3 does not change significantly. The results of correlation analysis method show that the test accuracy of the proposed method is 1/2–1/3 of the direct definition method when the amplitude of interference noise signal is 5%, 10% and 15%. Compared with the direct definition method, the rough signal processing technology has lower sampling amount and higher test accuracy, which helps to simplify the system and save the overhead cost.

Keywords

Intelligent sensor minimum mean error method correlation analysis method signal processing testing

Get full access to this article

View all access options for this article.

References

Omitaomu

Niu

. Artificial intelligence techniques in smart grid: A survey. Smart Cities 2021; 4(2): 548-568.

Zhang

Liu

. Smart grid cyber-physical attack and defense: A review. IEEE Access 2021; 9: 29641-29659.

Siniosoglou

Radoglou-Grammatikis

Efstathopoulos

Namasudra

Kadry

. A unified deep learning anomaly detection and classification approach for smart grid environments. IEEE Trans Network Serv Manage. 2021; 18(2): 1137-1151.

Liu

Chen

Kailkhura

Zhang

Hero

IAO

Varshney

. A primer on zeroth-order optimization in signal processing and machine learning: Principals, recent advances, and applications. IEEE Signal Process Mag 2020; 37(5): 43-54.

Chong

Park

. Efficiency low-power signal processing for multi-channel LiDAR sensor-based vehicle detection platform. J Korea Inst Inf Commun Eng 2021; 25(7): 977-985.

Gao

Shen

Wang

Nazir

. Milling chatter detection system based on multi-sensor signal fusion. IEEE Sens J. 2021; 21(22): 25243-25251.

Chen

Zhu

Ren

Liu

Jia

Luo

Yang

. Processing near sensor architecture in mixed-signal domain with CMOS image sensor of convolutional-kernel-readout method. IEEE Trans Circ Syst I: Regular Pap. 2020; 67(2): 389-400.

Saponara

Greco

Gini

. Radar-on-chip/in-package in autonomous driving vehicles and intelligent transport systems: Opportunities and challenges. IEEE Signal Process Mag. 2019; 36(5): 71-84.

Kumar

Abhishek

Shah

Namasudra

Kadry

. A novel elliptic curve cryptography-based system for smart grid communication. Int J Web Grid Serv. 2021; 17(4): 321-342.

10.

Mirifar

Kadivar

Azarhoushang

. First steps through intelligent grinding using machine learning via integrated acoustic emission sensors. J Manuf Mater Process. 2020; 4(2): 35-50.

11.

Zhang

Xue

Fleischer

. Chatter identification of thin-walled parts for intelligent manufacturing based on multi-signal processing. Adv Manuf Progress: English Version. 2021; 9(1): 22-33.

12.

Gao

Liu

Gao

Zheng

Hou

Wang

. Intelligent occlusion stabilization splint with stress-sensor system for bruxism diagnosis and treatment. Sens (Basel, Switzerland). 2020; 20(1): 89-103.

13.

Gough

Santos

AlSkaif

Javadi

Castro

Catalão

. Preserving privacy of smart meter data in a smart grid environment. IEEE Trans Ind Inf. 2021; 18(1): 707-718.

14.

Liu

Ren

Qiao

Wei

Liu

Luo

Yang

. NS-CIM: A current-mode computation-in-memory architecture enabling near-sensor processing for intelligent IoT vision nodes. IEEE Trans Circ Syst I: Regular Pap. 2020; 67(9): 2909-2922.

15.

Siniosoglou

Radoglou-Grammatikis

Efstathopoulos

Fouliras

Sarigiannidis

. Unified deep learning anomaly detection and classification approach for smart grid environments. IEEE Trans Network Serv Manage. 2021; 18(2): 1137-1151.

16.

Takahashi

Nishiwaki

Sugimoto

. Computational model of plasma-organic-polymer-film-coated QCR sensor for analysing its dynamic response. Int J Smart Sens Intell Syst. 2020; 7(5): 1-4.

17.

Wang

Huang

Zhang

. Secure and efficient mutual authentication protocol for smart grid under blockchain. Peer-to-Peer Network Appl. 2021; 14(5): 2681-2693.

18.

Kumar

Abhishek

Shah

Namasudra

Kadry

. A novel elliptic curve cryptography-based system for smart grid communication. Int J Web Grid Serv. 2021; 17(1): 321-342.

19.

Massaoudi

Abu-Rub

Refaat

Chihi

Oueslati

. Deep learning in smart grid technology: A review of recent advancements and future prospects. IEEE Access. 2021; 9: 54558-54578.

20.

Jiang

Lin

Chen

. An improved unsupervised single-channel speech separation algorithm for processing speech sensor signals. Wireless Commun Mob Comput. 2021; (170): 1-13.

21.

Wang

. Research on signal processing technology optimization of contact image sensor based on BP neural network algorithm. J Intell Fuzzy Syst. 2020; 38(2): 1-9.

22.

Xia

Shao

De Silva

. A stacked GRU-RNN-based approach for predicting renewable energy and electricity load for smart grid operation. IEEE Trans Ind Inf. 2021; 17(9): 7050-7059.