电力系统及其自动化学报2024，Vol.36Issue(5)：59-68,10.DOI:10.19635/j.cnki.csu-epsa.001302
基于改进深度极限学习机的光伏扩容用户识别方法
Identification Method for Photovoltaic Capacity Expansion Users Based on Improved Deep Extreme Learning Machine
摘要
Abstract
To accurately identify the distributed photovoltaic(PV)capacity expansion fraud users,an identification method for PV capacity expansion users based on an improved deep extreme learning machine(DELM)is proposed.First,with the consideration of the similarity of PV generation in the same region,the reference power station and the site to be tested are pre-processed by cosine similarity.Second,the sparrow search algorithm(SSA)is used to optimize the weight parameters of DELM,and the pre-processed data set is imported into the SSA-DELM fitting model.Finally,the expansion coefficients are calculated according to the characteristics of PV capacity expansion.Experimental results validate the effectiveness of the proposed method for the identification of distributed PV non-compliant capacity expan-sion users.关键词
分布式光伏/违规扩容/深度极限学习机/麻雀搜索算法Key words
distributed photovoltaic(PV)/non-compliant capacity expansion/deep extreme learning machine(DELM)/sparrow search algorithm(SSA)分类
信息技术与安全科学引用本文复制引用
汤渊,吴裕宙,苏盛,刘韵艺,王耀龙..基于改进深度极限学习机的光伏扩容用户识别方法[J].电力系统及其自动化学报,2024,36(5):59-68,10.基金项目
国家自然科学基金资助项目(51777015) （51777015）
南方电网公司科技项目(031900KK52220039) （031900KK52220039）
