Detection and classification of power quality events using empirical wavelet transform and error minimised extreme learning machine

2019 ◽  
Vol 10 (4) ◽  
pp. 452 ◽  
Author(s):  
Mrutyunjaya Sahani
Keyword(s):  
Wavelet Transform ◽  
Power Quality ◽  
Extreme Learning Machine ◽  
Empirical Wavelet Transform ◽  
Learning Machine

scholarly journals Power Quality Disturbance Classification Based on DWT and Multilayer Perceptron Extreme Learning Machine

2019 ◽  
Vol 9 (11) ◽  
pp. 2315 ◽  
Author(s):  
Jidong Wang ◽  
Zhilin Xu ◽  
Yanbo Che
Keyword(s):  
Wavelet Transform ◽  
Power Quality ◽  
Extreme Learning Machine ◽  
Fine Tuning ◽  
Current Layer ◽  
Data Imbalance ◽  
Empirical Wavelet Transform ◽  
Power Quality Disturbance ◽  
Learning Machine ◽  
The Impact

In order to effectively identify complex power quality disturbances, a power quality disturbance classification method based on empirical wavelet transform and a multi-layer perceptron extreme learning machine (ELM) is proposed. The model uses the discrete wavelet transform (DWT) multi-resolution method to extract classification features. Combined with hierarchical ELM (H-ELM) characteristics, the particle swarm optimization (PSO) single-object feature selection method is used to select the optimal feature set. The hidden layer of the H-ELM classifier in the model is trained by forward training. Once the previous layer is established, the weight of the current layer can be fixed without fine-tuning. Therefore, the training speed can be accelerated, the recognition accuracy is almost independent of the parameter adjustment, and the model has strong robustness. In order to solve the problem of data imbalance in the actual power system, a data enhancement method is proposed to reduce the impact of data imbalance and enhance the generalization performance of the network. The simulation results showed that the proposed method can identify 16 disturbances efficiently and accurately under different noise conditions, and the robustness of the proposed method is verified by the measured data.

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