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Xiaozhe Wang

Researcher at McGill University

Publications -  83
Citations -  793

Xiaozhe Wang is an academic researcher from McGill University. The author has contributed to research in topics: Electric power system & Computer science. The author has an hindex of 12, co-authored 70 publications receiving 473 citations. Previous affiliations of Xiaozhe Wang include Cornell University & Massachusetts Institute of Technology.

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Journal ArticleDOI

Long-Term Stability Analysis of Power Systems With Wind Power Based on Stochastic Differential Equations: Model Development and Foundations

TL;DR: A theory-based method is proposed for power systems with wind power to conduct long-term stability analysis, which is able to provide accurate stability assessments with fast simulation speed.
Journal ArticleDOI

PMU-Based Estimation of Dynamic State Jacobian Matrix and Dynamic System State Matrix in Ambient Conditions

TL;DR: In this article, a hybrid measurement-and model-based method is proposed which can estimate the dynamic state Jacobian matrix and the dynamic system state matrix in near real time utilizing statistical properties extracted from PMU measurements.
Journal ArticleDOI

Data-Driven Diagnostics of Mechanism and Source of Sustained Oscillations

TL;DR: This work develops mathematical framework for analysis of sustained oscillations and identifies statistical signatures of each mechanism, based on which a novel oscillation diagnosis method is developed via real-time processing of phasor measurement units (PMUs) data.
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Online PMU-Based Wide-Area Damping Control for Multiple Inter-Area Modes

TL;DR: A new phasor measurement unit (PMU)-based wide-area damping control (WADC) method to suppress the critical inter-area modes of large-scale power systems and demonstrates the good adaptivity of the control strategy subjected to network model changes, its effective damping performance comparing to power system stabilizers (PSSs), and its great potential for near real-time implementation.
Proceedings ArticleDOI

PMU-based estimation of dynamic state Jacobian matrix

TL;DR: A hybrid measurement- and model-based method is proposed which can estimate the dynamic state Jacobian matrix and the dynamic system state matrix in near real time utilizing statistical properties extracted from PMU measurements.