Wavelet and PCA to Power Quality Disturbance Classification Applying a RBF Network

The quality of electric power became an important issue for the electric utility companies and their customers. It is often synonymous with voltage quality since electrical equipments are designed to operate within a certain range of supply specifications. For instance, current microelectronic devices are very sensitive to subtle changes in power quality, which can be represented as a disturbance-induced variation of voltage amplitude, frequency and phase (Dugan et al., 2003). Poor power quality (PQ) is usually caused by power line disturbances such as transients, notches, voltage sags and swells, flicker, interruptions, and harmonic distortions (IEEE Std. 1159, 2009). In order to improve electric power quality, the sources and causes of such disturbances must be known. Therefore, the monitoring equipment needs to firstly and accurately detect and identify the disturbance types (Santoso et al., 1996). Thus, the use of new and powerful tools of signal analysis have enabled the development of additional methods to accurately characterize and identify several kinds of power quality disturbances (Karimi et al., 2000; Mokhtary et al., 2002). Santoso et al. proposed a recognition scheme that is carried out in the wavelet domain using a set of multiple neural networks. The network outcomes are then integrated by using decision-making schemes such as a simple voting scheme or the Dempster-Shafer theory. The proposed classifier is capable of providing a degree of belief for the identified disturbance waveform (Santoso et al., 2000a, 2000b). A novel classification method using a rule-based method and wavelet packet-based hidden Markov models (HMM) was proposed bay Chung et al. The rule-based method is used to classify the time-characterized-feature disturbance and the wavelet packet-based on HMM is used for frequency-characterizedfeature power disturbances (Chung et al., 2002). Gaing presented a prototype of waveletbased network classifier for recognizing power quality disturbances. The multiresolutionanalysis technique of discrete wavelet transforms (DWT) and Parseval’s theorem are used to extract the energy distribution features of distorted signals at different resolution levels. Then, the probabilistic neural network classifies these extracted features of disturbance type identification according to the transient duration and energy features (Gaing, 2004). Zhu et