The Research on Fault Identification of Spatiotemporal Location With Compressed Grid Data Using the Stochastic Sliding Blocks

Abstract

To identify the compressed power grid fault data quickly and effectively, this paper presents a spatiotemporal location fault diagnosis method for the data compressed with set partitioning in hierarchical trees (SPIHT) algorithm. Firstly, the data from the multi-channel collector is constructed into a two-dimensional grey-scale digital image and compressed with the improved SPIHT algorithm. Then, the peak signal-to-noise ratio (PSNR) parameter of the compressed data is considered to identify the abnormal data. Finally, a random sliding block is used to scan and process each block of power grid data during the abnormal time range. The abnormal data block of power grid data is identified by analyzing the compression parameters of power grid data processed by each block. The node corresponding to the abnormal data block is found, and the power grid fault data is in space. Simulation and experimental results verify the correctness and effectiveness of the proposed method.