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

scholarly journals Classification of Power Quality Disturbance Based on S-Transform and Convolution Neural Network

2021 ◽  
Vol 9 ◽  
Author(s):  
Jinsong Li ◽  
Hao Liu ◽  
Dengke Wang ◽  
Tianshu Bi
Keyword(s):  
Neural Network ◽  
Power Quality ◽  
Noise Immunity ◽  
Signal Classification ◽  
Classification Method ◽  
Classification Methods ◽  
Time Frequency ◽  
S Transform ◽  
Power Quality Disturbance

The accurate classification of power quality disturbance (PQD) signals is of great significance for the establishment of a real-time monitoring system of modern power grids, ensuring the safe and stable operation of the power system and ensuring the electricity safety of users. Traditional power quality disturbance signal classification methods are susceptible to noise interference, feature selection, etc. In order to further improve the accuracy of power quality disturbance signal classification methods, this paper proposes a power quality disturbance classification method based on S-transform and Convolutional Neural Network (CNN). Firstly, S-transform is used to extract disturbance signals to obtain the time-frequency matrix with characteristics of the disturbance signals. As an extension of wavelet transform and Fourier transform, S-transform can avoid the disadvantages of difficult window function selection and fixed window width. At the same time, the feature extracted by S-transform has better noise immunity. Secondly, CNN is used to perform secondary feature extraction on the obtained high-dimensional time-frequency modulus matrix to reduce data dimensions and obtain the main features of the disturbance signal, then the main features extracted are classified by using the SoftMax classifier. Finally, after a series of simulation experiments, the results show that the proposed algorithm can accurately classify single disturbance signals with different signal-to-noise ratios and composite disturbance signals composed of single disturbance signals, and it also has good noise immunity. Compared with other classification methods, the algorithm proposed in this paper has better timeliness and higher accuracy, and it is an efficient and feasible power quality disturbance signal classification method.

scholarly journals A new protection scheme for PQ events prediction and classification in distribution system: A SSO-ANN strategy

2019 ◽  
Vol 52 (5-6) ◽  
pp. 449-461 ◽  
Author(s):  
K Karthikumar ◽  
V Senthil Kumar ◽  
M Karuppiah
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Power Quality ◽  
Distribution System ◽  
Swarm Optimization ◽  
Protection Scheme ◽  
Event Prediction ◽  
Power Quality Disturbances ◽  
Artificial Neural

Increased utilization of nonlinear loads and fault event on the power system have resulted in a decline in the quality of power provided to the customers. It is fundamental to recognize and distinguish the power quality disturbances in the distribution system. To recognize and distinguish the power quality disturbances, the development of high protection schemes is required. This paper presents an optimal protection scheme for power quality event prediction and classification in the distribution system. The proposed protection scheme combines the performance of both the salp swarm optimization and artificial neural network. Here, artificial neural network is utilized in two phases with the objective function of prediction and classification of the power quality events. The first phase is utilized for recognizing the healthy or unhealthy condition of the system under various situations. Artificial neural network is utilized to perceive the system signal’s healthy or unhealthy condition under different circumstances. In the second phase, artificial neural network performs the classification of the unhealthy signals to recognize the right power quality event for assurance. In this phase, the artificial neural network learning method is enhanced by utilizing salp swarm optimization based on the minimum error objective function. The proposed method performs an assessment procedure to secure the system and classify the optimal power quality event which occurs in the distribution system. At that point, the proposed work is executed in the MATLAB/Simulink platform and the performance of the proposed system is compared with different existing techniques like Multiple Signal Classification-Artificial Neural Network (MUSIC-ANN), and Genetic Algorithm - Artificial Neural Network (GA-ANN). The comparison results demonstrate the superiority of the SSO-ANN technique and confirm its potential to power quality event prediction and classification.

New Research on Identification and Classification of Grid Power Quality Disturbances

2014 ◽  
Vol 986-987 ◽  
pp. 1431-1434
Author(s):  
Ning Xia Yang ◽  
Mao Fa Gong ◽  
Xiao Fei Wang ◽  
Hui Ting Ge ◽  
Yu Qing Lin ◽  
...  
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Wavelet Transform ◽  
Power Quality ◽  
Particle Swarm ◽  
Interference Signal ◽  
Swarm Optimization ◽  
Frequency Information ◽  
Power Quality Disturbances

To improve accuracy and speed of recognising and classifying grid power quality disturbances, this paper presents a new method which combines complex wavelet transform and particle swarm optimization (PSO) neural network to identify and classify the disturbance . This method extract both amplitude-frequency and phase frequency information of the interference signal to make up for the lack of traditional wavelet transform which only extract the amplitude-frequency information. And on this basis, using particle swarm optimization, we seek the optimal solution of neural network weights and thresholds for the identification and classification of power quality. The MATLAB simulation result has verified the accuracy and rapidity of this method compared with the traditional method .

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