activity
20172024
most citedRoles of Dynamic State Estimation in Power System Modeling, Monitoring and Operation

233 citations · 373 across the 18 of their papers we have counts for

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14 papers · 1 filter

eess.SY2023

Cyber-Physical Testbed Integrating RTAC with RTDS for Game-Theoretic Topology Control Under Load Altering Attacks

Alaa Selim, Junbo Zhao

This paper introduces a cyber-physical testbed that integrates the Real-Time Digital Simulator (RTDS) with the Real-Time Automation Controller (RTAC) to enhance cybersecurity in el…

eess.SY2022

Observers for Differential Algebraic Equation Models of Power Networks: Jointly Estimating Dynamic and Algebraic States

Sebastian Nugroho, Ahmad Taha, Nikolaos Gatsis +1

Phasor measurement units ({PMUs}) have become instrumental in modern power systems for enabling real-time, wide-area monitoring and control. Accordingly, many studies have investig…

eess.SY2021

Robust Data-Driven Linear Power Flow Model with Probability Constrained Worst-Case Errors

Yitong Liu, Zhengshuo Li, Junbo Zhao

To limit the probability of unacceptable worst-case linearization errors that might yield risks for power system operations, this letter proposes a robust data-driven linear power…

eess.SY2021★ 44 cited

A robust extended Kalman filter for power system dynamic state estimation using PMU measurements

Marcos Netto, Junbo Zhao, Lamine Mili

This paper develops a robust extended Kalman filter to estimate the rotor angles and the rotor speeds of synchronous generators of a multimachine power system. Using a batch-mode r…

eess.SY2020★ 3 cited

Dynamic State Estimation for Power System Control and Protection

Yu Liu, Abhinav Kumar Singh, Junbo Zhao +12

Dynamic state estimation (DSE) accurately tracks the dynamics of a power system and provides the evolution of the system state in real-time. This paper focuses on the control and p…

eess.SY2020

Distributed Robust State Estimation for Hybrid AC/DC Distribution Systems using Multi-Source Data

Manyun Huang, Junbo Zhao, Zhinong Wei +2

Hybrid AC/DC distribution systems are becoming a popular means to accommodate the increasing penetration of distributed energy resources and flexible loads. This paper proposes a d…

eess.SY2020

Data-Driven Assisted Chance-Constrained Energy and Reserve Scheduling with Wind Curtailment

Xingyu Lei, Zhifang Yang, Junbo Zhao +1

Chance-constrained optimization (CCO) has been widely used for uncertainty management in power system operation. With the prevalence of wind energy, it becomes possible to consider…

eess.SY2020★ 3 cited

Analytic Deep Learning-based Surrogate Model for Operational Planning with Dynamic TTC Constraints

Gao Qiu, Youbo Liu, Junyong Liu +4

The increased penetration of wind power introduces more operational changes of critical corridors and the traditional time-consuming transient stability constrained total transfer…

eess.SY2020★ 6 cited

Model-Free Voltage Regulation of Unbalanced Distribution Network Based on Surrogate Model and Deep Reinforcement Learning

Di Cao, Junbo Zhao, Weihao Hu +4

Accurate knowledge of the distribution system topology and parameters is required to achieve good voltage controls, but this is difficult to obtain in practice. This paper develops…

eess.SY2020

Real-time LCC-HVDC Maximum Emergency Power Capacity Estimation Based on Local PMU Measurements

Long Peng, Junbo Zhao, Yong Tang +3

The adjustable capacity of a line-commutated-converter High Voltage Direct Current (LCC-HVDC) connected to a power system, called the LCC-HVDC maximum emergency power capability or…

eess.SY2020★ 8 cited

Distributed Voltage Regulation of Active Distribution System Based on Enhanced Multi-agent Deep Reinforcement Learning

Di Cao, Junbo Zhao, Weihao Hu +3

This paper proposes a data-driven distributed voltage control approach based on the spectrum clustering and the enhanced multi-agent deep reinforcement learning (MADRL) algorithm.…

eess.SY2020★ 9 cited

Data-driven Optimal Power Flow: A Physics-Informed Machine Learning Approach

Xingyu Lei, Zhifang Yang, Juan Yu +3

This paper proposes a data-driven approach for optimal power flow (OPF) based on the stacked extreme learning machine (SELM) framework. SELM has a fast training speed and does not…

eess.SY2020★ 61 cited

Nonlinear Virtual Inertia Control of WTGs for Enhancing Primary Frequency Response and Suppressing Drive-Train Torsional Oscillations

Bi Liu, Qi Huang, Junbo Zhao +2

Virtual inertia controllers (VICs) for wind turbine generators (WTGs) have been recently developed to compensate for the reduction of inertia in power systems. However, VICs can in…

eess.SY2017★ 3 cited

Robust Power System Dynamic State Estimator with Non-Gaussian Measurement Noise: Part II--Implementation and Results

Junbo Zhao, Lamine Mili

This paper is the second of a two-part series that discusses the implementation issues and test results of a robust Unscented Kalman Filter (UKF) for power system dynamic state est…