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Detection and Classification of Multiple Power-Quality Disturbances With Wavelet Multiclass SVM

2008; Institute of Electrical and Electronics Engineers; Volume: 23; Issue: 4 Linguagem: Inglês

10.1109/tpwrd.2008.923463

1937-4208

Whei-Min Lin, Chien-Hsien Wu, Chia‐Hung Lin, Fu-Sheng Cheng,

Blind Source Separation Techniques

This paper presents an integrated model for recognizing power-quality disturbances (PQD) using a novel wavelet multiclass support vector machine (WMSVM). The so-called support vector machine (SVM) is an effective classification tool. It is deemed to process binary classification problems. This paper combined linear SVM and the disturbances-versus-normal approach to form the multiclass SVM which is capable of processing multiple classification problems. Various disturbance events were tested for WMSVM and the wavelet-based multilayer-perceptron neural network was used for comparison. A simplified network architecture and shortened processing time can be seen for WMSVM.