Distributed Photovoltaic Power Generation Forecasting And Monitoring Platform

The demand for energy consumption is growing rapidly with the rapid development of global science and economy,the drawbacks of traditional energy more and more prominent with the frequent occurrence of extreme weather and haze,people’s pursuit of low-carbon and new energy development and utilization of the desire is also more and more intense.In the trade-off between material interests and energy conservation,solar energy is particularly prominent in the field of new energy with the superiority of free transportation,zero pollution,no noise and inexhaustible.Flexible and efficient distributed photovoltaic power generation system is one of the most widely used in the field of solar energy application.But when the power grid system,and to the power grid operation because the photovoltaic system output is easily affected by the light fluctuation,intermittence and randomness make it become a controlled source,efficient,safe and stable operation of the power grid has brought serious challenges.The PV power ahead of the predicted as a benchmark for development of power projects,timely judge system operation and to make corresponding measures,is the ultimate guarantee effective measure power system reliable,durable and stable operation of the.As a result,distributed PV power forecasting and development of monitoring platform has become a hot research topic for researchers in China and abroad.n order to increase the output power of photovoltaic power generation prediction model precision and speed for the purpose,based on distributed photovoltaic power generation system of Weifang electric power company and its data acquisition platform,using Lab VIEW to write data monitoring platform to display and save the data and meteorological data of pv.After analyzing and processing the collected experimental data,the improved IHCMAC algorithm is used to build the prediction model of photovoltaic power output.The main research work is as follows:1.Explore and analyze the structure and characteristics of photovoltaic power generation system.The mathematical model of solar photovoltaic cells is built by using Matlab software,and its output characteristics are obtained.With the help of the local micro power data collected by weather station meteorological data and photovoltaic system,analysis of relationship between the output power of the photovoltaic system and elements from theory,eliminate unnecessary interference factors on the prediction results.2.Using LabVIEW software to build distributed PV system monitoring platform.A monitoring platform with data acquisition,display,prediction and preservation function is developed to observe the change of PV system running state and meteorological conditions in real time.The data is saved as Excel format in order to facilitate subsequent prediction.The monitoring platform predicts that the input of the input parameters of the function module is realized by calling the historical measured data which has been saved as Excel format.3.Analysis and processing model of real time monitoring data.With the historical data of the input and output of the PV system factor correlation analysis,main influencing factors of obtaining the prediction model,and filling method,GESD algorithm and Z-score algorithm to preprocess the experimental data using the mean,the experimental data is more complete and accurate,providing data basis for reasonable and reliable for prediction.4.The distributed PV power forecasting model is established.According to the calculation of short-term prediction of photovoltaic forecast low accuracy problem,based on field data acquisition,analysis and quantitative,based on the improved IHCMAC algorithm,proposed a short-term intelligent prediction algorithm and improve it,using the meteorological parameters,photovoltaic power generation and data acquisition,construct the prediction model of photovoltaic power generation the power and effectiveness of the algorithm is verified through performance evaluation.