Ultra-high-frequency partial discharge signal identification method for gas insulated switchgear (GIS)

Abstract

The invention discloses an ultra-high-frequency partial discharge signal identification method for gas insulated switchgear (GIS). The method comprises a model training process and a defect identification process, and specifically comprises the following steps of: reprocessing partial discharge signals of the GIS; extracting discharge characteristic parameters such as average discharge amplitude, discharge amplitude standard deviation, discharge phase distribution, discharge polarity, discharge time interval mean, discharge time interval standard deviation; optimizing a weight and a threshold value of a back propagation (BP) neural network by utilizing a genetic simulated annealing tool; training samples by utilizing a BP neural network tool; establishing a corresponding gas statistic algorithm (GSA)-BP model; preprocessing the partial discharge signals to be identified of the GIS; and identifying the samples to be measured in a classified way according to the GSA-BP model after extracting the corresponding characteristic parameters. By the method, the efficiency and the accuracy of partial discharge fault diagnosis of the GIS are improved effectively; and the method is critical to evaluate the insulation state of the GIS and formulate a reasonable maintenance strategy.

Description

technical field [0001] The invention relates to the technical field of electrical equipment insulation detection, in particular to a GIS ultra-high frequency partial discharge signal recognition method based on a BP neural network of a genetic simulated annealing algorithm (GSA). Background technique [0002] With the rapid development of my country's power industry construction, the modern power system is developing in the direction of large power grids, large units, ultra-high voltage, and large capacity. In order to ensure the stability and reliability of the power system, more safety requirements for power equipment are also proposed high demands. As one of the most important equipment in the substation, the enclosed combined electrical equipment (GIS) is widely used in the field of high-voltage power transmission due to its high reliability and small footprint. The impact and loss are huge. So before GIS breaks down, it is especially important to detect and judge its i...

AI Technical Summary

Problems solved by technology

At present, the BP neural network is the most widely used neural network in practical applications. However, due to the gradient descent method used in the algorithm of the BP neural network, there are inevitably problems such as long training time, slow convergence speed, and easy to fall into local minimum values. It is reliable. low accuracy

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