In order to improve the reliability of the operation status detection for substation equipment and standardize its opera-tion,based on the application of improved non-dominated sorting genetic algorithm,this paper takes a certain intelligent substation as an example to carry out the design and research on its status detection method.Sensors are arranged to collect operational data from power equipment,and a variational mode decomposition method is introduced to denoise and reduce dimensionality of this data.Introducing genetic algorithms,by iterating between knowledge domains and multiple objectives,finding the optimal balance point,selecting the most effective feature combination,and achieving knowledge set generation and feature extraction from power equipment operation data in substations.Introducing deep transfer learning,constructing and training a self-organizing map(SOM)network consisting of multiple neural nodes,adaptively clustering input features,and achieving online management and a-nomaly detection of intelligent substation equipment.Comparative experimental results show that this method can accurately iden-tify abnormal states in power equipment during operation,thereby enabling intelligent management of substations.
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