Power quality disturbance identification method using time-domain compression multiresolution fast S-transform feature extraction

Abstract

The invention provides a power quality disturbance identification method using time-domain compression multiresolution fast S-transform feature extraction. The power quality disturbance identificationmethod is applied to the analysis and research of power quality disturbance signals and is characterized by including the steps of simulating power quality disturbance signals, performing multi-resolution fast S-transform processing on the disturbance signals, on the premise of determining features, calculating the required reserved information for a fast Fourier transform inverse transform result for each major frequency point in the fast S-transform process, constructing an intermediate matrix, and extracting valid features from the intermediate matrix to construct a feature vector for classifier construction. The power quality disturbance identification method has the advantages of being scientific and reasonable, having strong applicability and a good effect, and being capable of completing the identification of complex power quality disturbance signals.

Description

Technical field [0001] The invention is a power quality disturbance identification method adopting time-domain compression multi-resolution fast S-transform (TCMFST) feature extraction, and is applied to the analysis and research of power quality disturbance signals. Background technique [0002] With the development of sustainable development and low-carbon concepts, the scale of new energy power generation continues to expand. The grid connection of new energy power generation and the application of a large number of power electronic equipment will affect the power quality (PQ) of the grid. In science, technology and industry With the rapid development of production, attention to power supply quality is increasing. Therefore, it is necessary to carry out in-depth monitoring and analysis of power quality. The massive power quality data collected by a large number of monitoring points puts forward higher requirements on the power quality signal classification system . At present...

AI Technical Summary

Problems solved by technology

However, in practical applications, when the signal sampling rate is too high and the signal is too long, the scale of the modulus-time-frequency matrix obtained by the fast S transform is still large, the space complexity is high, the data storage space is large, and the hardware requirements are high.

In terms of classifiers, traditional support vector machines, BP neural networks, and decision trees have strong robustness, but cannot meet the needs of efficient and accurate classification, and extreme learning machines, as an optimization algorithm for single hidden layer neural networks, overcome this shortcoming

However, since the input weights and hidden layer nodes of the extreme learning machine are randomly selected, there must be a series of non-optimal or unnecessary values. On the one hand, it will increase the response time of the extreme learning machine algorithm to the test data. It will also increase the complexity of the network structure

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