基于EEMD的用户电异常判定

Xiaoqiang Zhong, Zhiwei Guo, Dongdong Xu, Hao Zhong, Yu Dong
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引用次数: 0

摘要

目前供电企业对用户用电行为关注较少。他们很难及时发现电源用户的异常情况。针对这一问题,本文提出了一种基于EEMD的异常识别方法。在模型中,我们将电力负荷信号分解为多个本征模态函数(IMF)和残差趋势。不同的IMF分量代表不同周期的不同干扰因素,残差趋势代表剔除波动的总趋势。基于电力负荷聚类理论,选取某企业,得到电力负荷数据。通过对数据的相关性研究,可以对用户的电异常进行诊断。实验表明,本文提出的方法可以有效地判断用户的电异常。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Electrical abnormality determination of the users based on EEMD
Currently power supply enterprises have less attention on the behavior of users in electricity. It is difficult for them to find the abnormality of the power users in time. To solve this problem, this paper gave a method of abnormality determination based on EEMD. In the model, we decomposed electricity load signals into a number of intrinsic mode functions (IMF) and the residual trend. Different IMF components represent different disturbance factors of different cycles, and the residual trend represents the general trend rejecting the fluctuations. Based on the theory of power load clustering, we chose certain enterprise and got the electric load data. The correlation research of the data could be served as the diagnosis of electrical abnormality of users. The experiment shows that the method proposed in this paper can determine the electrical abnormality of users effectively.
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