Power distribution network system's electric energy quality disturbance positioning and identifying method

Abstract

The invention discloses an electric energy quality disturbance positioning and identifying method based on the lifting of the wavelet and the improvement of a BP neural network. The method comprises the following steps: using the Euclidean decomposition algorithm to obtain a db4 wavelet lifting scheme; performing lifting wavelet decomposition to the disturbance signal; in combination with the modulus maxima, performing the positioning detection to the disturbance mutation point peak; and utilizing the method of combining the self-adaptive learning rate with the incremental momentum term to improve and carry out disturbance identification to the BP neural network. The method of the invention can better obtain the disturbance period information, achieves fast positioning and high precision, and overcomes the shortcoming that a traditional BP neural network is prone to fall into local minimal points and slow convergence speed, therefore, achieving a high identifying rate for the power distribution network system's electric energy quality disturbance.

Description

technical field [0001] The invention relates to a power quality disturbance location and identification method, in particular to a distribution network system power quality disturbance location and identification method based on lifting wavelet and improved BP neural network. Background technique [0002] In recent years, the wide application of power electronic equipment has made the problem of power quality disturbance in distribution network system increasingly prominent. Therefore, how to improve power quality has become a hot topic in related fields such as distribution network systems. More and more attention has been paid to the research and management of distribution network power quality disturbances, and fast and accurate positioning and identification of distribution network power quality disturbances is an important link and an important measure to evaluate and improve power quality. [0003] Extensive and in-depth research and discussion have been carried out o...

AI Technical Summary

Problems solved by technology

Due to the relatively fixed length and shape of the time window, the short-time Fourier transform cannot reflect the characteristics of high frequency and low frequency at the same time, which has limitations; the S-transform method is used to detect and classify power quality disturbances, and the detection and positioning accuracy is high, and the classification is relatively accurate , but the S-transform has a large amount of calculation, and it is difficult to guarantee real-time performance; the wavelet transform can better locate and identify power quality disturbances, but the traditional wavelet is used, the calculation speed is slow, and the positioning takes a long time; the generalized S-transform method To locate and identify power quality disturbances, the positioning accuracy and disturbance recognition rate are high, but the positioning method is complex and the amount of calculation is large

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