Identification and Classification of Power Quality Disturbances Using Wavelet Analysis and Support Vector Machines

In this documentation, I present the core components of a graduation programming project that employs wavelet analysis and support vector machines (SVM) for identifying and classifying power quality disturbances. The primary methodology revolves around energy feature analysis. We will examine in detail the process of using discrete wavelet transform (DWT) for multi-resolution signal decomposition to extract distinctive energy distribution patterns across different frequency bands. The implementation involves calculating energy coefficients from wavelet decomposition subbands using functions like wavedec and wrcoef in MATLAB to form feature vectors. These feature vectors are then fed into a multi-class SVM classifier, implemented using libraries like LIBSVM or MATLAB's Classification Learner, which employs kernel functions (e.g., RBF kernel) to map features to higher-dimensional spaces for optimal hyperplane separation. The discussion will further explore parameter optimization techniques for both wavelet decomposition levels and SVM kernel parameters to enhance classification accuracy. Additionally, we will investigate potential improvements such as hybrid feature extraction methods and ensemble learning approaches. Through this programming implementation, we aim to provide better understanding and solutions for power quality disturbance issues, thereby supporting stable operation of power systems.