基于1DCNN-BiLSTM-BiGRU的电能质量扰动分类方法

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Abstract To address the issue of reduced recognition accuracy in identifying power quality disturbance (PQD) due to noise interference, this paper introduces a PQD classification method based on one-dimensional convolutional neural network (1DCNN)-bidirectional long short-term memory (BiLSTM)-bidirectional gated recurrent unit (BiGRU). This method initially utilizes 1DCNN to effectively extract shallow local features from the raw signals. Subsequently, it employs a combination of BiLSTM and BiGRU modules to delve deeper into temporal information and contextual relationships, facilitating the extraction of deep temporal features. Finally, the extracted features are input to the classification module for PQD recognition. Simulation results show that the proposed method has better accuracy and stronger noise resistance.

Key words： power quality one-dimensional convolutional neural network (1DCNN) bidirectional long short-term memory (BiLSTM) bidirectional gated recurrent unit (BiGRU)

Received: 03 January 2024

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	Articles by authors
	WANG Lihui
	KE Yong
	SU Rukai

Cite this article:

WANG Lihui,KE Yong,SU Rukai. The classification method of power quality disturbance based on 1DCNN-BiLSTM-BiGRU[J]. Electrical Engineering, 2024, 25(5): 51-56.

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https://dqjs.cesmedia.cn/EN/Y2024/V25/I5/51

[1] 夏得青, 向星宇, 李宽龙, 等. 农村配电网低电压治理研究进展[J]. 电气技术, 2023, 24(6): 1-5.
[2] 严静, 邵振国. 电能质量谐波监测与评估综述[J]. 电气技术, 2020, 21(7): 1-7.
[3] 贾君宜, 吴命利, 宋可荐, 等. 基于短时傅里叶变换和深度学习的牵引网过电压辨识[J]. 电气技术, 2021, 22(10): 1-10.
[4] 陈子龙, 冀卓婷, 郑重, 等. 基于传递函数和小波变换的变压器故障诊断研究[J]. 电气技术, 2017, 18(12): 30-37.
[5] 李建文, 秦刚, 李永刚, 等. 基于布莱克曼窗与窗宽比的S变换电能质量扰动特征提取[J]. 高电压技术, 2020, 46(8): 2769-2779.
[6] 杨逸帆, 赵兵兵, 康迪, 等. 基于改进希尔伯特-黄变换的电力系统谐波检测系统设计[J]. 电气技术, 2022, 23(5): 9-17.
[7] LIU Zhigang, CUI Yan, LI Wenhui.A classification method for complex power quality disturbances using EEMD and rank wavelet SVM[J]. IEEE Transactions on Smart Grid, 2015, 6(4): 1678-1685.
[8] 王涛, 孙志鹏, 崔青, 等. 基于分类决策树算法的电力变压器故障诊断研究[J]. 电气技术, 2019, 20(11): 16-19.
[9] 董志文, 苏晶晶. 基于变分模态分解能量熵混合时域特征和随机森林的故障电弧检测方法[J]. 电气技术, 2024, 25(1): 1-7.
[10] 孙志鹏, 崔青, 张志磊, 等. 多分类支持向量机在电力变压器故障诊断中的应用[J]. 电气技术, 2019, 20(10): 25-28.
[11] QU Xiaoyu, DONG Kun, ZHAO Jianfeng, et al.An identification and location method for power quality disturbance sources in MMC converter based on KNN algorithm[C]//2021 4th International Conference on Energy, Electrical and Power Engineering (CEEPE), Chongqing, China, 2021: 170-177.
[12] KHAN M R, PADHI S K, SAHU B N, et al.Non stationary signal analysis and classification using FTT transform and Naive Bayes classifier[C]//2015 IEEE Power, Communication and Information Technology Conference (PCITC), Bhubaneswar, India, 2015: 967-972.
[13] 陈华丰, 杨志刚, 曾涛. 基于S变换和规则基的复合电能质量扰动识别[J]. 电测与仪表, 2015, 52(12): 122-128.
[14] CAI Kewei, CAO Wenping, AARNIOVUORI L, et al.Classification of power quality disturbances using Wigner-Ville distribution and deep convolutional neural networks[J]. IEEE Access, 2019, 7: 119099-119109.
[15] 郑炜, 林瑞全, 王俊, 等. 基于GAF与卷积神经网络的电能质量扰动分类[J]. 电力系统保护与控制, 2021, 49(11): 97-104.
[16] 陈伟, 何家欢, 裴喜平. 基于相空间重构和卷积神经网络的电能质量扰动分类[J]. 电力系统保护与控制, 2018, 46(14): 87-93.
[17] WANG Jidong, XU Zhilin, CHE Yanbo.Power quality disturbance classification based on compressed sensing and deep convolution neural networks[J]. IEEE Access, 2019, 7: 78336-78346.
[18] 刘伟, 王凯. 基于通道选择多尺度融合深度残差网络的电能质量扰动识别[J]. 电气技术, 2023, 24(5): 11-15, 22.
[19] 吴怀诚, 刘家强, 岳蕾, 等. 基于多特征融合的卷积神经网络的电能质量扰动识别方法[J]. 电网与清洁能源, 2023, 39(9): 19-23, 31.
[20] 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.
[21] MOUTHAMI K, ANANDAMURUGAN S, AYYASAMY S.BERT-BiLSTM-BiGRU-CRF: ensemble multi models learning for product review sentiment analysis[C]//2022 6th International Conference on Electronics, Communication and Aerospace Technology, Coimbatore, India, 2022: 1514-1519.
[22] IEEE recommended practice for monitoring electric power quality: IEEE Std1159-1995[S].
[23] 郭云峰, 杨晓梅. 基于SVM的电能质量扰动信号分类方法[J]. 计算机应用与软件, 2022, 39(7): 95-100, 120.
[24] 贺虎成, 辛钟毓, 王琳珂, 等. 基于特征向量筛选和双层BPNN的电能质量扰动识别方法[J]. 高电压技术, 2022, 48(4): 1237-1250.
[25] 王伟, 李开成, 许立武, 等. 基于一维卷积神经网络多任务学习的电能质量扰动识别方法[J]. 电测与仪表, 2022, 59(3): 18-25.
[26] 奚鑫泽, 邢超, 覃日升, 等. 基于多层特征融合注意力网络的电能质量扰动识别方法[J]. 智慧电力, 2022, 50(10): 37-44.