Power Quality Disturbance Recognition based on Deep Neural Network and Adaptive Feature Fusion

Lei Chen; Chao Zhou; Jianjun Chen

doi:10.54097/fcis.v6i1.02

Authors

Lei Chen
Chao Zhou
Jianjun Chen

DOI:

https://doi.org/10.54097/fcis.v6i1.02

Keywords:

Power Quality Disturbance, Neural Network, Feature Fusion, Adaptive Weights, GADF Algorithm

Abstract

Aiming at the problems of low recognition accuracy and low noise resistance of current power quality disturbance recognition algorithms, a power quality disturbance recognition method based on the fusion of neural network and adaptive features is proposed. Firstly, one-dimensional features are extracted by 1D_CNN+GRU network. The GADF algorithm and 2D_CNN are used to extract 2D features, and then the extracted 1D features and 2D features are adaptively fused into a new feature. Finally, the new features are input into the channel attention mechanism and classified through the full connection layer. The experimental results show that the classification accuracy of the proposed method is above 92% for 40 disturbance types containing single, double, triple and quadruple disturbances, and above 96% for 20 disturbance types containing single and double disturbances.

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References

Wang Weibo, Zhang Bin, Zeng Wenyu, et al. Power quality disturbance classification based on feature fusion one-dimensional Convolutional neural network [J]. Power System Protection and Control, 2020, 48(06): 53-60.

Sebastijan S, Niko L, Boheme, et al. Power quality experimental analysis of grid-connected photovoltaic systems in urban distribution networks[J]. Energy, 2017, 139.

Jin Guo, Zhu Qing-Zhi, Meng Yang, et al. Power Quality disturbance multi-label classification algorithm based on multi-layer extreme Learning Machine [J]. Power System Protection and Control, 2020, 48(08): 96-105.

HUANG Jianming, LI Xiaoming. Harmonic detection method of power system combined with short-time Fourier transform and spectral steepness[J]. Power System Protection and Control, 2017, 45(07): 43-50.

Tao Caixia, Du Xue, Gao Fengyang, et al. Fault location for single-phase grounding of hybrid transmission lines based on empirical wavelet transform [J]. Power System Protection and Control, 2021, 49(10): 105-112.

Cheng Zhiyou, Yang Meng. Power quality disturbance type recognition based on two-dimensional discrete cosine S transform [J]. Power System Protection and Control, 2021, 49(17): 85-92. (in Chinese)

Luo Xuelian, Liu Guiying, Liu Xiangyin. Research on Detection and Identification of transient power quality Disturbance in Microgrid based on Wavelet transform and Hilbert-Yellow Transform [J]. Power Capacitors and reactive power compensation, 2020, 41(03): 182-188.

LI Xiaona, SHEN Xing-lai, Xue Xue, et al. Power quality disturbance identification based on improved HHT and decision tree [J]. Journal of Electric Power Construction, 2017, 38(02): 114-121.

Wang Jiye, Zhu Xinyan, Zhao Guang, et al. Research on optimal outage model based on deep artificial neural network and GIS data [J]. Power System Protection and Control, 2019, 47(16): 58-63.

Guo Yunfeng, Yang Xiaomei. Power quality disturbance signal classification based on SVM [J]. Computer Applications and Software, 2022, 39(07): 95-100+120.

Li Zuming, Lv Qianyun, Chen Nuo, et al. Power quality complex disturbance recognition based on chaotic integrated decision Tree [J]. Power System Protection and Control, 2021, 49(21): 18-27.

Zhang Minglong, Zhang Zhenyu, Luo Xiang, et al. Research on Hybrid disturbance waveform identification algorithm based on Multi-core support Vector Machine [J]. Power System Protection and Control, 2022, 50(15): 43-49.

Wang Renming, Wang Hongyang, Zhang Yunning, et al. Hybrid power quality disturbance identification Method based on segmental improved S-transform and Random forest [J]. Protection and Control of Power Systems, 2020, 48(07): 19-28.

XU Yanchun, Fan Shirong, Tan Chao, et al. Power Quality Disturbance detection and classification of high permeability active distribution Network based on improved EWT-CMPE [J]. Power Grid Technology, 2020, 44(10): 3991-4005.

YAO Li, Sun Jianjun, Ma Chenbo. Fault diagnosis Method of Rolling Bearing Based on Gram Angle Field and CNN-RNN [J]. Journal of Bearings, 2022(02): 61-67.

Yang Wei, Pu Caixia, Yang Kun, et al. Short-term Fault prediction Method of Transformer Based on CNN-GRU Combined neural Network [J]. Power Systems Protection and Control, 2022, 50(06): 107-116.

Xu Dong, Yang Guan, Liu Xiaoming, et al. Small sample image classification based on adaptive feature fusion and transformation [J]. Computer Engineering and Applications, 2022, 58(24): 223-232.

Song Tiewei, Shi Weifeng, Bi Zong, et al. Power quality Disturbance Identification of Marine Power System based on 2D-ResNet [J]. Power System Protection and Control, 2022, 50(10): 94-103.