摘要
提出基于模糊C-均值(FCM)与神经网络相结合的异常电力负荷修正方法。采用AFCM对电力负荷数据进行聚类,确定聚类中心与聚类数目。依据异常数据所在类别,运用遗传算法优化BP神经网络对异常电力负荷数据进行修正。实验表明:该方法具有很好的聚类效果,对单个异常数据与连续异常数据的平均修正相对误差分别为0. 34%和1. 1%,修正结果优于单纯使用模糊聚类的方法和神经网络的方法。
An abnormal power load correction method based on fuzzy C-means( FCM) and neural network is proposed. Firstly,the method uses adaptive fuzzy C-means( AFCM) to cluster the power load data to determine the cluster center and the number of clusters. Secondly,based on the category of abnormal data,genetic algorithm is used to optimize BP neural network to correct abnormal power load data. Experiments show that the proposed method has a good clustering effect. The average corrected relative error of single anomaly data and continuous anomaly data is 0. 34 % and 1. 1 %,respectively. The correction result is better than the method using fuzzy clustering and neural network alone.
作者
郝晓弘
宋吉祥
裴婷婷
张春燕
李亚岚
周治伊
HAO Xiaohong;SONG Jixiang;PEI Tingting;ZHANG Chunyan;LI Yalan;ZHOU Zhiyi(College of Electrical Engineering and Information Engineering,Lanzhou University of Technology,Lanzhou 730050,China;College of Computer and Communication,Lanzhou University of Technology,Lanzhou 730050,China;Grid Technology Center,Power Science Research Institute of Gansu Electric Power Company,Lanzhou 730070,China)
出处
《传感器与微系统》
CSCD
2020年第4期152-155,共4页
Transducer and Microsystem Technologies
基金
国家自然科学基金资助项目(61540033)
基于可时移农业负荷的分布式电源就地消纳技术研究与示范项目(SGTYHT/14-JS-188)。
