基于优化广义S变换和混合输入神经网络的电能质量扰动识别

doi:10.3969/j.issn.1000-1158.2025.01.09

Abstract
Figure/Table
References (25)
Related Citation (15)

Download: PDF (2595 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract Based on the correlation between the time maximum value curve and frequency maximum value curve of generalized S-transform time-frequency matrix and the amplitude and spectrum envelope of power quality signal， a method of optimizing generalized S-transform is proposed to select the parameters of Gaussian window function adaptively， which fully preserves the amplitude and frequency characteristics of power quality disturbance. Then， a hybrid input neural network framework is proposed to automatically extract the features of the original time series and the time-frequency matrix obtained from the optimized generalized S-transform. Finally， the features extracted from the two inputs are combined and the disturbance types are identified by the fully connected layer. Through the training and verification of the simulation data set containing 26 types of power quality disturbance， the results show that the disturbance recognition accuracy of the proposed method is 99.77%， and then the two kinds of actual grid disturbance signals are tested， and the disturbance recognition accuracy can still reach 92.5%， which is higher than the traditional single input neural network.

Key words： electrical measurement power quality disturbance identification S-transform convolutional neural network mixed input

Received: 25 March 2024 Published: 14 January 2025

PACS:

TB971

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	LIU Haitao
	WU Xiang
	ZHANG Shuqing
	LIU Dapeng
	LIU Yong
	MU Yong

Cite this article:

LIU Haitao,WU Xiang,ZHANG Shuqing, et al. Power Quality Disturbance Identification Based on Optimized Generalized S⁃transform and Hybrid Input Neural Network[J]. Acta Metrologica Sinica, 2025, 46(1): 53-61.

URL:

http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2025.01.09 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2025/V46/I1/53

2	ELBOUCHIKHI E， ZIA M F， BENBOUZID M， et al. Overview of signal processing and machine learning for smart grid condition monitoring［J］. Electronics， 2021， 10（21）： 1-40.
4	李盼，娄钊瑜，马康，等. 一种自适应S变换在电能质量特征提取中的应用［J］. 中国电机工程学报， 2021， 41（22）： 7660-7668. LI P， LOU Z Y， MA K， et al. Application of Adaptive S-transform in Power Quality Feature Extraction［J］. Proceedings of the CSEE， 2021， 41（22）： 7660-7668.
6	KUMAR R， SAXENA A， KUMAR R， et al. A comprehensive overview on modified versions of stockwell transform for power quality monitoring［J］. IEEE Access， 2022，10（9）： 91963-91975.
10	曹梦舟，张艳. 基于卷积?长短期记忆网络的电能质量扰动分类［J］. 电力系统保护与控制， 2020， 48（2）： 86-92. CAO M Z， ZHANG Y. Classification for power quality disturbances based on CNN?LSTM network［J］. Power System Protection and Control， 2020， 48（2）： 86-92.
12	CAI K W， HU T P， CAO W P， et al. Classifying power quality disturbances based on phase space reconstruction and a convolutional neural network［J］. Applied Sciences， 2019， 9（18）： 1-15.
14	SINDI H， NOUR M， RAWA M， et al. A novel hybrid deep learning approach including combination of 1D power signals and 2D signal images for power quality disturbance classification［J］. Expert Systems with Applications， 2021， 174（3）：1-13.
16	USMAN A， CHOUDHRY M A. A precision detection technique for power disturbance in electrical system［J］. Electrical Engineering， 2022， 104（2）： 781-796.
20	MACHLEV R， CHACHKES A， BELIKOV J， et al. Open source dataset generator for power quality disturbances with deep?learning reference classifiers［J］. Electric Power Systems Research， 2021， 195（3）：1-7.
9	ZHU Z Q， LEI Y B， QI G Q， et al. A review of the application of deep learning in intelligent fault diagnosis of rotating machinery［J］. Measurement， 2023， 206（12）： 1-24.
22	MOTLAGH S Z T， FOROUD A A. Power quality disturbances recognition using adaptive chirp mode pursuit and grasshopper optimized support vector machines［J］. Measurement， 2021， 168（11）： 1-20.
24	OLIVIA FLORENCIAS?OLIVEROS， ESPINOSA?GAVIRAM. J.， JUAN?JOSÈ GONZÁLEZ?DE?LA ROSA， et al. Real?life Power Quality Sags［EB/OL］. IEEE Dataport， doi： https：//dx.doi.org/10.21227/H2K88D.
19	de OLIVEIRA R A， BOLLEN M H J. Deep learning for power quality［J］. Electric Power Systems Research， 2023， 214（7）： 1-12.
3	张淑清，张赟，刘海涛，等.多重分形去趋势波动分析及改进决策树在电能质量分析中的应用［J］.计量学报，2021，42（4）：424-431. ZHANG S Q， ZHANG Y， LIU H T， et al. Application in Power Quality Analysis Based on Multifractal Detrended Fluctuation Analysis and lmproved Decision Tree［J］. Acta Metrologica Sinica， 2021， 42（4）： 424-431.
5	伊慧娟，高云鹏，朱彦卿，等. 基于自适应不完全S变换与LOO?KELM算法的复合电能质量扰动识别［J］. 电力自动化设备， 2022， 42（1）： 199-205. YI H J， GAO Y P， ZHU Y Q， et al. Recognition of composite power quality disturbance based on improved incomplete S transform and LOO?KELM algorithm［J］. Electric Power Automation Equipment， 2022， 42（1）： 199-205.
7	LI P， Han Z， XIANG W X， et al. A fast adaptive S?transform for complex quality disturbance feature extraction［J］. IEEE Transactions on Industrial Electronics， 2022， 70（5）： 5266-5276.
8	LIANG C B， TENG Z S， LI J M， et al. A Kaiser window?based S?transform for time?frequency analysis of power quality signals［J］. IEEE Transactions on Industrial Informatics， 2021， 18（2）： 965-975.
13	SHUKLA J， PANIGRAHI B K， RAY P K. Power quality disturbances classification based on Gramian angular summation field method and convolutional neural networks［J］. International Transactions on Electrical Energy Systems， 2021， 31（12）： 1-16.
15	ZHU K Z， TENG Z S， QIU W， et al. Complex Disturbances Identification： A Novel PQDs Decomposition and Modeling Method［J］. IEEE Transactions on Industrial Electronics， 2022， 70（6）： 6356-6365.
17	SWARNKAR N K， MAHELA O P， LALWANI M. Multivariable signal processing algorithm for identification of power quality disturbances［J］. Electric Power Systems Research， 2023， 221（3）：1-17.
18	GONZALEZ?ABREU A D， DELGADO?PRIETO M， OSORNIO-RIOS R A， et al. A novel deep learning?based diagnosis method applied to power quality disturbances［J］. Energies， 2021， 14（10）： 1-17.
23	MARKOVSKA M， TASKOVSKI D， KOKOLANSKI Z， et al. Real?time implementation of optimized power quality events classifier［J］. IEEE Transactions on Industry Applications， 2020， 56（4）： 3431-3442.
1	YU Y Q， ZHAO W， LI S S， et al. A two?stage wavelet decomposition method for instantaneous power quality indices estimation considering interharmonics and transient disturbances［J］. IEEE Transactions on Instrumentation and Measurement， 2021， 70（1）： 1-13.
11	CUI C C， DUAN Y J， HU H L， et al. Detection and classification of multiple power quality disturbances using stockwell transform and deep learning［J］. IEEE Transactions on Instrumentation and Measurement， 2022， 71（11）： 1-12.
21	LIU Y L， JIN T， MOHAMED M A， et al. A novel three?step classification approach based on time?dependent spectral features for complex power quality disturbances［J］. IEEE Transactions on Instrumentation and Measurement， 2021， 70（3）： 1-14.
25	OLIVIA FLORENCIAS?OLIVEROS， ESPINOSA?GAVIRAM J， JUAN?JOSÈ GONZÁLEZ?DE?LA ROSA， et al. Real?life Power Quality Transients［EB/OL］. IEEE Dataport， doi： https：//dx.doi.org/10.21227/H2Q30W.