An efficient algorithm for atomic decomposition of power quality disturbance signals using convolutional neural network

2022 ◽  
Vol 206 ◽  
pp. 107790
Author(s):  
Yang Han ◽  
Yingjun Feng ◽  
Ping Yang ◽  
Lin Xu ◽  
Amr S. Zalhaf
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Power Quality ◽  
Efficient Algorithm ◽  
Atomic Decomposition ◽  
Power Quality Disturbance

scholarly journals Classifying Power Quality Disturbances Based on Phase Space Reconstruction and a Convolutional Neural Network

2019 ◽  
Vol 9 (18) ◽  
pp. 3681 ◽  
Author(s):  
Kewei Cai ◽  
Taoping Hu ◽  
Wenping Cao ◽  
Guofeng Li
Keyword(s):  
Neural Network ◽  
Phase Space ◽  
Convolutional Neural Network ◽  
Power Quality ◽  
Hybrid Approach ◽  
Phase Space Reconstruction ◽  
Feature Maps ◽  
Image File ◽  
Mode Decomposition ◽  
Power Quality Disturbance

This paper presents a hybrid approach combining phase space reconstruction (PSR) with a convolutional neural network (CNN) for power quality disturbance (PQD) classification. Firstly, a PSR technique is developed to transform a 1D voltage disturbance signal into a 2D image file. Then, a CNN model is developed for the image classification. The feature maps are extracted automatically from the image file and different patterns are derived from variables in CNN. A set of synthetic signals, as well as operational measurements, are used to validate the proposed method. Moreover, the test results are also compared with existing methods, including empirical mode decomposition (EMD) with balanced neural tree (BNT), S-transform (ST) with neural network (NN) and decision tree (DT), hybrid ST with DT, adaptive linear neuron (ADALINE) with feedforward neural network (FFNN), and variational mode decomposition (VMD) with deep stochastic configuration network (DSCN). Based on deep learning algorithms, the proposed method is capable of providing more accurate results without any human intervention for PQDs. It also enables the planning of PQ remedy actions.

scholarly journals Automatic Detection of Power Quality Disturbance Using Convolutional Neural Network Structure with Gated Recurrent Unit

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Enes Yiğit ◽  
Umut Özkaya ◽  
Şaban Öztürk ◽  
Dilbag Singh ◽  
Hassène Gritli
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Power Quality ◽  
State Of The Art ◽  
Current State ◽  
Frequency Domains ◽  
Power Quality Disturbance ◽  
Neural Network Structure ◽  
Short Time ◽  
Gated Recurrent Unit

Power quality disturbance (PQD) is essential for devices consuming electricity and meeting today’s energy trends. This study contains an effective artificial intelligence (AI) framework for analyzing single or composite defects in power quality. A convolutional neural network (CNN) architecture, which has an output powered by a gated recurrent unit (GRU), is designed for this purpose. The proposed framework first obtains a matrix using a short-time Fourier transform (STFT) of PQD signals. This matrix contains the representation of the signal in the time and frequency domains, suitable for CNN input. Features are automatically extracted from these matrices using the proposed CNN architecture without preprocessing. These features are classified using the GRU. The performance of the proposed framework is tested using a dataset containing a total of seven single and composite defects. The amount of noise in these examples varies between 20 and 50 dB. The performance of the proposed method is higher than current state-of-the-art methods. The proposed method obtained 98.44% ACC, 98.45% SEN, 99.74% SPE, 98.45% PRE, 98.45% F1-score, 98.19% MCC, and 93.64% kappa metric. A novel power quality disturbance (PQD) system has been proposed, and its application has been represented in our study. The proposed system could be used in the industry and factory.

scholarly journals Power Quality Disturbance Identification and Optimization Based on Machine Learning

2021 ◽  
Author(s):  
Fei Long ◽  
Fen Liu ◽  
Xiangli Peng ◽  
Zheng Yu ◽  
Huan Xu ◽  
...  
Keyword(s):  
Neural Network ◽  
Power Quality ◽  
Classification Method ◽  
Neural Network Training ◽  
Training Set ◽  
Network Training ◽  
The Neural Network ◽  
Recognition Ability ◽  
Power Quality Disturbance ◽  
Depth Learning

In order to improve the electrical quality disturbance recognition ability of the neural network, this paper studies a depth learning-based power quality disturbance recognition and classification method: constructing a power quality perturbation model, generating training set; construct depth neural network; profit training set to depth neural network training; verify the performance of the depth neural network; the results show that the training set is randomly added 20DB-50DB noise, even in the most serious 20dB noise conditions, it can reach more than 99% identification, this is a tradition. The method is impossible to implement. Conclusion: the deepest learning-based power quality disturbance identification and classification method overcomes the disadvantage of the selection steps of artificial characteristics, poor robustness, which is beneficial to more accurately and quickly discover the category of power quality issues.

C213 Automatic Detection-Localization of Fault Point on Waveform and Classification of Power Quality Disturbance Waveshape Fault Using Wavelet and Neural Network

2003 ◽  
Vol 2003.2 (0) ◽  
pp. _2-241_-_2-246_
Author(s):  
Dilokratanatrakool Choosak ◽  
Navaratana Na Ayudhya Piyasawat ◽  
Chayavanich Tasanee ◽  
Prapanavarat Cherdchai
Keyword(s):  
Neural Network ◽  
Power Quality ◽  
Automatic Detection ◽  
Power Quality Disturbance
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