Abstract
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.
Highlights
Due to the large-scale use of power electronic devices, there has been an increase in distributed power supply grid-connected non-linear loads
We propose to use a power quality disturbance recognition method based on discrete wavelet transform (DWT) and a multilayer perceptron extreme learning machine
The hierarchical ELM (H-ELM) framework [20] is a multilayer perceptron extreme learning machine that consists of two independent phases: (1) an unsupervised hierarchical feature representation that automatically extracts features from the input data and the original input features are converted to a higher latitude representation, and (2) the supervised feature classification
Summary
Due to the large-scale use of power electronic devices, there has been an increase in distributed power supply grid-connected non-linear loads. The classification study of power quality disturbance (PQD) is divided into three stages, feature extraction, feature selection [3] and classifier design. Most existing studies have aimed to optimize the classifiers and feature extraction, but have lacked consideration of the actual operating conditions of power quality disturbance data. The method is applied to the classification of power quality disturbances. We propose to use a power quality disturbance recognition method based on DWT and a multilayer perceptron extreme learning machine. The simulation results show that the method is more accurate than the traditional methods in classification accuracy Both speed and the ability to process big data have improved significantly.
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