A Very Short-Term Prediction Model For Photovoltaic Power Based On Improving The Total Sky Cloud Image Recognition

Because of the cloud cover, the power generation of photovoltaic power plant will reduce suddenly, that may lead to instability of the grid and bring some risks. Usually we use methods based on the observation of cloud to achieve photovoltaic power very short term prediction. But in the haze weather, since the decline in the quality of the total sky image, the cloud recognition effect is reduced, further lead to decreased levels of photovoltaic power prediction. This paper presents a cloud extraction algorithm that includes facula removal restoration, enhancement and intensity stratification, clouds are completely extracted under haze conditions. Based on the accurate identification of cloud images and the mapping relationship between cloud and solar radiation, the prediction accuracy of photovoltaic power generation is improved.Firstly, the extraction method using DFT check clouds exist or not, and computing in the position of the solar facula, and then fix the facula. Next, using piece wise linear transformation algorithm for cloud enhancement, and then use cloud technology to generate intensity layered cloud stain chart. Finally, the relationship between cloud and radiation is used to realize the very short term prediction of photovoltaic power.The experimental results show that the algorithm has a very good universality, and the cloud image can be identified by the fog and haze, so it improves the accuracy of the ultra-short term prediction of photovoltaic power.