Disturbance classification method based on EWT-MPE-PSO-BP

Abstract

The invention relates to a power quality disturbance classification method based on an EWT-MPE-PSO-BP neural network, and the method comprises the steps: carrying out the accurate mode decomposition with anti-noise performance of different types of disturbance signals through EWT, and obtaining the mode components of different frequencies; then, introducing a time scale concept, and optimizing the traditional permutation entropy so as to be better suitable for a complex system problem; thirdly, introducing PSO to optimize the BP neural network, and converting the problem of searching for the minimum error in BP into the problem of searching for the optimal position of PSO, so that the defect that the convergence speed in the BP neural network is low is overcome, and the working efficiency of the BP network is improved; and finally, taking the extracted characteristic quantity as the input of the optimized neural network, and obtaining a final power quality disturbance classification result through multiple times of training. The method provided by the invention effectively solves the problems of inaccurate disturbance signal detection and low classification process speed, and is high in accuracy and high in working efficiency.

Description

technical field [0001] The invention relates to the field of power quality analysis and control, in particular to the field of power quality disturbance classification based on feature extraction and pattern recognition. Background technique [0002] As my country's requirements for power systems are getting higher and higher, not only requires high stability of power load supply, but also requires high reliability of power quality. The power quality in the power system shows different characteristics due to the different working characteristics of power equipment, the time and location of faults, etc., resulting in different types of power quality disturbances. Common power quality disturbances mainly include harmonics, voltage unbalance, voltage fluctuation and flicker, transient pulse and oscillation, voltage swell, sag, interruption, etc. Comprehensive and in-depth analysis of power quality problems, rapid and accurate identification of power quality disturbance types, ...

AI Technical Summary

Problems solved by technology

Among them, when the decision tree deals with problems with many levels, the attribute weights are unreliable, the SVM kernel function selection problem is complicated, the neural network is easy to fall into the problem of local optimal solution of the algorithm, the training time is long, and there is an over-fitting problem

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