  In this paper, a novel method for classification of power quality events is
illustrated. 15 types of power quality events consisting of single and
multi-stage disturbances are considered for study. A database of the synthetic
PQ events is generated in MATLAB using mathematical models. The generated
signals are passed through a novel Modified Stockwell transform consisting of
second order gaussian window which provides the ST matrix. From the ST matrix
various statistical features such as energy and standard deviation of the
magnitude and phase contour are extracted and given as input to Support Vector
Machine (SVM). Furthermore, to improve the performance of SVM, a novel
meta-heuristic technique called Whale Optimization Algorithm (WOA) is used to
tune the hyper parameters of the SVM classifier. The performance of the
proposed method is analyzed under noisy and noiseless conditions. It is
observed that WOA tuned SVM provides improved classification accuracy than
other widely used meta-heuristic optimization algorithms such as Particle Swarm
Optimization (PSO) tuned SVM and Genetic Algorithm (GA) tuned SVM. Further, two
novel circuits for generation of sag, swell and interrupt are developed and the
proposed technique is validated on real time signals obtained from the
circuits.
