| 康宁宁,李川,曾虎,李英娜.采用FCM聚类与改进SVR模型的窃电行为检测[J].电子测量与仪器学报,2017,31(12):2023-2029 |
| 采用FCM聚类与改进SVR模型的窃电行为检测 |
| Electric larceny detectionusing FCM clustering and improved SVR model |
| |
| DOI：10.13382/j.jemi.2017.12.020 |
| 中文关键词: 窃电检测 负荷曲线 FCM聚类分析 粒子群算法 支持向量机回归算法 |
| 英文关键词:electricity larceny detection load curve FCM clustering analysis particle swarm optimization algorithm support vector regression algorithm |
| 基金项目: |
|
|
| Author | Institution |
| Kang Ningning | School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650504, China |
| Li Chuan | School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650504, China |
| Zeng Hu | School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650504, China |
| Li Yingna | School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650504, China |
|
| 摘要点击次数: 2387 |
| 全文下载次数: 7203 |
| 中文摘要: |
| 针对窃电手段多样、隐蔽性强、窃电检测效率有待提高等问题，首先采用模糊C均值（FCM）聚类算法构造不同的用户负荷特征曲线，通过待测负荷曲线与相应特征曲线作对比初步确定疑似窃电用户；其次，采用粒子群算法优化的支持向量机回归模型对疑似窃电用户的用电行为进行检测。实验证明，所用方法缩小了窃电检测的范围、克服了窃电样本少的影响，改善了窃电检测的效率，并且窃电检测的均方误差和平均绝对误差分别提高了0.005 1和0.034。 |
| 英文摘要: |
| Aiming atthe variety of electric larceny means,the efficiency of electric larceny detection remains improvement.Firstly, the fuzzy C mean clustering algorithm is used to construct different load characteristic curves of the user, and the suspiciouselectric larceny user is preliminarily determined by comparing the curves to be detected with the corresponding characteristic curve.Secondly,the particle swarm optimization support vector machine regression model is adopted to detect the behavior of suspected power stealing users.The experiments show that this method can reduce the range of electricity larcenydetection and overcome the influence of less electricity larcenysamples, improve the efficiency ofelectricity larcenydetection, and increasethe mean square error and average absolute error by 00051 and 0.034 respectively. |
| 查看全文 查看/发表评论 下载PDF阅读器 |
|
|
|
