S
Shikhar Pandey
Researcher at Washington State University
Publications - 22
Citations - 207
Shikhar Pandey is an academic researcher from Washington State University. The author has contributed to research in topics: Phasor measurement unit & Engineering. The author has an hindex of 5, co-authored 13 publications receiving 86 citations. Previous affiliations of Shikhar Pandey include Commonwealth Edison & Pacific Northwest National Laboratory.
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A Real Time Event Detection, Classification and Localization Using Synchrophasor Data
TL;DR: Algorithms include statistic, clustering, and Maximum Likelihood Criterion (MLE) based anomaly detection, Density-based spatial clustering of applications with noise (DBSCAN) for event detection and physics-based rule/ decision tree for event classification.
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Online Estimation of Steady-State Load Models Considering Data Anomalies
TL;DR: The challenges in online estimation of the load parameters using phasor measurement unit data are addressed and a novel adaptive search-based algorithm to estimate load model parameters is presented here.
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Resiliency-Driven Proactive Distribution System Reconfiguration With Synchrophasor Data
TL;DR: Data mining approaches for anomaly detection in D-PMUs and proposing resiliency-driven pre-event reconfiguration with islanding as a proactive mechanisms to minimize the impact of adverse events on system using processed synchrophasors data are provided.
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Real-Time Synchrophasor Data Anomaly Detection and Classification Using Isolation Forest, KMeans, and LoOP
TL;DR: The proposed synchrophasor anomaly detection and classification (SyADC) tool analyzes a selected window of data points using a combination of three unsupervised methods, namely: isolation forest, KMeans and LoOP, and classifies the data as anomalies or normal data with more than 99% recall.
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Data-driven failure diagnosis in transmission protection system with multiple events and data anomalies
TL;DR: A PMU based algorithm is presented and discussed to detect the root cause of the failure in transmission protection system based on the observed state, e.g. multiple line tripping, breaker failures and results show that the ensemble approach has some distinct advantages in data anomaly detection compared to the previously used standalone algorithms.