Sai Munikoti

TL;DR: A detailed review and comparative analysis of qualitative frameworks as well as quantitative metrics for studying resilience are provided and the desirable properties of a resilience metric are highlighted and challenges associated with formulating, developing and calculating such a metric in practical scenarios are discussed.

TL;DR: This paper adopts three tools from machine learning to address the challenge of FDI of incipient faults of dc motor with the most commonly and readily measured current data, and adopts the convolutional network as the best method.

TL;DR: In this article, the authors proposed a new computationally efficient analytical framework of voltage sensitivity analysis that allows for stochastic analysis of voltage change due to random changes in PV generation, and derived an analytical approximation for voltage change at any node of the network due to a change in power at other nodes in an unbalanced distribution network.

TL;DR: In this paper, a weighted Hetero-functional graph theory (HFGT) based modeling framework is proposed to assess the robustness of interdependent networks against complete/partial and random/targeted attacks, and several robustness metrics are used to provide a comprehensive evaluation of system robustness.

TL;DR: This paper presents a computationally efficient analytical approach to compute the probability distribution of voltage change due to random behavior of randomly located multiple distributed PVs based on Spatio-temporal probabilistic voltage sensitivity analysis that exploits both spatial and temporal uncertainties associated with PV injections.