Karthik Thirumala

TL;DR: A new automated recognition approach based on tunable-Q wavelet transform and a dual multiclass support vector machines (MSVM) has been proposed for detection of power quality disturbances and results demonstrate the applicability, strength, and accuracy of the proposed approach for classification of single and combined disturbances under different noisy conditions.

TL;DR: In this paper, an empirical wavelet transform (EWT)-based time-frequency technique is proposed for the estimation of power-quality indices (PQIs), which first estimates the frequency components present in the distorted signal, computes the boundaries, and then filtering is done based on the boundaries computed.

TL;DR: An automated recognition approach for the classification of power quality (PQ) disturbances based on adaptive filtering and a multiclass support vector machine (SVM) is presented to elucidate the efficiency and robustness of the proposed approach against noise and different degrees of disorder.

TL;DR: In this article, a generalized empirical wavelet transform (GEWT) is employed to assess all sorts of power signals having disturbances such as voltage fluctuation, sag, swell, interruption, transients, harmonics, and interharmonics.

TL;DR: A P2P market settlement mechanism which lowers this risk and maximizes the welfare of buyers and sellers, and gives room to the existing distribution system operators by assigning them the duty of optimally allocating energy.