Ravi Yadav

TL;DR: A method for accurate detection, temporal localization, and classification of multiple events in real time using synchrophasor data is proposed and a time series classification based method using energy similarity measure (ESM) is proposed.

TL;DR: A kernel density estimation approach for accurate real-time classification of events in a power system with renewables using synchrophasor data using a diffusion type kernel density estimator (DKDE) to characterize the shape of 3-D voltage and frequency distribution along time in terms of probability density functions (PDFs).

TL;DR: A coherency identification method based on spectrum similarity approach that captures pair-wise similarity between synchrophasor frequency signals is proposed and validated with renewable generation in the IEEE-39 bus system and a real transmission system of India grid.

TL;DR: This article provides objective-driven models for false setting injection (FSI) and false command injection (FCI) type attacks and proposes a principal component analysis (PCA) assisted sequential deep learning approach for online classification of cyber outages and natural events in a power system.

TL;DR: In this paper, a multi-channel/multi-sample dynamic injection attack in phasor trends to closely mimic natural disturbances aiming at trends-based applications is proposed, and an unsupervised wavelet probability mapping method is proposed for detecting and correction of dynamic false data sequences.