World's Best Scientists 2026 revealed!

D-Index & Metrics

Electronics and Electrical Engineering

D-Index
57
Citations
11231
World Ranking
1994
National Ranking
783

Computer Science

D-Index
56
Citations
10357
World Ranking
4141
National Ranking
1957

Junbo Zhao publication distribution in Electronics and Electrical Engineering in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Electronics and Electrical Engineering in 2026. The highlighted bar marks where Junbo Zhao sits on this spectrum.

34–53 publications: 24 scientists 54–73 publications: 52 scientists 74–93 publications: 114 scientists 94–113 publications: 203 scientists 114–133 publications: 269 scientists 134–153 publications: 355 scientists 154–173 publications: 403 scientists 174–193 publications: 445 scientists 194–213 publications: 430 scientists 214–233 publications: 431 scientists 234–253 publications: 399 scientists 254–273 publications: 366 scientists 274–293 publications: 335 scientists 294–313 publications: 300 scientists 314–333 publications: 276 scientists 334–353 publications: 250 scientists 354–373 publications: 214 scientists 374–393 publications: 187 scientists 394–413 publications: 152 scientists 414–433 publications: 169 scientists 434–453 publications: 147 scientists 454–473 publications: 111 scientists 474–493 publications: 117 scientists 494–513 publications: 103 scientists 514–533 publications: 99 scientists 534–553 publications: 92 scientists 554–573 publications: 75 scientists 574–593 publications: 58 scientists 594–613 publications: 69 scientists 614–633 publications: 50 scientists 634–653 publications: 62 scientists 654–673 publications: 54 scientists 674–693 publications: 44 scientists 694–713 publications: 37 scientists 714–733 publications: 28 scientists 734–753 publications: 26 scientists 754–773 publications: 26 scientists 774–793 publications: 19 scientists 794–813 publications: 23 scientists 814–833 publications: 20 scientists 834–853 publications: 16 scientists 854–873 publications: 20 scientists 874–893 publications: 11 scientists 894–913 publications: 11 scientists 914–933 publications: 16 scientists 934–953 publications: 13 scientists 954–973 publications: 10 scientists 974–993 publications: 11 scientists 994–1,013 publications: 9 scientists 1,014–1,033 publications: 9 scientists 1,034–1,053 publications: 10 scientists 1,054–1,064 publications: 6 scientists 1,065+ publications: 99 scientists
34 publications 1,065+

This scientist: 251 publications — 45th percentile

45% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 1,065 publications or more.

Junbo Zhao D-index placement in Electronics and Electrical Engineering in 2026

The chart shows the D-index (discipline H-index) distribution of Electronics and Electrical Engineering scientists ranked by Research.com in 2026. The highlighted bar marks where Junbo Zhao sits on this spectrum.

30 D-Index: 178 scientists 31 D-Index: 257 scientists 32 D-Index: 263 scientists 33 D-Index: 262 scientists 34 D-Index: 244 scientists 35 D-Index: 236 scientists 36 D-Index: 211 scientists 37 D-Index: 220 scientists 38 D-Index: 214 scientists 39 D-Index: 214 scientists 40 D-Index: 205 scientists 41 D-Index: 187 scientists 42 D-Index: 194 scientists 43 D-Index: 201 scientists 44 D-Index: 155 scientists 45 D-Index: 189 scientists 46 D-Index: 148 scientists 47 D-Index: 160 scientists 48 D-Index: 134 scientists 49 D-Index: 130 scientists 50 D-Index: 141 scientists 51 D-Index: 156 scientists 52 D-Index: 108 scientists 53 D-Index: 130 scientists 54 D-Index: 112 scientists 55 D-Index: 97 scientists 56 D-Index: 111 scientists 57 D-Index: 102 scientists 58 D-Index: 108 scientists 59 D-Index: 120 scientists 60 D-Index: 103 scientists 61 D-Index: 93 scientists 62 D-Index: 92 scientists 63 D-Index: 74 scientists 64 D-Index: 77 scientists 65 D-Index: 73 scientists 66 D-Index: 64 scientists 67 D-Index: 69 scientists 68 D-Index: 60 scientists 69 D-Index: 39 scientists 70 D-Index: 57 scientists 71 D-Index: 59 scientists 72 D-Index: 46 scientists 73 D-Index: 49 scientists 74 D-Index: 38 scientists 75 D-Index: 35 scientists 76 D-Index: 32 scientists 77 D-Index: 35 scientists 78 D-Index: 31 scientists 79 D-Index: 22 scientists 80 D-Index: 34 scientists 81 D-Index: 31 scientists 82 D-Index: 34 scientists 83 D-Index: 23 scientists 84 D-Index: 18 scientists 85 D-Index: 30 scientists 86 D-Index: 19 scientists 87 D-Index: 19 scientists 88 D-Index: 20 scientists 89 D-Index: 8 scientists 90 D-Index: 17 scientists 91 D-Index: 7 scientists 92 D-Index: 14 scientists 93 D-Index: 9 scientists 94 D-Index: 15 scientists 95 D-Index: 10 scientists 96 D-Index: 12 scientists 97 D-Index: 10 scientists 98 D-Index: 10 scientists 99 D-Index: 12 scientists 100 D-Index: 16 scientists 101 D-Index: 5 scientists 102 D-Index: 7 scientists 103 D-Index: 7 scientists 104 D-Index: 8 scientists 105 D-Index: 9 scientists 106 D-Index: 13 scientists 107 D-Index: 4 scientists 108 D-Index: 5 scientists 109 D-Index: 10 scientists 110 D-Index: 8 scientists 111+ D-Index: 96 scientists
30 D-Index 111+

This scientist: 57 D-Index — 72nd percentile

72% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 111 D-Index or more.

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
Yingchen Zhang
Yingchen Zhang National Renewable Energy Laboratory
Innocent Kamwa
Innocent Kamwa Université Laval
Bikash C. Pal
Bikash C. Pal Imperial College London

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

For students in Electronics and Electrical Engineering, exploring related online degrees can expand career opportunities and skill sets. Many professionals seek certificate programs that pay well to quickly boost their credentials and improve earning potential without committing to lengthy degree programs.

Additionally, some career paths in engineering appeal especially to introverts due to the nature of the work. The resource on high paying jobs for introverts highlights roles that involve independent problem solving and technical expertise, which are common in this field.

Leadership and management skills are also valuable in engineering careers. For those interested in advancing into supervisory roles, earning a fast track project management degree online offers a flexible way to develop these skills efficiently.

Moreover, pursuing a bachelor's degree in project management can complement an engineering background by enhancing leadership capabilities and project oversight expertise, preparing graduates for diverse and rewarding career pathways.

Best Scientists Citing Junbo Zhao

Trending Scientists

Recently Published Articles