Fault Detection and Recognition Method of Power System Components Based on Morphological Singular Entropy

Abstract

The invention discloses a method for detecting and identifying an electric system element fault based on form singular entropy. The method comprises the following steps that a three-phase voltage signal and a direct current offset zero sequence voltage signal are respectively used as input of a multi-scale morphological filter and four corresponding feature matrixes are constructed by output of the morphological filter; each feature matrix is decomposed in sequence according to the singular value decomposition technology, each set of singular values are obtained, and a large singular value is screened from each set of singular values; the singular value obtained by screening is calculated so that the corresponding morphological singular entropy can be obtained; a fault classification indicator corresponding to each signal is calculated, the fault classification indicator corresponding to each signal is compared with a preset threshold value in sequence, and whether the fault exists or not is detected; if the fault exists, a fault phase is identified and whether the fault is a ground fault or not is judged. According to the method, the mathematical morphology theory, the singular value decomposition theory and the information entropy theory are combined, fault detection and identification are conducted in a short-time window, response is fast, and the calculated amount is small.

Description

technical field [0001] The invention relates to a method for detecting and identifying faults of power system components, in particular to a method for detecting and identifying faults of power system components based on morphological singular entropy, which belongs to the technical field of power system automation. Background technique [0002] When a power system component fails, the faulty component should be removed as soon as possible to ensure the safe operation of the power system. Fast and correct detection and identification of faults in the system is the key to solving the problem. The traditional fault detection and identification method is based on steady-state power frequency. Its principle is clear and has passed the long-term test of system operation. However, in recent years, the power system has developed very rapidly, and the operating conditions of the system have become very complex, which has brought new problems and challenges to the traditional fault ...

AI Technical Summary

Problems solved by technology

In the analysis and application of fault transient signals, a key problem is how to effectively and reliably extract fault characteristic information contained in fault transient signals. On the one hand, since fault transient signals are usually mixed in steady-state quantities, Moreover, due to low energy and small amplitude, it is often easily overwhelmed by steady-state quantities and system noise; on the other hand, because the fault information contained in fault transient signals is often massive and irregular data information, it is difficult to directly achieve Purpose of Fault Identification Study

These two reasons have caused difficulties in the effective extraction of fault signal feature information.

In the published patents and literatures, many scholars have also conducted extensive research on how to improve the performance of detection and recognition methods, and proposed many methods, which have also achieved certain results. Defects such as slow response speed and low reliability

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