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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;

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