Research on the Risk Early Warning Model for Hydroelectric Power Plant Field Operations Based on Reverse Learning.

To ensure the safety of on-site operations in hydropower plants, this paper studies a reverse learning based risk warning model for on-site operations in hydropower plants. It selects indicators from four types of risks: public risk, unit equipment system equipment risk, electrical system equipment risk, and hydraulic construction risk to construct a risk warning indicator system for on-site operation of hydropower plants. The sensor system based on reverse learning is adopted, and the Linear List composed of sensor input sample points and expected output value is used to retrieve the position of measurement points so as to obtain accurate risk early warning index data. LSTM neural network is used to construct a risk warning model, the warning indicator data is used as the input of the model, and the risk warning level is used as the output of the model. Reverse output comparison is implemented through the propagation path to optimize different weight matrices and deviations, thereby obtaining the risk warning results of on-site operations in hydropower plants. At the same time, the particle swarm optimization algorithm based on reverse learning behavior is used to optimize the parameters of the early warning model and improve the accuracy of the model s early warning. The experimental results show that the model can accurately predict the risks of on-site operations in hydropower plants and ensure the safety of on-site operations. [ABSTRACT FROM AUTHOR]