| 引用本文: | 吕干云,蒋小伟,郝思鹏,等.基于半监督支持向量机的电压暂降源定位[J].电力系统保护与控制,2019,47(18):76-81.[点击复制] |
| Lü Ganyun,JIANG Xiaowei,HAO Sipeng,et al.Location of voltage sag source based on semi-supervised SVM[J].Power System Protection and Control,2019,47(18):76-81[点击复制] |
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| 摘要: |
| 电压暂降源定位对解决相应供用电双方纠纷及责任认定等起到重要作用。针对现有暂降源定位方法的准确率低、含源位置信息的电压暂降监测数据非常有限且不易获取等问题，提出了一种基于半监督支持向量机(SVM)的电压暂降源定位方法。首先分析了现有源定位方法的定位机理和判据，然后通过支持向量机结合多个定位特征量，利用二分类思想在高维定位特征空间内构建上下游分类面。最后运用半监督SVM充分利用大量无暂降源位置标签的电压暂降监测数据，不断优化上下游定位的分类面，从而实现少量标签数据下电压暂降源的优化定位。实验结果表明，在少量标签数据下，该方法定位准确率高，能可靠定位出各类电压暂降源位置。 |
| 关键词: 电压暂降 源定位 半监督支持向量机 标签数据 |
| DOI：10.19783/j.cnki.pspc.181222 |
| 投稿时间：2018-09-27修订日期：2018-12-13 |
| 基金项目:国家自然科学基金项目资助(51577086);电力传输与功率变换控制教育部重点实验室开放课题资助(2016AA02);江苏“六大人才高峰”、江苏高校“青蓝工程”项目资助 |
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| Location of voltage sag source based on semi-supervised SVM |
| LÜ Ganyun,JIANG Xiaowei,HAO Sipeng,LIN Fen,CHENG Haozhong,ZHANG Xinyin,LI Jun |
| (School of Electric Power Engineering, Nanjing Institute of Technology, Nanjing 211167, China;Key Laboratory of Control of Power Transmission and Transformation Shanghai Jiaotong University, Shanghai 200240, China) |
| Abstract: |
| The location of voltage sag source plays an important role in solving the disputes and identifying responsibility between two power supply companies. In view of the situation that using a single location feature does not have high location accuracy, and the voltage sag data with location information is very limited and difficult to obtain, this paper proposes a new method based on semi-supervised Support Vector Machine (SVM) to locate the voltage sag source based on the thought of intelligent classification. Firstly, the location mechanism and criteria of existing source location methods are analyzed and compared. Then this paper combines multiple location features and uses the idea of two classifications to locate upstream and downstream in the high-dimensional location feature spaces. Finally, in order to make full use of a large number of untagged sag monitoring data, the semi-supervised SVM is used for optimizing the upstream and downstream optimal classification planes, and thus can achieve improved sources location performance under a small amount of tag data. The simulation results show that the method has high location accuracy and can locate kinds of sag sources reliably under a small amount of tag data. This work is supported by National Natural Science Foundation of China (No. 51577086). |
| Key words: voltage sag source location semi-supervised support vector machine tagged data |
