PQ Disturbances Identification Based on SVMs Classifier
PQ Disturbances Identification Based on SVMs Classifier
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基于SVM分类器的电能质量干扰识别
DOI：
10.1109/icnnb.2005.1614602
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发表时间：
2005
期刊：
影响因子：
--
通讯作者：
Changjiang Zhang
中科院分区：
文献类型：
--
作者：
G. Lv;Xiaodong Wang;Haoran Zhang;Changjiang Zhang
电力系统的放松管制政策导致了对电能质量进行量化的必要性。需要一种有效的PQ干扰分类策略。提出了一种基于N-I支持向量机的电能质量扰动识别新方法。通过相移和一些简单的代数运算，首先检测PQ扰动。然后进行数据处理，从检测输出中提取特征。然后用N-I支持向量机分类器对N种电能质量扰动进行分类。测试结果表明，该方法能够成功地对电能质量扰动进行分类。此外，该分类器在训练速度和可靠性方面都有很好的表现
The deregulation polices in electric power systems result in the absolute necessity to quantify power quality (PQ). An effective classification strategy for PQ disturbances was needed. A new method based on N-I support vector machines (SVMs) was presented for PQ disturbances identification. Through phase-shift and some simple algebra operations, the PQ disturbances were detected first. Then a data dealing process was carried out to extract features from the detecting outputs. Then N kinds of PQ disturbances were classified with an N-I SVMs classifier. The testing results show that the proposed method could classify the PQ disturbances successfully. Moreover, the classifier has an excellent performance on training speed and reliability