Pre-Processing Tools and Intelligent Systems Applied to Power Quality Analysis

In the last few years the power quality has become the target of many researches carried out either by academic or by utility companies. Moreover, a desired good power quality is essential for the Power Distribution System (PDS). The PDS can have (or impose) inherent operational conditions, that affect frequency and three-phase voltage signals. Among the main disturbances that indicate a poor power quality, the following can be highlighted: voltage sag/swell, overvoltage, undervoltage, interruption, oscillatory transient, noise, flicker and harmonic distortion (Dugan et al., 2003). Actually, in literature, a diversity of papers can be found concerning detection and identification of power quality disturbances by applying intelligent systems, such as Artificial Neural Networks (ANN) (Janik & Lobos, 2006; Oleskovicz et. al., 2009; Jayasree, Devaraj & Sukanesh, 2010) and Fuzzy Inference Systems (Zhu, Tso & Lo, 2004; Hooshmand & Enshaee, 2010; Meher & Pradhan, 2010; Behera, Dash & Biswal, 2010). However, only some papers use data pre-processing tools before the application of intelligent systems. Among these papers, the use of Discrete Wavelet Transform (DWT) (Zhu, Tso & Lo, 2004; Uyar, Yildirim & Gencoglu, 2008; Oleskovicz et. al., 2009) and Discrete Fourier Transform (DFT) (Zhang, Li & Hu, 2011) can be highlighted in the pre-processing stage. According to the literature, it should also be mentioned that the pre-processing tools help to ensure a better detection and identification of disturbances in the power quality context. In Hooshmand & Enshaee (2010), the authors propose a new method for detecting and classifying power quality disturbances. However, this method can be used both for the occurrence of one and multiple disturbances. This is a method that uses techniques for data pre-processing combined with intelligent systems. In this case, the authors extracted features of a time-varying voltage signal, such as: • Fundamental component; • Phase angle shift; • Total harmonic distortion; • Number of the maximums of the absolute value of wavelet coefficients; • Calculation of energy of the wavelet coefficients;