Fault Detection on Insulated Overhead Conductors Based on DWT-LSTM and Partial Discharge

Abstract

Insulated conductors can improve the stability of power transmission and reduce the construction space compared with traditional bare conductors. Therefore, insulated conductors are used more and more in overhead power transmission. However, a major challenge of using insulated overhead conductors (IOC) is that the ordinary protection devices are not able to detect the phase-to-ground faults and something, such as tree branch, hitting conductor events. This may cause an accident such as power failure or electrical fire and result in serious damage. In this paper, a new approach, which is based on Discrete Wavelet Transform (DWT) and Long Short Term Memory network (LSTM) for detecting of IOC fault according to partial discharge, is presented. Firstly, the original signal is denoised by DWT. Secondly, the denoised signal is decomposed and extracted features on different layers by DWT. Finally, IOC fault is detected by LSTM. This method can improve the detection accuracy of IOC fault which is tested on the ENET public data set and compared with other classification methods. INDEX TERMS Insulated overhead conductors, partial discharge, discrete wavelet transform, long short term memory network, fault detection.

Interpretable Detection of Partial Discharge in Power Lines with Deep Learning

“…To the best of our knowledge, no other published study outside of the competition leaderboard reported results on the second test dataset. In [ 12 , 18 , 19 ], the reported results are computed on a subset of the labeled dataset. In [ 12 ], results are reported on the full training set and might therefore be overfitted.…”

“…We report anyway their results in Table 2, where we recompute the value of the metrics they would achieve on our set, assuming constant sensitivity and specificity of their model. This cannot be done for the work in [ 19 ], as the numbers of tested samples with and without PD are not reported.…”

“…They aim at characterizing the pulses with engineered features. The features become a multidimensional space where decision boundaries are established, for example with traditional classifiers such as random forest [ 7 ], support vector machines (SVM) [ 11 , 12 ], or deep learning methods such as artificial neural networks (ANN) [ 13 ], convolutional neural networks (CNN) [ 14 , 15 ], autoencoders [ 16 ], and recurrent neural networks (long short-term memory networks (LSTM)) [ 17 , 18 , 19 ]. For a more exhaustive overview, the reader is referred to the literature reviews in [ 20 , 21 ].…”

Power System Event Classification and Localization Using a Convolutional Neural Network

“…Nowadays, great efforts have been made in developing new methodologies, from both data and model perspectives, for fault detection and isolation (FDI) (Chen and Patton, 2012;Costamagna et al, 2015;Jan et al, 2017;Zhang et al, 2016;Alhelou et al, 2018). Data-driven FDI has received significant attention recently; for example, approaches using deep learning techniques such as long short-term memory (LSTM) networks and convolutional neural networks (CNNs) are popular among the deep neural networks (Chen et al, 2017;Zhang et al, 2017;Patil et al, 2019;Paul and Mohanty, 2019;Qu et al, 2020). An LSTM network (Hochreiter and Schmidhuber, 1997) has advantages for learning sequences containing both short-and long-term patterns from time series (Malhotra et al, 2015), while CNN is a commodity in the computer vision field that is capable of achieving record-breaking results on highly challenging image datasets (Krizhevsky et al, 2012;Zhu et al, 2018).…”

Research on Optical Fiber Sensor Localization Based on the Partial Discharge Ultrasonic Characteristics in Long-Distance XLPE Cables

“…A numerical model that describes the partial discharge characteristics of high-voltage direct-current HVDC cables was built considering the effects of the electric field and charge dynamics [10], and the partial discharge position was determined by analyzing the time and angle information of UHF electromagnetic waves generated by partial discharge [11]. A new approach was proposed based on the discrete wavelet transform for detecting insulated overhead conductor faults according to partial discharge [12], which achieves the identification and classification of different types of events, including internal PD, corona PD, surface PD, and noise [13]. However, the specific localization algorithm requires large sensor hardware overhead and is difficult to apply.…”