Identification method for GIS local discharge mode

Abstract

The invention discloses an identification method of a GIS local discharge mode. The method comprises the following steps: identifying the local discharge mode by using a probability neural network, and performing dimension reduction processing on a high-dimension sample by using a linear judgment method; mapping a high-dimension sample to the optimal authentication vector space, classifying and compressing the sample dimension, wherein various mapped samples have the maximum inter-class distance and the minimum intra-class distance in the new sample space, and the identification effect can bewell improved; furthermore, the smooth factor of the probability neural network is optimized by using a genetic algorithm, the sample processing speed of the local discharge mode of the GIS is greatlyimproved, and the identification effect on the GIS local discharge mode by the probability neural network is greatly improved. The method disclosed by the invention has good promotion and use value.

Description

technical field [0001] The invention belongs to the field of pattern recognition, and in particular relates to a recognition method for GIS partial discharge patterns. Background technique [0002] In power systems, gas insulated switchgear (GIS) has the advantages of high reliability in safe operation, low cost and economy, small footprint, low pollution and long service life, and has been widely used. However, in the process of production, transportation and installation of GIS equipment, some internal defects cannot be avoided. In addition, the long-term operation of the equipment will lead to the deterioration of the internal insulation, which will cause partial discharge (PD) behavior of the equipment. If the PD behavior of GIS cannot be monitored and rectified in time, it will have a serious impact on the stable operation of the power grid. Detection, analysis and identification of PD signals of various insulation defects inside GIS equipment, and timely discovery of ...

AI Technical Summary

Problems solved by technology

At present, researchers have made great progress in solving the "dimension disaster" of data. At present, there are plans to reduce the dimension by introducing Principal Component Analysis (PCA) for 26 statistical parameters extracted from UHF signals. After processing, a better recognition effect is obtained, but there is still some ambiguity in the inter-class data after dimensionality reduction; in addition, the method of Locally linear embedding (LLE) can also be used for dimensionality reduction. Complex, and the dimensionality reduction effect will be affected by parameters

Images