Risk Assessment of Tower Transmission Based on Insulator Online Monitoring

Insulators play a vital role in ensuring the safe and reliable operation of transmission lines. In order to accurately judge the deterioration state of insulators in real-time, predict their remaining life, and then evaluate the transmission risk of towers, this paper proposes a regression neural network life prediction model using the Adam optimization algorithm based on terahertz wave micro-crack online monitoring technology. The model uses a powerful self-learning ability to continuously adjust the parameters of the network model. Through the adjusted model, the data of the insulator deterioration state are extracted and compared layer by layer, and the online prediction of the insulator’s remaining life in operation is completed. Finally, the model is applied to insulator strings and towers to realize online risk assessment, and an example is designed to verify the effectiveness and feasibility of the model.