Classification of power quality disturbances based on independent component analysis and support vector machine

This paper proposes a method to identify and classify power quality disturbances (PQD) based on independent component analysis (ICA) and support vector machine (SVM). Firstly, PQD signals are decomposed into 10 layers by db4-wavelet with multi-resolution analysis. Energy Differences (ED) of every level between PQD signals and standard signals are extracted as eigenvectors. Then, Principal Component Analysis (PCA) is adopted to reduce the dimensions of eigenvectors and ICA is used to bleach eigenvectors, which forms new feature vectors. Finally, these new feature vectors are used for power quality disturbance classification using SVM. The results show this method meets the classification accuracy, has a strong resistance to noise, improves classification speed, and is suitable for the classification of PQD.