Classification of Power-Quality Disturbances Using Deep Belief Network

Li, Cuimei; Li, Zeng-Xiang; Jiang, Nan; Qi, Zhi-Liang; Wu, Jianhua

doi:10.1109/icwapr.2018.8521311

Cited by 15 publications

(5 citation statements)

References 15 publications

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“…Specific attempts have been made to classify PQDs using stacked auto-encoders (SAEs) and stacked sparse auto-encoders (SSAEs), and they produced positive results in terms of classification [7], [39], [40]. Deng et al [41] attempted to detect PQDs using a RNN, and conducted a study using a deep belief network (DBN) [42], [43]. Various CNN architectures have been used to classify PQDs, and studies on hybrid CNN utilization methods have also been conducted for detection and classification [9], [44]- [47].…”

Section: Deep Learning For Electric Power Systemmentioning

confidence: 99%

Deep Learning-Based Method for the Robust and Efficient Fault Diagnosis in the Electric Power System

Yoon

2022

IEEE Access

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The robust and efficient diagnosis of power quality disturbances (PQDs) in electric power systems (EPSs) is one of the most important steps to protect a power system with minimal damage. However, the conventional fault detection methods used in the EPS mainly rely on heavy mathematical calculations, resulting in delayed actions against PQDs. To overcome these limitations, deep learning has been recently proposed to diagnose PQDs in the EPS, which allows the extraction of features from a huge amount of data to delineate subtle differences in electrical waveforms under faulty conditions. In this study, a deep learningbased diagnostic method for PQDs was proposed by exploiting a convolutional neural network (CNN) and simulated realistic three-phase voltage and current waveforms obtained from the PSCAD/EMTDC software. Specifically, PQDs related to various faults in EPSs were assessed to demonstrate the applicability of the deep-learning method as a fault diagnostic method. The proposed CNN model, trained by end-toend learning and supervised learning approaches, successfully classified the type and location of the faults. Moreover, we found that simulated data obtained at the sampling rate of 50 Hz also accurately diagnosed the faults with an accuracy of over 99%; therefore, the proposed method could be a potential diagnostic tool in practice.

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Section: Deep Learning For Electric Power Systemmentioning

confidence: 99%

Deep Learning-Based Method for the Robust and Efficient Fault Diagnosis in the Electric Power System

Yoon

2022

IEEE Access

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“…Recently, various deep learning algorithms such as deep belief network (DBN), convolutional neural network (CNN), recurrent neural network (RNN), and auto-encoder (AE) have been proposed for PQD classification. In [20], an approach of deep belief network (DBN) is proposed for the classification of PQDs. In [3], a convolutional neural network (CNN) is used to extract various features, and then these features are fed to the multi-class support vector classifier to detect and classify the PQD events.…”

Section: Related Workmentioning

confidence: 99%

Type identification and time location of multiple power quality disturbances based on KF‐ML‐aided DBN

Yang

Chen

Tang

et al. 2021

IET Generation Trans & Dist

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Type identification and time location of power quality disturbances (PQDs) is the key to adopting corresponding measures to suppress disturbances. More complex multiple disturbances caused by the overlapping of different micro-grids make it a challenging task. The paper proposes a hybrid approach combing KF-ML (Kalman filter based on maximum likelihood) with deep belief network (DBN) for dealing with PQDs. To be specific, the KF-ML is firstly applied to reduce noise from the original distorted signal, and the innovation sequence obtained by KF-ML can be used to locate starting-ending times of PQDs. Then, the DBN, which fuses feature extraction and classification into a single block, is capable of recognizing the type of PQDs accurately. To verify the effectiveness of the proposed method, 20 classes of PQDs with noise interference are tested, and experiment results show that the detection time of the proposed method is very close to the set time, and the absolute error of time location is less than 0.3 ms. The average classification accuracy at different noise levels reaches about 95%, and is very high even with more disturbances combined. Thus, the proposed method is immune to noise and less affected with more disturbances combined relative to other methods.1 PQDs obviously emerged [3].Multiple power quality disturbances (MPQDs), which are composed of several single disturbances with different type, dif-This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

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“…Besides, both [21,22] have not considered the overfitting problem, which may extremely decline the accuracy of DNN. e PQDs are assessed based on a deep belief network in [23]. Although these methods are producing acceptable results in PQD detection in single PQDs, they scuffle with multiple PQDs.…”

Section: Introductionmentioning

confidence: 99%

An Improved Power Quality Disturbance Detection Using Deep Learning Approach

Sekar¹,

Karthick²,

Subramanian³

et al. 2022

Mathematical Problems in Engineering

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Recently, the distribution network has been integrated with an increasing number of renewable energy sources (RESs) to create hybrid power systems. Due to the interconnection of RESs, there is an increase in power quality disturbances (PQDs). The aim of this article was to present an innovative method for detecting and classifying PQDs that combines convolutional neural networks (CNNs) and long short-term memory (LSTM). The disturbance signals are fed into a combined CNN and LSTM model, which automatically recognizes and classifies the features associated with power quality disturbances. In comparison with other methods, the proposed method overcomes the limitations associated with conventional signal analysis and feature selection. Additionally, to validate the proposed method's robustness, data samples from a modified IEEE 13-node hybrid system are collected and tested using MATLAB/Simulink. The results are good and encouraging.

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Classification of Power-Quality Disturbances Using Deep Belief Network

Cited by 15 publications

References 15 publications

Deep Learning-Based Method for the Robust and Efficient Fault Diagnosis in the Electric Power System

Deep Learning-Based Method for the Robust and Efficient Fault Diagnosis in the Electric Power System

Type identification and time location of multiple power quality disturbances based on KF‐ML‐aided DBN

An Improved Power Quality Disturbance Detection Using Deep Learning Approach

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