| 引用本文: | 林宝德,杨铮宇.基于多维特征的电网台区线损数据异常识别研究[J].电力系统保护与控制,2022,50(9):173-178.[点击复制] |
| LIN Baode,YANG Zhengyu.Anomaly recognition of line loss data in power grid stations based on multi-dimensional features[J].Power System Protection and Control,2022,50(9):173-178[点击复制] |
|
| 摘要: |
| 随着智能电表及用电管理终端的广泛应用，电网台区相关监测终端每天可收集到海量线损数据，并且可对存在的异常情况进行识别。但是数据噪声对电网台区线损数据的干扰，导致识别的准确率和召回率下降。针对这些问题，提出了一种基于多维特征的电网台区线损数据异常识别方法。该方法首先将电网台区线损数据样本形成对应的二维数据，采用二维小波阈值法进行去噪。根据去噪后二维数据的位置特征以及时间数据特征，对Hasusdorff距离公式进行改进，用以计算电网台区线损数据的多维特征相似度，得到线损数据之间的相似性矩阵。最后将多维Hasusdorff距离应用到层次聚类算法中去识别电网台区线损数据中的异常。仿真实验结果表明，所提方法的准确率和召回率较高。电网台区线损数据异常识别时间较短，满足工程实际使用要求。 |
| 关键词: 多维特征 电网台区线损数据 异常识别 去噪 |
| DOI：DOI: 10.19783/j.cnki.pspc.211424 |
| 投稿时间：2021-10-24修订日期：2021-12-28 |
| 基金项目:南方电网公司科技项目资助(YNKJXM20170824) |
|
| Anomaly recognition of line loss data in power grid stations based on multi-dimensional features |
| LIN Baode,YANG Zhengyu |
| (Information Center of Yunnan Power Grid Co., Ltd., Kunming 650000, China) |
| Abstract: |
| With the wide application of smart electricity meters and power consumption management terminals, the relevant monitoring terminals in power grid stations can collect massive line loss data every day and identify the abnormal situations. However, the interference of data noise to line loss data in power grid stations leads to the decline of identification accuracy and recall rate. In order to solve these problems, an anomaly recognition method for line loss data in power grid area based on multidimensional features is proposed. In this method, the line loss data samples of power grid area are first formed into corresponding two-dimensional data and denoised by two-dimensional wavelet threshold method. Hasusdorff distance formula is improved according to the location and time characteristics of two-dimensional data after denoising, which is used to calculate the multi-dimensional characteristic similarity of line loss data in power grid area, and the similarity matrix between line loss data is obtained. Finally, multi-dimensional Hasusdorff distance is applied to hierarchical clustering algorithm to identify anomalies in line loss data of power grid area. Simulation results show that the proposed method has high accuracy and recall rate. The abnormal identification time of line loss data in power grid area is short, which meets the requirements of practical engineering. |
| Key words: multi-dimensional features line loss data of power grid stations anomaly identification denoising |
