Determination the Switching State of Compensatory Equipment Based on Monitor Data Analysis

Abstract

The power supply companies have codes to limit the power quality disturbance from the disturbance source, for example, wind park or arc furnace. To equip the power quality compensatory equipment is the straight way to limit it. However, it is difficult for power supply company to manage the power quality compensatory equipment on the user side. This paper presents an algorithm, based on power quality monitoring data on the grid side, to determine the operation status of the compensatory equipment on the user side for power grid companies. In this paper, the probabilistic neural network is used to classify the operation status of the compensatory equipment, by taking the voltage deviation, three-phase voltage unbalance, total harmonic distortion rate and long-term voltage flicker as the input data of the network, so as to realize the determination of the operation status of the compensatory equipment. The correctness and reliability of the proposed method is verified by the recorded power quality data from a substation in Anhui Power Grid in China.