State Grid Hunan Electric Power Limited Company Power Supply Service CenterMetrology Center
In order to improve the accuracy of power quality disturbance recognition and make up for the shortcomings of traditional single feature quantity pattern recognition methods that are easily disturbed and low precision, a power quality disturbance pattern recognition method based on fuzzy cluster analysis is proposed. The HHT transform is used to extract the corresponding disturbance characteristic quantities from a variety of different types of power quality disturbance signals, and then the extracted characteristic quantities are subjected to fuzzy cluster analysis to accurately classify these power quality disturbance signals one by one into photovoltaic disturbances and photovoltaic disturbances. There are two major categories of public grid disturbances, and a power quality disturbance identification process based on fuzzy clustering analysis is established at the same time. The simulation results show that this method overcomes the limitations of the traditional single-feature pattern recognition method, optimizes the recognition effect of disturbance signals, improves the recognition efficiency, and has high recognition accuracy and strong anti-noise ability.