Electrical energy power quality disturbance automatic identification method and system based on information fusion

Abstract

The invention is a automatic identification method and system based on the power quality disturbances of the information fusion, characterized by: collecting the transient and steady-state measurement datum associated with the power quality disturbances from the Power Quality Monitoring System and other automation systems, disposing of noise such as pretreatments; Using the method combining Fourier analysis, small wave multi-resolution decomposition and analysis of the correlation functions, distilling the from the disturbance datum, establishing the disturbance eigenvector, and as a the input characteristic vector of three probabilistic neural networks, realizing the mapping from a feature space to the disturbance space; the output of three probabilistic neural networks regarded as the evidence body of independent of each other, realizing Information Fusion by the use of D-S evidence theory, obtaining recognition results. This invention through the correct selection and extraction of disturbance eigenvectors, can input neural network parallel in classification and reflect the disturbance situation from the various aspects, thus effectively enhancing the correct identification rate of disturbance, a first step in Intelligent Recognition of the power quality disturbances.

Description

technical field [0001] The invention belongs to the field of power system automation, in particular to an information fusion-based automatic identification method and system for power quality disturbances. Background technique [0002] The essence of power quality disturbance recognition is pattern recognition. At present, the basic idea of ​​this problem is to analyze the signal according to fast Fourier transform and wavelet transform or other time-frequency analysis methods, extract features, and combine pattern recognition methods such as neural network and fuzzy expert system. Automatic recognition of power quality disturbances. Most of the existing methods simply extract one feature quantity from the distorted time-domain waveform of the power quality monitoring system to identify power quality disturbances, and lack the collaborative processing and comprehensive utilization of multi-source and multi-dimensional information. Due to the limited disturbance feature quan...

AI Technical Summary

Problems solved by technology

Most of the existing methods simply extract one aspect of the feature quantity from the distorted time-domain waveform of the power quality monitoring system to identify power quality disturbances, and lack the collaborative processing and comprehensive utilization of multi-source and multi-dimensional information.

Due to the limited disturbance feature quantities used by this method, if these feature quantities are affected by various noises, the accuracy and reliability of disturbance identification may be reduced, and there are varying degrees of differences in accuracy, reliability, and practicability. defect

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