Particle Swarm Optimization for Thermal Power Plant Safety Evaluation

Abstract

Electric power industry is a basic industry of national economy, the power plant production safety related to people's life safety and property of the state, the power of reform and social stability, safety evaluation of power plant is an important guarantee of safety production in thermal power plant.The paper mainly improves the traditional BP neural network algorithm through particle swarm optimization algorithm, i.e. PSO, and establishes the model of BP neural network based on particle swarm optimization. By using PSO algorithm to optimize the BP neural network’s connection weight values and threshold values, it can give full play to the global optimization ability of the PSO and BP algorithm local search advantage as well as overcome the randomness problem of BP neural network weight values[1].Now the instance verification results show that the BP neural network based upon PSO applied into safety evaluation of thermal power plant, can achieve the purpose of accurate to evaluate the security situation of thermal power plants. The model has two advantages: the first is the convergence speed is very fast in the operation process and the second is the computation results have a higher precision.