| 引用本文: | 汪 颖,王 欢,李琼林,等.基于距离判别分析的电压暂降源识别方法[J].电力系统保护与控制,2020,48(19):9-16.[点击复制] |
| WANG Ying,WANG Huan,LI Qionglin,et al.Identification method of voltage sag source based on distance discriminant analysis[J].Power System Protection and Control,2020,48(19):9-16[点击复制] |
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| 摘要: |
| 为准确识别电网中的各类电压暂降源，并避免其他方法在识别过程中特征提取困难的问题，从最小距离的角度提出了一种基于距离判别分析的电压暂降源识别方法。利用暂降分段法对电压暂降有效值的波形变化特点进行分析，并以粗粒化的有效值波形构建了与电压暂降源类型相对应的六个总体。采用多总体马氏距离判别分析方法，利用训练样本进行学习，建立相应的判别函数及其判别准则对待判样本进行判别，从而实现电压暂降源的识别。通过仿真建模对所提方法进行了验证和对比分析，结果表明该方法的识别准确率和性能较高、交叉误判率低，对噪声鲁棒性好，满足实际应用要求。 |
| 关键词: 电能质量 电压暂降源 距离判别分析法 马氏距离 性能评估指标 识别 |
| DOI：DOI:10.19783/j.cnki.pspc.191453 |
| 投稿时间：2019-11-20修订日期：2019-12-26 |
| 基金项目:国家自然科学基金项目资助(51807126) |
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| Identification method of voltage sag source based on distance discriminant analysis |
| WANG Ying,WANG Huan,LI Qionglin,LIU Shuming,XU Lin,SHAO Bin |
| (1.College of Electrical Engineering, Sichuan University, Chengdu 610065, China; 2. State Grid Henan Electric
Power Research Institute, Zhengzhou 450052, China; 3. Electric Power Research Institute of State
Grid Sichuan Electric Power Company, Chengdu 610072, China) |
| Abstract: |
| In order to accurately identify all kinds of voltage sag sources in a power grid and avoid the difficulty of feature extraction experienced by other methods, this paper proposes an identification method of voltage sag source based on distance discriminant analysis from the perspective of minimum distance. The characteristics of waveform change of voltage sag RMS are analyzed using the sag segmentation method, and six populations corresponding to the types of voltage sag source are constructed based on a coarse-grained RMS waveform. The multi population Mahalanobis distance discriminant analysis method is adopted, and the training samples are used for learning, and the corresponding discriminant functions and discriminant criteria are established to distinguish the samples to be judged, so as to realize the identification of voltage sag sources. The proposed method is validated and compared by simulation modeling. The results show that the proposed method has high recognition accuracy and performance, low cross error rate, good noise robustness and meets the practical application requirements.
This work is supported by National Natural Science Foundation of China (No. 51807126). |
| Key words: power quality voltage sag source distance discriminant analysis Mahalanobis distance performance evaluation index identification |
