Wind power prediction deviation grading identification method based on multiple meteorological factors of fuzzy theory

• Main Authors: YAO GUANGZHI, ZHANG JIANNAN, HUANG YE, YANG JUNJIE, AN FENGQIANG, HU JIYUN, JUN JANG-IK, SUN YU, WANG HANJUN, LEE MOOK-WOO, QU KEDING, ZHANG XIAOTIAN, LYU CHANGLIN, LIU HONGYE
• Format: Patent
• Language: Chinese; English
• Published: 17.11.2023
• Subjects: CALCULATING; CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTINGELECTRIC POWER; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS; COMPUTING; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER; COUNTING; DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES; ELECTRIC DIGITAL DATA PROCESSING; ELECTRICITY; GENERATION; PHYSICS; SYSTEMS FOR STORING ELECTRIC ENERGY; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR

The invention discloses a wind power prediction deviation grading identification method based on multiple meteorological factors of a fuzzy theory, and the method comprises the steps: extracting meteorological prediction data to form X (xnmt), and extracting wind power prediction deviation data to form Y (ynt); dividing the X (xnmt) and the Y (ynt) into a plurality of clusters; respectively constructing a meteorological state index, a meteorological change speed index and a meteorological change amplitude index; respectively calculating a meteorological state distance, a meteorological change speed distance, a meteorological change amplitude distance and a comprehensive distance; calculating a fuzzy similar matrix of the comprehensive distance matrix; calculating a transitive closure of the fuzzy similar matrix, selecting a proper confidence level value lambda, and clustering according to a lambda section matrix; and obtaining a wind power prediction deviation level and a confidence level thereof of the to-be