Distributed Photovoltaic Power Station Power Short-term Forecast Method Research

As fossil energy continues to deplete and the ecological environment is continuously destroyed,photovoltaic power generation,an emerging power generation technology,is increasingly favored by people,and photovoltaic power generation systems will become the mainstream industry for power grid generation in China in the future.As the weather changes and becomes impermanent,the photovoltaic system's power generation output has great uncertainty.If it is merged into the large power grid,it will bring immeasurable impact to the power grid,and bring the normal and safe operation of the power system.Certain hidden dangers.Therefore,in order to achieve safer and more stable operation of power systems and coordinated development of power supply and distribution,accurate prediction of power generation and power generation becomes critical.This paper relies on a photovoltaic demonstration power station in Shenyang City,Liaoning Province,to analyze and study the monitoring data of photovoltaic power plants to achieve accurate prediction of photovoltaic power station,which provides a sustainable forward development for the photovoltaic industry and better power supply for the future power generation industry.Certain theoretical support and technical support.This article will conduct a major analysis of the two modules:(1)Research the main components of the PV test platform,which mainly research the arrangement of the PV sequence and the installation of the inverter,and the monitoring system composition and data collection of the PV demonstration power station in Shenyang,Liaoning Province.The electric data and meteorological data collected by the PV monitoring system were preprocessed,and the demonstration power station was taken as an example to analyze the different influencing factors of the four seasons,including solar radiation,sunshine hours and daily maximum temperature and photovoltaic power.Correlation degree,correlation coefficient is 0.902,0708 and 0.364.And based on this,the quantitative analysis of the disturbance degree of power generation by different weather types is carried out.The spring is disturbed most in rainy weather,and the power generation disturbance is the largest in summer under cloudy/cloudy weather,while the cloudy/sunny weather produces electricity in autumn and winter.The power disturbance is the largest.(2)This paper constructs three short-term prediction models of power generation.The first two prediction models use random forest prediction method and grey-Markov chain combination prediction method.These two methods are designed directly for the power generation law of photovoltaic power generation.The prediction model is based on thephotovoltaic demonstration power station as an example.The input variable is the historical power generation data of the photovoltaic power station.The evaluation model is implemented by two model evaluation criteria.The third prediction model uses the similar day algorithm and the IGA-BP combination method.The eigenvectors are constructed by the similarity construction principle.On this basis,the IGA-BP algorithm is used to construct the power short-term prediction model.Finally,the prediction results of the three prediction algorithms are evaluated and analyzed,and the advantages of the three prediction methods are evaluated.What kind of occasion.The prediction results show that the three prediction methods can show strong feasibility and effectiveness in the field of photovoltaic power generation power prediction,and each prediction method has its own different advantages,and the similar day algorithm and IGA-BP algorithm combined with prediction method prediction The best results.