Electric energy quality analysis method based on variational mode decomposition multi-scale permutation entropy

Abstract

The invention discloses an electric energy quality analysis method based on variational mode decomposition multi-scale permutation entropy. The method comprises the steps of collecting original training data of electric energy quality monitoring points when the electric energy quality monitoring points are disturbed by different types; adopting variational mode decomposition to decompose the components to obtain K IMF components; calculating the multi-scale permutation entropy of each IMF component, and constructing a feature vector of the original training data; selecting R features from thefeature vectors to form an optimized feature vector of the original training data; taking the optimized feature vector of the original training data and the corresponding disturbance type as input data and output data respectively, and training an ELM neural network model to obtain an electric energy quality disturbance classifier; and acquiring optimized feature vectors of voltage signals of to-be-detected power quality monitoring points according to same method; inputting the optimized feature vectors into the power quality disturbance classifier to obtain the disturbance type of the power quality monitoring point to be detected. According to the method, the real-time diagnosis efficiency of the disturbance type of the power quality is greatly improved.

Description

technical field [0001] The invention belongs to the field of power quality detection, and in particular relates to a power quality analysis method based on variational mode decomposition multi-scale permutation entropy. Background technique [0002] With the development of science and technology, the change of power load and the increase of non-linear load, the power grid is often affected by various disturbances, which leads to waveform distortion and power quality problems become increasingly prominent. Power quality problems such as voltage swells, sags, harmonics, attenuation oscillations, and voltage flicker in the power grid have attracted widespread attention from the power sector and grid users. Power quality is a description of the quality of power in the power system, and it is used to measure the quality of power. Generally speaking, the timing waveform of electric energy should be a stable sine wave with stable amplitude and consistent frequency, but due to the ...

AI Technical Summary

Problems solved by technology

At present, the mainstream feature quantity extraction and analysis methods mainly include: mathematical morphology algorithm, Fourier transform, wavelet transform, S transform, EMD decomposition, etc. A series of studies have shown that the feature quantities extracted by these extraction methods have great influence on power quality disturbances. Certainly representative, but also sensitive to noise, lack of adaptability, complex calculation process, low efficiency, serious endpoint effect, serious modal aliasing of decomposition results, low classification accuracy when extracting features for classification, etc.

[0004] On the other hand, the classification methods used in existing research are mainly template matching or various traditional neural networks, which lack optimization. When there are too many input features, the complexity of the recognition model is too high, and the efficiency of the classifier is low.

At the same time, the existing research is difficult to achieve real-time online measurement of power quality, and the classifier to make corresponding adjustments according to different target environments

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