Research on quality problems management of electric power equipment based on knowledge–data fusion method

The quality of electric power equipment directly affects and decides the securable and stable operation of the power grid. Revealing the quality problem causes and influencing factors are considered as the key point to improve and guarantee the quality of power apparatus. However, the data of power supplies quality problems have the features of diversity and complexity. It is of great value to take the full advantages of the multi‐source heterogeneous data, especially the possess flow information tracing time and space, to locate vulnerable processes, problem causes and influencing factors. This study put forward a data source system, which includes general information, quality problem information, process flow information and other supplementary information. Furthermore, an improved neural network model utilising knowledge–data fusion method is proposed. In this way, the efficiency and accuracy of analysis for quality problems is available and enhanced. To verify the validity of the knowledge–data fusion model, a case study with 2084 sample data of gas‐insulated switchgear is carried out, proving help to strengthen the management and control measures of power equipment quality problems.