Hilbert Huang Transform and type-1 Fuzzy based Recognition and Classification of Power Signal Disturbances

Abstract

this paper deals with hybrid recognition method and classification technique based on Hilbert-Huang transform (HHT) and support vector machine to enhance the accurate delivery and assure efficient recognition of power quality events in the electrical systems. An authentic and quick disturbance recognition method which is the base of power quality control is mandatory. To accomplish this power quality disturbance issue, a Hilbert–Huang transform based method is presented here. Hilbert–Huang transform is an advanced signal processing technique that can be used in the study of non-linear and non-stationary signals.. In the proposed technique, the synthetically generated power quality events are breaking into Hilbert–Huang transform components, referred as empirical mode decomposition and intrinsic mode components. A decomposition action and features separation using Empirical Mode Decomposition (EMD) is conducted for non-stationary power quality disturbances into Intrinsic Mode Functions (IMFs). These components play important role in the calculation of the frequency and amplitude of power quality events. On the bases of these features, fuzzy rules are designed and classification of power quality disturbances performed. The performance evaluation based on simulations results shows that the proposed method has better accuracy and validity for power quality disturbance monitoring in electrical systems.