Research on substation monitoring information eventisation based on artificial intelligence and BP neural network

The power grid and dependable power supply are both essential for current society's functioning, and the substation is a vital part of the power grating. A substation monitoring system may track the substation's operational state in real-time, identify issues as they arise, and resolve them to protect the stability and dependability of the electrical grid.However, with the quantification and complexity of substation operation data, how to quickly and accurately analyse the monitoring data has become a challenge.The low efficiency of information processing and the challenge of centrally monitoring several sites and multiple pieces of equipment in substations are addressed in this research using an upgraded BP neural network technique. The algorithm is used in combination with the L-M fast learning method to achieve event-based processing of substation equipment information. The image processing technology is used to perform artificial intelligence visual scanning of the eventized monitoring results, and by taking real-time photos of the monitoring centre, key information is extracted, and different event results are output and displayed to help personnel quickly obtain information. The procedure time for a single incident is decreased by an syandard of 2.968 seconds when compared to the substation's flow check techniques.