scholarly journals Power Quality Disturbance Classification Based on A Novel Fourier Neural Network and Hyperbolic S-transform

2016 ◽  
Vol 9 (1) ◽  
pp. 111-124
Author(s):  
Lin Lin ◽  
Xiaohuan Wu ◽  
Jiajin Qi ◽  
Hongxin Ci
Keyword(s):  
Neural Network ◽  
Power Quality ◽  
S Transform ◽  
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.

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