Power Quality Assessment Using Signal Processing and Soft Computing Approach

Abstract

During the recent decades the evolution of electrical Power systems increases the interest in the power quality. The increasing utilisation of non-linear and sensitive loads leads to gradual deterioration of the power quality. Power quality problems encountered like voltage sags/swells, harmonics, flicker, and transients etc. affect suppliers and customers alike. But at the same time, the evolution of Digital Signal Processors enables advanced signal processing & soft computing techniques to analyse and correctly describe the changes in voltage and current waveforms. The measurement techniques for detection, classification and assessment of these power quality problems become a key issue. This work is focused on the need for the development of accurate measurement techniques to detect, analyse, quantify and classify the different related power quality problems in distorted environments. Such techniques need to satisfy requirements for real time power quality assessments. The development of a new technique implementing the Wavelet Transform of analytic signals is suitable to investigate and assess different aspects of PQ and to determine correctly the value of the electrical energy. In order to improve the quality of electric power, the sources and causes of disturbances must be known before appropriate mitigating actions can be taken. Real-time identification and classification of voltage and current disturbances is an important and demanding task in power system monitoring and protection. New intelligent technologies using Expert Systems (ES), Genetic Algorithms (GA), Artificial Neural Networks (ANN), adaptive fuzzy logic and Fuzzy Logic (FL), provide some unique advantages regarding PQ problem classification.