Research on complementary algorithm of photovoltaic power missing data based on improved cloud model

International Transactions on Electrical Energy SystemsVolume 30, Issue 7 e12350 SPECIAL ISSUE PAPER Research on complementary algorithm of photovoltaic power missing data based on improved cloud model Mao Yang, Mao Yang Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology, Ministry of Education(Northeast Electric Power University), Jilin, ChinaSearch for more papers by this authorDingze Liu, Corresponding Author Dingze Liu 739214574@qq.com orcid.org/0000-0002-8692-6845 Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology, Ministry of Education(Northeast Electric Power University), Jilin, China Correspondence Dingze Liu, Northeast Electric Power University, Jilin 132012, China. Email: 739214574@qq.comSearch for more papers by this authorYang Cui, Yang Cui Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology, Ministry of Education(Northeast Electric Power University), Jilin, ChinaSearch for more papers by this authorXin Huang, Xin Huang Nanchang Power Supply Branch of Jiangxi Electric Power Co., Ltd. of STATE GRID Corporation of China, Nanchang, ChinaSearch for more papers by this authorGangui Yan, Gangui Yan Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology, Ministry of Education(Northeast Electric Power University), Jilin, ChinaSearch for more papers by this author Mao Yang, Mao Yang Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology, Ministry of Education(Northeast Electric Power University), Jilin, ChinaSearch for more papers by this authorDingze Liu, Corresponding Author Dingze Liu 739214574@qq.com orcid.org/0000-0002-8692-6845 Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology, Ministry of Education(Northeast Electric Power University), Jilin, China Correspondence Dingze Liu, Northeast Electric Power University, Jilin 132012, China. Email: 739214574@qq.comSearch for more papers by this authorYang Cui, Yang Cui Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology, Ministry of Education(Northeast Electric Power University), Jilin, ChinaSearch for more papers by this authorXin Huang, Xin Huang Nanchang Power Supply Branch of Jiangxi Electric Power Co., Ltd. of STATE GRID Corporation of China, Nanchang, ChinaSearch for more papers by this authorGangui Yan, Gangui Yan Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology, Ministry of Education(Northeast Electric Power University), Jilin, ChinaSearch for more papers by this author First published: 04 March 2020 https://doi.org/10.1002/2050-7038.12350Citations: 4 Funding information: National Key Research and Development Program of China, Grant/Award Number: 2018YFB0904200 Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Abstract The completion of the missing data of photovoltaic (PV) power is of great significance for the development of PV power related research. Based on the analysis of the main influencing factors of PV power, the PV power data of different illumination characteristics were classified by the goodness of fit. Combined with Copula theory and abnormal data discriminant criteria, the PV power anomaly data were effectively identified. We changed the normal random entropy in the traditional cloud model to the random entropy based on Copula theory, and established an improved cloud model of irradiance and power. Based on the probability of PV power conditions generated by the cloud model, according to the volatility of PV power, this article constructed a conditional interpolation algorithm. The comparison of the results of different random missing proportions proved the limitation of the traditional cloud model and the instability of the mean method. It also proved that the accuracy of the method was improved and the error was the lowest. Citing Literature Volume30, Issue7Special Issue: Data Analytics and Machine Learning for the Energy InternetJuly 2020e12350 RelatedInformation