A new signal processing approach/method for classification of power quality disturbances

Abstract

The disadvantage of conventional signal processing methods used for Power Quality (PQ) classification is that their mathematical calculations are complex. Therefore, it is necessary to develop simple, effective, and fast signal processing tools to analyze the PQ signal. In this study, a new signal processing method with simple mathematical operations based on the Integer Factor (IF) down sampling/Approximation Derivatives (AD) was developed to analyze PQ signals. IF was employed to analyze the signals at various sampling frequencies, while the AD method was employed to obtain different degrees of detail and approximation coefficients for the signals. In order to prove that the IF/AD signal processing method can perform a fast and detailed signal processing analysis, the IF/AD method and the Discrete Wavelet Transforms (DWT)/Multiresolution method, which is one of the basic signal processing methods, were compared. It was determined that the developed signal processing method has analyzed PQ disturbances in a shorter time and in more detail than DWT/Multiresolution method. After it was determined that the IF/AD method was effective in the analysis of PQ signals, a new classification method based on the IF-AD signal processing approach, the Slime Mould feature selection algorithm, and Support Vector Machine was developed. The classification method was applied to synthetic signals with noise, real-time data generated in the laboratory and PQ online data. The results proved that the developed classification method was successful. Therefore, this study will provide a different approach to PQ classification methods and non-stationary signal analysis studies.