Power quality disturbance detection and classification using wavelet and RBFNN
The power quality disturbance detection and classification is important for improving the power quality. Various disturbances were taken in to account, over 20 events. Wavelet is used to extract features from a MATLAB simulated disturbance waveforms. Radial Basis Function Neural Network has been used to detect and classify the disturbance and compared with other considered approach. The entire work has been presented with particle swarm optimization.
power quality neural networks particle swarm optimization wavelet transformation