Abstract: This paper presents the development of a new low rank robust kernel ridge regression (KRR) classifier for power quality (PQ) disturbance pattern recognition. It is well known that the kernel methods are used extensively for regression and classification problems using support vector machines (SVM) by mapping the original nonlinear data into a high-dimensional space thereby increasing the generalization performance, and accuracy of classification. On the other hand, the nonlinear kernel ridge regression approach is known for its simple implementation, fast processing speed and accuracy in comparison to the widely used least square support vector machine (LS-SVM). However, for large scale training patterns, the size of kernel matrix becomes large thereby the execution time becomes prohibitive. Besides, the presence of noise and outliers in the data affects the accuracy of the conventional KRR classifier. This paper, therefore, attempts to develop a dimensionally reduced but robust KRR classifier by choosing a random set of support vectors from the training subset to reduce substantially the training time at the cost of a slight loss in accuracy. Further, the KRR algorithm is modified by using a new objective function to minimize the mean and variance of the error to provide robustness and accuracy. For applying this new classifier to power quality disturbance events three relatively new signal processing techniques like the Short-time modified Hilbert Transform (STMHT), Morphological filters, and Fourier kernel S-transform are used to extract the relevant features from the data samples. The simulation results imply that the proposed methods have a higher recognition rate compared with other established techniques such as ELM and Poly SVM while classifying the PQ disturbances. A PC integrated hardware assembly has been used to verify the PQ events classification in real-time.
Keywords: Short time modified hilbert transform, fourier kernel based s-transform, mathematical morphology, power quality events, kernel ridge regression
