World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
56
Citations
10357
World Ranking
4141
National Ranking
1956

Electronics and Electrical Engineering

D-Index
57
Citations
11231
World Ranking
1994
National Ranking
782

Overview

Junbo Zhao is affiliated with the University of Connecticut in the United States. Their primary research contributions are situated in the field of Engineering, with a specialized focus on Electrical and Electronic Engineering, Control and Systems Engineering, Artificial Intelligence, Safety, Risk, Reliability and Quality, and Computer Networks and Communications.

The scientist has authored a substantial number of publications across several core research topics. These main topics include:

  • Power System Optimization and Stability
  • Optimal Power Flow Distribution
  • Microgrid Control and Optimization
  • Smart Grid Energy Management
  • Energy Load and Power Forecasting
  • Smart Grid Security and Resilience
  • Power System Reliability and Maintenance

Zhao's recent published papers cover various aspects of power systems and smart grids. These include:

  • "Reinforcement Learning and Its Applications in Modern Power and Energy Systems: A Review" (2020), Journal of Modern Power Systems and Clean Energy
  • "A Novel Hybrid Short-Term Load Forecasting Method of Smart Grid Using MLR and LSTM Neural Network" (2020), IEEE Transactions on Industrial Informatics
  • "A Multi-Agent Deep Reinforcement Learning Based Voltage Regulation Using Coordinated PV Inverters" (2020), IEEE Transactions on Power Systems
  • "Roles of Dynamic State Estimation in Power System Modeling, Monitoring and Operation" (2020), IEEE Transactions on Power Systems
  • "Power system inertia estimation: Review of methods and the impacts of converter-interfaced generations" (2021), International Journal of Electrical Power & Energy Systems

The scientist frequently publishes in the following venues:

  • IEEE Transactions on Power Systems
  • arXiv (Cornell University)
  • IEEE Transactions on Smart Grid
  • International Journal of Electrical Power & Energy Systems
  • IEEE Transactions on Industrial Informatics

Collaborative efforts are evident through frequent co-authors, which include:

  • Weihao Hu
  • Bendong Tan
  • Di Cao
  • Fei Ding
  • Zhe Chen

Junbo Zhao's work intersects various domains of power systems, emphasizing optimization, forecasting, control, and reliability. Their research contributions span advanced methodologies integrating machine learning and reinforcement learning applied to power system dynamics and smart grid technologies.

Best Publications

  • Power System Dynamic State Estimation: Motivations, Definitions, Methodologies, and Future Work

    Junbo Zhao;Antonio Gomez-Exposito;Marcos Netto;Lamine Mili

  • A Robust Iterated Extended Kalman Filter for Power System Dynamic State Estimation

    Junbo Zhao;Marcos Netto;Lamine Mili

  • Reinforcement Learning and Its Applications in Modern Power and Energy Systems: A Review

    Di Cao;Weihao Hu;Junbo Zhao;Guozhou Zhang

  • Fault Diagnosis of Electric Power Systems Based on Fuzzy Reasoning Spiking Neural P Systems

    Tao Wang;Gexiang Zhang;Junbo Zhao;Zhengyou He

  • A Novel Hybrid Short-Term Load Forecasting Method of Smart Grid Using MLR and LSTM Neural Network

    Jian Li;Daiyu Deng;Junbo Zhao;Dongsheng Cai

  • Robust Unscented Kalman Filter for Power System Dynamic State Estimation With Unknown Noise Statistics

    Junbo Zhao;Lamine Mili

  • A Multi-Agent Deep Reinforcement Learning Based Voltage Regulation Using Coordinated PV Inverters

    Di Cao;Weihao Hu;Junbo Zhao;Qi Huang

  • Short-Term State Forecasting-Aided Method for Detection of Smart Grid General False Data Injection Attacks

    Junbo Zhao;Gexiang Zhang;Massimo La Scala;Zhao Yang Dong

  • Power system inertia estimation: Review of methods and the impacts of converter-interfaced generations

    Bendong Tan;Junbo Zhao;Marcos Netto;Venkat Krishnan

  • Power System Real-Time Monitoring by Using PMU-Based Robust State Estimation Method

    Junbo Zhao;Gexiang Zhang;Kaushik Das;George N. Korres

  • Roles of Dynamic State Estimation in Power System Modeling, Monitoring and Operation

    Junbo Zhao;Marcos Netto;Zhenyu Huang;Samson Shenglong Yu

  • Assessing Gaussian Assumption of PMU Measurement Error Using Field Data

    Shaobu Wang;Junbo Zhao;Zhenyu Huang;Ruisheng Diao

  • A Generalized False Data Injection Attacks Against Power System Nonlinear State Estimator and Countermeasures

    Junbo Zhao;Lamine Mili;Meng Wang

  • Data-Driven Multi-Agent Deep Reinforcement Learning for Distribution System Decentralized Voltage Control With High Penetration of PVs

    Di Cao;Junbo Zhao;Weihao Hu;Fei Ding

  • Dynamic State Estimation With Model Uncertainties Using $H_\infty$ Extended Kalman Filter

    Junbo Zhao

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

    Xingyu Lei;Zhifang Yang;Juan Yu;Junbo Zhao

  • A Robust Generalized-Maximum Likelihood Unscented Kalman Filter for Power System Dynamic State Estimation

    Junbo Zhao;Lamine Mili

  • Deep Reinforcement Learning Enabled Physical-Model-Free Two-Timescale Voltage Control Method for Active Distribution Systems

    Di Cao;Junbo Zhao;Weihao Hu;Nanpeng Yu

  • Attention Enabled Multi-Agent DRL for Decentralized Volt-VAR Control of Active Distribution System Using PV Inverters and SVCs

    Di Cao;Junbo Zhao;Weihao Hu;Fei Ding

  • Dynamic State Estimation for Power System Control and Protection IEEE Task Force on Power System Dynamic State and Parameter Estimation

    Yu Liu;Abhinav Kumar Singh;Junbo Zhao;A. P. Sakis Meliopoulos

  • Calibrating Parameters of Power System Stability Models Using Advanced Ensemble Kalman Filter

    Renke Huang;Ruisheng Diao;Yuanyuan Li;Juan Sanchez-Gasca

  • Design and implementation of membrane controllers for trajectory tracking of nonholonomic wheeled mobile robots

    Xueyuan Wang;Xueyuan Wang;Gexiang Zhang;Ferrante Neri;Ferrante Neri;Tao Jiang

  • A Framework for Robust Hybrid State Estimation With Unknown Measurement Noise Statistics

    Junbo Zhao;Lamine Mili

Frequent Co-Authors

Lamine Mili
Lamine Mili Virginia Tech
Gexiang Zhang
Gexiang Zhang Chengdu University of Information Technology
Weihao Hu
Weihao Hu University of Electronic Science and Technology of China
Zhenyu Huang
Zhenyu Huang Pacific Northwest National Laboratory
Vladimir Terzija
Vladimir Terzija Newcastle University
Zhe Chen
Zhe Chen Aalborg University
Tianshu Bi
Tianshu Bi North China Electric Power University
Innocent Kamwa
Innocent Kamwa Université Laval
Federico Milano
Federico Milano University College Dublin
Bikash C. Pal
Bikash C. Pal Imperial College London

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