DWT-Based Power Quality Classification

Abstract

This chapter presents application of DWT and fuzzy set theory to classification of power quality problems. The system uses discrete wavelet transform as linear filters for preprocessing and fuzzy expert system for feature extraction and classification. The signal under test (electrical current or voltage for power quality study) is processed through a DWT decomposition block to generate the feature extraction curve. Then, a fuzzy logic–based inference engine utilizing these features as inputs is implemented for decision making. The DWT level and energy information from the feature extraction curve are passed through a diagnostic module that computes the truth value of the signal combination and determines the class to which the signal belongs. Also presented are comparative performances of fuzzy inference engine with various defuzzification procedures. The proposed scheme has been validated for both Mamdani-type and Sugeno-type fuzzy inference engines. The proposed scheme is much simpler and powerful than currently available power quality (PQ) classification schemes. The organization of the chapter is as follows. Section 5.1 presents background material of the subject. Section 5.2 presents a general introduction to application of DWT in PQ classification. Application of fuzzy inferencing and DWT for monitoring PQ issues has been dealt with in Sect. 5.3, which covers in detail the results related to PQ classification with DWT system. Finally, Sect. 5.4 presents conclusions.