Deep learning-based power grid failure detection method

Abstract

The invention discloses a deep learning-based power grid failure detection method, which comprises the following steps: (1) common point voltage signals are subjected to multi-resolution empirical mode decomposition; (2) phase space reconstruction is carried out by using the decomposition signals in the first step, each layer of phase space matrix is subjected to singular spectral decomposition toobtain a singular value of each layer of phase space matrix, the singular spectral entropy of each layer of phase space is extracted according to the singular value of each layer, multiple layers ofsingular spectral entropy are combined to form a depth neural network parameter feature vector of the multi-resolution singular spectral entropy; and (3) the feature vector in the second step is directly substituted into the depth neural network model for power grid failure detection. The feature vector extracted by the method has stability for similar signals and the same kind of samples, and plays a good role in distinguishing failure and disturbance states, reducing misjudgment in a disturbance state and avoiding malfunction of a photovoltaic system.

Description

technical field [0001] The invention relates to a construction and detection method of a deep learning power grid fault model considering empirical mode decomposition, in particular to a power grid fault detection method based on multi-resolution singular spectrum entropy and deep learning. Background technique [0002] In a power grid with a large number of distributed power sources, faults caused by other reasons such as line faults will cause great harm to the safe operation of the power grid. As the penetration rate of distributed power continues to rise, fault detection is facing unprecedented challenges: renewable power brings non-negligible uncertainty and noise to grid operation, aggravating the intensity of disturbances, and if these disturbances are misidentified as faults In this situation, the distributed power supply will be forced to cut off and run, which will bring huge harm. Therefore, a grid abnormal state detection method with high recognition accuracy is...

AI Technical Summary

Problems solved by technology

[0006] This method provides a construction and detection method of a power grid fault model based on multi-resolution singular spectral entropy and deep learning, and its purpose is to solve the problem of low precision existing in previous methods

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