Abstract
The proposed technique is different from others in respect that it is based on the concept of local non-linear relation and uses non-linear fuzzy functions to extract the feature-specific data. To extract any change during change in the patterns of power quality (PQ) events, non-linear Gaussian functions have been used which results in the formation of fuzzy lattices. The fuzzy lattices have been expressed in the form of Schrodinger equation to find the kinetic energy (KE) used corresponding to any change occurring in the power quality disturbances. Finally, the KE value embedded in two-dimension space has been used to distinguish PQ events. The method is applied to classify the various PQ events such as transient, sag, swell and harmonics and results are simulated using MATLAB version 7.3. The simulated results validate that the proposed algorithm can efficiently distinguish the PQ events in a single cycle and work perfectly in real time.
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