Short-time Power Forecast Of Photovoltaic Power System Based On PSO-BP Neural Network

With the rapid development of society,users' demand for electricity continues to grow,and clean renewable energy solar power generation has attracted more and more attention.However,the disadvantages of solar power generation are also very obvious,such as the volatility and intermittency of photovoltaic power generation output,which will cause a huge impact on the main network and is not conducive to maintaining the stability of power grid operation.Accurate photovoltaic power prediction network is an important prerequisite for effective mitigation of adverse effects.So it has very important practical significance and application value on the prediction of photovoltaic power generation system's generation.Firstly,the working principle of power generation photovoltaic cells are elaborated;Secondly,the paper introduces the classification of photovoltaic power generation system and a variety of basic components of photovoltaic power generation system.Combined with the established solar cell model,the factors(solar irradiance,temperature,and weather type)affecting the output power of the photovoltaic power station were studied.of the photovoltaic power station were studied.Then,the output power prediction model of photovoltaic power station based on BP neural network is established,and the specific network topology,learning process and number of neurons in each layer of the network are described in detail.each layer of the network are described in detail.In the process of using BP prediction model to predict,it is found that BP neural network has disadvantages of multiple iterations and long convergence time.In order to overcome these disadvantages,this paper proposes to use PSO genetic algorithm to optimize the weight and threshold of BP neural network,and establishes PSO-BP prediction model.Then analyze the data processing of PV system power generation forecast.Finally,using weather parameters and historical data of photovoltaic power generation as samples,the prediction effects of the BP neural network prediction model and the PSO-BP neural network prediction model were compared under different weather types.Secondly,it is a task to analysis the data processing of photovoltaic power plant.Based on the prediction results of the above two prediction models,simulation analysis and error evaluation are carried out.The prediction results show that the prediction error effects of both models are in a good range under sunny and cloudy weather.This result proves that the BP neural network and PSO-BP neural network established in this paper are used for short-term prediction of photovoltaic power generation under some typical weather types.And they have certain accuracy and practicality.