Analysis of power plant outage event results based on SOM clustering

Abstract

Through the file mining analysis of fault event analysis reports, it is possible to discover the fault mode characteristics that exist between different fault events, thereby providing empirical guidance for safety management. The self-organizing mapping SOM clustering method was used to analyze the fault cause characteristics of 520 unplanned shutdown events in power plants. The main influencing factors are divided into six primary indicators: external factors, technical factors, equipment failures, pipeline failures, logical factors, and human factors, and 36 secondary indicators of influencing factors. Based on different indicator characteristics, the power plant shutdown event can be mapped into a two-dimensional fault feature event map, which can intuitively obtain the visualization results of shutdown events with different influencing factor dimensions. Through SOM clustering analysis, the shutdown fault events were ultimately divided into three different clusters. In cluster 1, the highest proportion is ultimately caused by external influencing factors and equipment failures. In cluster 2, it is mainly affected by human factors and control logic factors. In cluster 3, it is mainly the pipeline aging failure caused by long-term running time factors. By visualizing the results of intuitive fault characteristics, it can provide diversified knowledge information for risk management and technical personnel to take corresponding measures to improve the safe and stable operation of the unit.