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文章摘要
基于自适应PFCM聚类的电力负荷数据预处理
Power load data preprocessing based on adaptive PFCM clustering
Received:May 11, 2019  Revised:May 11, 2019
DOI：10.19753/j.issn1001-1390.2020.21.006
中文关键词: 异常值  负荷预处理  可能性模糊C均值  粒子群算法
英文关键词: Outliers, Data processing, Possibility fuzzy C-means, Particle swarm optimization algorithm
基金项目:国家自然科学基金项目（ NO.61540033）
Author NameAffiliationE-mail
haoxiaohong College of Electrical and Information Engineering, Lanzhou University of Technology haoxhlut@163.com 
zhangchunyan* College of Computer and Communication, Lanzhou University of Technology 1505306148@qq.com 
peitingting College of Electrical and Information Engineering, Lanzhou University of Technology 253943700@qq.com 
wangweizhou Gansu Electric Power Research Institute of State Grid 1157639917@qq.com 
liufuchao Gansu Electric Power Company Electric Power Research Institute liufc@sina.com 
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中文摘要:
      考虑到电网实际运行过程中，负荷数据因各随机因素产生异常对负荷预测的准确性与负荷调度的有效性造成严重影响，提出一种自适应PFCM聚类算法以修正电力负荷异常数据。该算法首先利用新定义的PFCM聚类有效性指标函数与动态调节权重的PSO算法分别实现了负荷曲线最优聚类数目与聚类中心的自适应确定；然后利用改进的PFCM算法提取负荷特征曲线，实现了对负荷曲线的聚类；最后使用该方法对西北某市负荷数据进行聚类分析，并利用相关方法进行异常数据的识别与修正。验证性实验结果表明，改进算法样本距聚类中心的距离明显更小，且在相同异常值修正公式下，使用改进后算法聚类结果修正的异常值更接近于原始负荷数据，平均相对误差相比改进前降低1.99%。
英文摘要:
      
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