Assistant Professor, Electrical and Computer Engineering
| junbo@uconn.edu | |
| Mailing Address | Electrical and Computer Engineering 371 Fairfield Way U-4157 Storrs, Connecticut 06269-4157 |
| Campus | Storrs |
| Link | Affiliation Website |
| Google Scholar Link | |
Brief Bio
Dr. Junbo Zhao is the director of DOE Northeast University Cybersecurity Center for Advanced and Resilient Energy Delivery (CyberCARED) and Castleman Term Professor in Engineering Innovation at the Department of Electrical and Computer Engineering at the University of Connecticut. He is also a Research Scientist at the National Renewable Energy Laboratory. He was an Associate Director of the Eversource Energy Center for Grid Modernization and Strategic Partnerships from 2022 to 2024. He was also an assistant professor and research assistant professor at Mississippi State University and Virginia Tech from 2019-2021 and 2018-2019, respectively. He received his Ph.D. from the Bradley Department of Electrical and Computer Engineering at Virginia Tech in 2018. His advisor is Prof. Lamine Mili (IEEE Life Fellow). He did a summer internship at Pacific Northwest National Laboratory in 2017.
He is the Principal Investigator for a multitude of projects funded by the National Science Foundation, the Department of Energy, DoD ESTCP, National Laboratories, Eversource Energy, Dominion Energy, Avangrid, ISO New England, etc. He is now leading multiple IEEE PES technical activities, such as the founding chair of the IEEE PES Working Group on Distribution System Behind-The-Meter DERs: Visibility, Analytics, and Control, chair of the IEEE Task Force on Cyber-Physical Interdependency for Power System Operation and Control, co-chair of the IEEE Working Group on Power System Static and Dynamic State Estimation, the secretary of the IEEE PES Distribution System Analysis Subcommittee, and IEEE PES Renewable Systems Integration Coordinating Committee. He has published 4 chapters and more than 200 peer-reviewed journal and conference papers. He serves as the Associate Editor of IEEE Transactions on Power Systems, IEEE Transactions on Industry Applications, International Journal of Electrical Power & Energy Systems, North America Regional Editor of the IET Renewable Power Generation, and subject editor of IET Generation, Transmission & Distribution. He was the associate editor of IEEE Transactions on Smart Grid and IEEE Transactions on Network Science and Engineering. He has been listed as the 2020-2024 World’s Top 2% Scientists released by Stanford University in both Single-Year and Career tracks.
He is the receipt of multiple recognitions, such as the Best Paper Awards from 2020-2024 IEEE PES General Meeting (10 papers), 2022 IEEE Conference on Energy Internet and Energy System Integration, 2019 and 2022 IEEE PES ISGT Asia, 2021 IEEE Sustainable Power and Energy Conference, IEEE I&CPS Asia 2021 and 2023, and the 2020 Journal of Modern Power Systems and Clean Energy, 2022 and 2023 Outstanding Associate Editor of IEEE Transactions on Power Systems, Top 3 Associate Editor of IEEE Transactions Smart Grid in 2020, the 2020 and 2022 IEEE PES Chapter Outstanding Engineer Award, the 2021 and 2022 Best Paper of IEEE Transactions on Power Systems (3 papers), the 2023 Best Paper Awards from International Journal of Electrical Power & Energy Systems, the 2021 IEEE PES Outstanding Volunteer Award, the 2022 and 2024 IEEE PES Technical Committee Working Group Recognition Award for Outstanding Technical Report (3 awards), 2023 AAUP Research Excellence Award-Early Career, 2022 IEEE PES Outstanding Young Engineer Award (Society Level for one per year worldwide), 2023-2025 IEEE PES Technical Committee Prize Paper Award (3 awards), 2023 IEEE PES Working Group Recognition Award for Outstanding Technical Report (Society Level), 2024 Best Paper Award from IEEE Transactions on Smart Grid, 2024 Early-Career Research Fellowship of National Academies of Sciences, Engineering, and Medicine, 2024 IEEE PES Technical Committee Distinguished Individual Service Award and 2025 NSF CAREER Award. He is a Fellow of IET and the Editor-in-Chief of the International Journal of Electrical Power & Energy Systems (Impact Factor: 5 and JCR Q1).
- Cyber-Physical Power System Learning and Cybersecurity
- Robust Statistical Signal Processing
- Power System Estimation and Dynamics
- Power System Modeling, Operation, Resilience, Stability, and Control
- ECE 5512 Power Distribution (Spring 2023)
- ECE 5510 Power System Analysis (Fall 2022)
- ECE 8990/6095 Machine Learning for Power Systems (Spring 2021/2022)
- ECE 8623 Stability and Control of Power Systems (Fall 2020-MSU)
- ECE 8673 Computer Methods for Power System Analysis (Spring 2020-MSU)
- ECE 3413 Introduction to Electronic Circuits (Fall 2019)
- A. Selim, J. B. Zhao, “Load-Altering Attacks Mitigation using Bayesian-enhanced Game Theory in Active Distribution Networks,” IEEE Trans. Industry Applications, 2025.
- T. Su, T. Wu, J. B. Zhao, A. Scaglione, X. Le, “A Review of Safe Reinforcement Learning Methods for Modern Power Systems,” Proceedings of the IEEE, 2025.
- T. Su, J. B. Zhao, Y. Yao, A. Selim, D. Fei, “Safe Reinforcement Learning-Based Transient Stability Control for Islanded Microgrids with Topology Reconfiguration,” IEEE Trans. Smart Grid, 2025.
- H. Wang, Y. Yao, J. B. Zhao, F. Ding, “Data-Driven Mean-Corrected Recursive Estimation-based Optimal DER Dispatch for Distribution System Voltage Control,” IEEE Trans. Sustainable Energy, 2025.
- J. Zhang, J. B. Zhao, G. Cheng, A. Rouhani, X. Chen, “Explainable Multi-Fidelity Bayesian Neural Network for Distribution System State Estimation,” Applied Energy, vol 392, 2025.
- B. Tan, J. B. Zhao, N. Chiang, N. Duan, “High-Dimension Bayesian Parameter Estimation for WECC Composite Load Model using Realistic Event Measurements“, IEEE Trans. Power Systems, 2025.
- I. Zografopoulos, A. Srivastava, C. Konstantinou, J. B. Zhao, et. al, “Cyber-Physical Interdependence for Power System Operation and Control,” IEEE Trans. Smart Grid, vol. 16, no. 3, pp. 2554-2573, 2025.
- B. Tan, J. B. Zhao, “Bayesian Post-Fault Power System Dynamic Trajectory Prediction,” IEEE Trans. Power Systems, 2025.
- A. Selim, Z. Yan, J. B. Zhao, B. Yang, “Scalable Volt-VAR Optimization using RLlib- IMPALA Framework: A Reinforcement Learning Approach for Solar-Powered Grids,” Solar Energy, vol. 288, 2025.
- Z. Chen, H. Liu, J. B. Zhao, T. Bi, “Small-sample Event Identification Based on Adaptive 2nd-order MDF and Triplet CNNs using Distribution-Level Synchronized Measurements,” IEEE Trans. Smart Grid, vol. 16, no. 1, pp. 223-235, 2025.
- T. Su, J. B. Zhao, Y. Pei, Y. Yao, F. Ding, “Analytic Neural Network Gaussian Process Enabled Chance-Constrained Voltage Regulation for Active Distribution Systems with PVs, Batteries and EVs,” IEEE Trans. Power Systems, vol. 40, no. 3, pp. 2037-2049, 2025.
- B. Tan, J. B. Zhao, Y. Chen, “Scalable Risk Assessment of Rare Events in Power Systems with Uncertain Wind Generation and Loads,” IEEE Trans. Power Systems, vol. 40, no. 2, pp. 1374-1388, 2025.
- A. Selim, J. B. Zhao, B. Yang, “Large Language Model for Smart Inverter Cyber-Attack Detection via Textual Analysis of Volt/VAR Commands,” IEEE Trans. Smart Grid, vol. 15, no. 6, pp. 6179-6182, 2024 (Top 13 hot Paper since Jan 2025).
- H. Wang, Y. Liang, Y. Yao, J. B. Zhao, F. Ding, “Online Model-Free DER Dispatch via Adaptive Voltage Sensitivity Estimation and Chance Constrained Programming,” IEEE Trans. Power Systems, vol. 39, no. 6, pp. 7318-7330, 2024.
- Y. Pei, K. Ye, J. B. Zhao, Y. Yao, T. Su, F. Ding, “Visibility Enhanced Model-Free Deep Reinforcement Learning Algorithm for Voltage Control in Realistic Distribution Systems Using Smart Inverters,” Applied Energy, vol. 372, no. 123758, 2024.
- S. Allahmoradi, S. Afrasiabi, X. Liang, J. B. Zhao, M. Shahidehpour, “Data-Driven Volt/VAR Optimization for Modern Distribution Networks: A Review,” IEEE Access, vol. 12, pp. 71184-71204, 2024.
- T. Su, J. B. Zhao, X. Chen, “Deep Sigma Point Processes-Assisted Chance-Constrained Power System Transient Stability Preventive Control,” IEEE Trans. Power Systems, vol. 39, no. 1, pp. 1965-1978, 2024.
- B. Tan, J. B. Zhao, D. Nan, “Amortized Bayesian Parameter Estimation Approach for WECC Composite Load Model,” IEEE Trans. Power Systems, vol. 39, no. 1, pp. 1517-1529, 2024.
- D. Cao, J. B. Zhao, W. Hu, Y. Pei, Z. Chen, Q. Huang, “Physics-informed Graphical Representation-enabled Deep Reinforcement Learning for Robust Distribution System Voltage Control,” IEEE Trans. Smart Grid, vol. 15, no. 1, pp. 233-246, 2024. (2024 IEEE Transactions on Smart Grid Best Paper Award-Second Place.pdf, Highly Cited Paper according to Web of Science since September 2024).
- J. Ling, Z. Yang, J. B. Zhao, J. Yu, “Modular Linear Power Flow Model Against Large Fluctuations,” IEEE Trans. Power Systems, vol. 39, no. 1, pp. 402-415, 2024.
- M. Gao, J. Yu, Z. Yang, J. B. Zhao, “A Physics-Guided Graph Convolution Neural Network for Optimal Power Flow,” IEEE Trans. Power Systems, vol. 39, no. 1, pp. 380-390, 2024 (Hot Paper according to Web of Science since May 2024-in the top 0.1% of papers in the field within the past two years).
- K. Ye, J. B. Zhao, H. Li, M. Gu, “A High Computationally Efficient Parallel Partial Gaussian Process for Large-scale Power System Probabilistic Transient Stability Assessment,” IEEE Trans. Power Systems, vol. 39, n0. 2, pp. 4650-4660, 2024.
- A. Selim, J. B. Zhao, F. Ding, F. Miao, S. Park, “Adaptive Deep Reinforcement Learning Algorithm for Distribution System Cyber Attack Defense with High Penetration of DERs“, IEEE Trans. Smart Grid, vol. 15, no. 4, pp. 4077-4089, 2024.
- Y. Feng, V. Levi, D. Ćetenović, J. B. Zhao, P. Taylor, V. Terzija, “Primal-Dual Decomposed State Estimation for Multi-Energy Systems Leveraging Variational Bayesian Approximation,” IEEE Trans. Smart Grid, vol. 15, no. 3, pp. 2696-2709, 2024.
- J. Zhang, J. B. Zhao, J. Yang, J. H. Zhao, “Deep Multi-fidelity Bayesian Data Fusion for Probabilistic Distribution System Voltage Estimation with High Penetration of PVs,” IEEE Trans. Power Systems, vol. 39, no. 2, pp. 3661-3672, 2024.
- X. Lei, Z. Yang, J. B. Zhao, J. Yu, W. Li, “Surrogate Formulation for Chance-Constrained DC Optimal Power Flow with Affine Control Policy,” IEEE Trans. Power Systems, vol. 39, no. 6, pp. 7417-7420, 2024.
- M. Gao, J. Yu, Z. Yang, J. B. Zhao, “Physics Embedded Graph Convolution Neural Network for Power Flow Calculation Considering Uncertain Injections and Topology,” IEEE Trans. Neural Networks and Learning Systems, vol. 35, no. 11, pp. 15467-15478, 2024.
- D. Cao, J. B. Zhao, W. Hu, N. Yu, Z. Chen, Q. Huang, “Physics-informed Graphical Learning and Bayesian Averaging for Robust Distribution State Estimation,” IEEE Trans. Power Systems, vol. 39, no. 2, pp. 2879-2892, 2024. (Highly Cited Paper according to Web of Science since July 2024).
- Y. Pei, J. B. Zhao, Y. Yao, F. Ding, “Multi-Task Reinforcement Learning for Distribution System Voltage Control With Topology Changes,” IEEE Trans. Smart Grid, vol. 14, no. 3, pp. 2481-2484, 2023.
- T. Su, J. B. Zhao, Y. Pei, F. Ding, “Probabilistic Physics-Informed Graph Convolutional Network for Active Distribution System Voltage Prediction,” IEEE Trans. Power Systems, vol. 38, no. 6, pp. 5969-5972, 2023.
- Y. Chen, Y. Liu, J. B. Zhao, G. Qiu, H. Yin, Z. Li, “Physical-Assisted Multi-agent Graph Reinforcement Learning Enabled Fast Voltage Regulation for PV-rich Active Distribution Network,” Applied Energy, vol. 1, no. 121743, 2023.
- Z. Chen, H. Liu, J. B. Zhao, T. Bi, “Real-Time Event Detection Based on STA/LTA Method Using Field Synchrophasor Measurements,” IEEE Trans. Power Delivery, vol. 38, no. 6, pp. 4070-4080, 2023.
- G. Zhang, J. B. Zhao, W. Hu, D. Cao, I. Kamwa, N. Duan, Z. Chen, “A Multi-Agent Deep Reinforcement Learning Enabled Dual Branch Damping Controller for Multi-Mode Oscillation“, IEEE Trans. Control Systems Technology, vol. 31, no. 1, pp. 483-492, 2023.
- B. Tan, J. B. Zhao, “Debiased Uncertainty Quantification Approach for Probabilistic Transient Stability Assessment,” IEEE Trans. Power Systems, vol. 38, no. 5, pp. 4954-4957, 2023.
- Y. Liang, J. B. Zhao, D. Srinivasan, “Temporally-adaptive Robust Data-driven Sparse Voltage Sensitivity Estimation for Large-scale Realistic Distribution Systems with PVs,” IEEE Trans. Power Systems, vol. 38, no. 4, pp. 3977-3980, 2023.
- G. Chen, Y. Lin, J. Yan, J. B. Zhao, L. Bai, “Model-Measurement Data Integrity Attacks,” IEEE Trans. Smart Grid, vol. 14, no. 6, pp. 4741-4757, 2023.
- G. Zhang, J. B. Zhao, W. Hu, D. Cao, B. Tan, Q. Huang, Z. Chen, “A Novel Data-Driven Self-Tuning SVC Additional Fractional-Order Sliding Mode Controller for Voltage Stability with Wind Generations,” IEEE Trans. Power Systems, vol. 38, no. 6, pp. 5755-5767, 2023.
- B. Tan, J. B. Zhao, L. Xie, “Transferable Deep Kernel Emulator for Probabilistic Load Margin Assessment With Topology Changes, Uncertain Renewable Generations and Loads,” IEEE Trans. Power Systems, vol. 38, no. 6, pp. 5740-5754, 2023.
- Y. Liang, J. B. Zhao, D. Kumar, K. Ye, D. Srinivasan, “Robust Data-driven Sparse Estimation of Distribution Factors Considering PMU Data Quality and Renewable Energy Uncertainty – Part I: Theory,” IEEE Trans. Power Systems, vol. 38, no. 5, pp. 4800-4812, 2023.
- Y. Liang, J. B. Zhao, D. Kumar, K. Ye, D. Srinivasan, “Robust Data-driven Sparse Estimation of Distribution Factors Considering PMU Data Quality and Renewable Energy Uncertainty – Part II: Scalability and Applications,” IEEE Trans. Power Systems, vol. 38, no. 5, pp. 4813-4825, 2023.
- K. Ye, J. B. Zhao, N. Duan, Y. Zhang, “Physics-Informed Sparse Gaussian Process for Probabilistic Stability Analysis of Large-Scale Power System with Dynamic PVs and Loads,” IEEE Trans. Power Systems, vol. 38, no. 3, pp. 2868-2879, 2023. (2023 IEEE PES Technical Committee Prize Paper Award)
- R. Fan, R. Huang, S. Wang, J. B. Zhao, “Wavelet and Deep-Learning-Based Approach for Generation System Problematic Parameters Identification and Calibration,” IEEE Trans. Power Systems, vol. 38, no. 4, pp. 3787-3798, 2023.
- Y. Liu, P. Ren, J. B. Zhao, J. Liu, “Real-time Topology Estimation for Active Distribution System using Graph-bank Tracking Bayesian Networks,” IEEE Trans. Industrial Informatics, vol. 19, no. 4, pp. 6127-6137, 2023.
- D. Cao, J. B. Zhao, W. Hu, Z. Chen, “Topology Change Aware Data-Driven Probabilistic Distribution State Estimation Based on Gaussian Process,” IEEE Trans. Smart Grid, vol. 14, no. 2, pp. 1317-1320, 2023.
- K. Ye, J. B. Zhao, N. Duan, D. Maldonado, “Stochastic Power System Dynamic Simulation and Stability Assessment Considering Dynamics from Correlated Loads and PVs,” IEEE Trans. Industry Applications, vol. 58, no. 6, pp. 7764-7775, 2022.
- Y. Liu, Z. Li, J. B. Zhao, “Robust Data-Driven Linear Power Flow Model With Probability Constrained Worst-Case Errors,” IEEE Trans. Power Systems, vol. 37, no. 5, pp. 4113-4116, 2022.
- K. Ye, J. B. Zhao, H. Zhang, Y. Zhang, “Data-Driven Probabilistic Voltage Risk Assessment of MiniWECC System with Uncertain PVs and Wind Generations using Realistic Data,” IEEE Trans. Power Systems, vol. 37, no. 5, pp. 4121-4124, 2022.
- H. Liu, S. Liu, J. B. Zhao, T. Bi, X. Yu, “Dual-Channel Convolutional Network-Based Fault Cause Identification for Active Distribution System Using Realistic Waveform Measurements,” IEEE Trans. Smart Grid, vol. 13, no. 6, pp. 4899-4908, 2022.
- Q. Li, J. Zhang, J. B. Zhao, X. Ye, W. Song, F. Li, “Adaptive Hierarchical Cyber Attack Detection and Localization in Active Distribution Systems,” IEEE Trans. Smart Grid, vol. 13, no. 3, pp. 2369-2380, 2022.
- X. Lei, Z. Yang, J. B. Zhao, J. Yu, “Data-Driven Assisted Chance-Constrained Energy and Reserve Scheduling with Wind Curtailment,” Applied Energy, vol. 321, no. 1, 2022.
- P. Wang, J. B. Zhao, D. Srinivasan, S. Zou, G. Wang, Z. Ma, “Data-driven Energy Management in Residential Areas Leveraging Demand Response,” Energy & Buildings, 2022.
- C. Chen, M. Cui, J. B. Zhao, W. Bi, X. Zhang, “Data-Driven Detection of Stealthy False Data Injection Attack Against Power System State Estimation,” IEEE Trans. Industrial Informatics, vol. 18, no. 12, pp. 8467-8476, 2022.
- D. Cao, J. B. Zhao, W. Hu, Y. Zhang, Z. Chen, F. Blaabjerg, “Robust Deep Gaussian Process Probabilistic Electrical Load Forecasting Method Under Anomalous Conditions,” IEEE Trans. Industrial Informatics, vol. 18, no. 2, pp. 1142-1153, 2022.
- T. Su, Y. Liu, J. B. Zhao, J. Liu, “Deep Belief Network Enabled Surrogate Modeling for Fast Preventive Control of Power System Transient Stability,” IEEE Trans. Industrial Informatics, vol. 18, no. 1, pp. 315-326, 2022.
- Z. Li, H. Liu, J. B. Zhao, T. Bi, Q. Yang, “A Power System Disturbance Classification Method Robust to PMU Data Quality Issues,” IEEE Trans. Industrial Informatics, vol. 18, no. 1, pp. 130-142, 2022.
- R. Yohanandhan, R. Madurai Elavarasan, R. Pugazhendhi, P. Manoharan, L. Mihet-Popa, J. B. Zhao, V. Terzija, “A Specialized Review on Outlook of Future Cyber-Physical Power System (CPPS) Testbeds for Securing Electric Power Grid,” International Journal of Electrical Power & Energy Systems, vol. 136, no. 107720, 2022.
- G. Chen, Y. Lin, J. B. Zhao, J. Yan, “A Highly Discriminative Detector against False Data Injection Attacks in AC State Estimation,” IEEE Trans. Smart Grid, vol. 13, no. 3, pp. 2318-2330, 2022.
- D. Cao, J. B. Zhao, W. Hu, F. Ding, N. Yu, Q. Huang, Z. Chen, F. Blaabjerg, “Model-Free Voltage Control of Active Distribution System with PVs using Surrogate Model-based Deep Reinforcement Learning,” Applied Energy, vol. 306, no. 117982, 2022.
- J. Liu, Z. Yang, J. B. Zhao, J. Yu, B. Tan, W. Li, “Explicit Data-Driven Small-Signal Stability Constrained Optimal Power Flow,” IEEE Trans. Power Systems, vol. 37, no. 5, pp. 3726-3737, 2022.
- D. Cao, J. B. Zhao, W. Hu, N. Yu, F. Ding, Q. Huang, Z. Chen, “Deep Reinforcement Learning Enabled Physical-Model-Free Two-Timescale Voltage Control Method for Active Distribution Systems,” IEEE Trans. Smart Grid, vol. 13, no. 1, pp. 149-165, 2022.
- S. Khazeiynasab, J. B. Zhao, I. Batarseh, B. Tan, “Power Plant Model Parameter Calibration Using Conditional Variational Autoencoder“, IEEE Trans. Power Systems, vol. 37, no. 2, pp. 1642-1652, 2022.
- J. Khazaei, M. Alkaf, J. B. Zhao, “Convex Optimization of Cyberattacks Overflowing Multiple Lines in Cyber-Physical Power Systems,” IEEE System Journal, vol. 16, no. 4, pp. 5224-5233, 2022.
- C. Chen, X. Zhang, K. Zhang, M. Cui, J. B. Zhao, F. Li, “Stability Assessment of Secondary Frequency Control System With Dynamic False Data Injection Attacks“, IEEE Trans. Industrial Informatics, vol. 18, no. 5, pp. 3224-3234, 2022.
- H. Song, Y. Liu, J. B. Zhao, J. Liu, G. Wu, “Prioritized Replay Dueling DDQN Based Grid-Edge Control of Community Energy Storage System,” IEEE Trans. Smart Grid, vol. 12, no. 6, pp. 4950-4961, 2021.
- K. Ye, J. B. Zhao, F. Ding, R. Yang, C. Xiao, G. Dobbins, “Global Sensitivity Analysis of Large Distribution System with PVs using Deep Gaussian Process,” IEEE Trans. Power Systems, vol. 36, no. 5, pp. 4888-4891, 2021.
- D. Cao, J. B. Zhao, W. Hu, F. Ding, Q. Huang, Z. Chen, F. Blaabjerg, “Data-Driven Multi-agent Deep Reinforcement Learning for Distribution System Decentralized Voltage Control with High Penetration of PVs,” IEEE Trans. Smart Grid, vol. 12, no. 5, pp. 4137-4150, 2021.
- K. Ye, J. B. Zhao, C. Huang, N. Duan, Y. Zhang, T. Field, “A Data-Driven Global Sensitivity Analysis Framework for Three-Phase Distribution System with PVs,” IEEE Trans. Power Systems, vol. 36, no. 5, pp. 4809-4819, 2021.
- Z. Li, H. Liu, J. B. Zhao, T. Bi, Q. Yang, “Fast Power System Event Identification using Enhanced LSTM Network with Renewable Energy Integration,” IEEE Trans. Power Systems, vol. 36, no. 5, pp. 4492-4502, 2021.
- X. Wang, Y. Liu, J. B. Zhao, C. Liu, J. Liu, J. Yan, “Surrogate Model Enabled Deep Reinforcement Learning for Hybrid Energy Community Operation,” Applied Energy, vol. 289, no. 116722, 2021.
- G. Zhang, W. Hu, J. B. Zhao, D. Cao, Z. Chen, F. Blaabjerg, “A Novel Deep Reinforcement Learning Enabled Multi-band PSS for Multi-Mode Oscillation Control,” IEEE Trans. Power Systems, vol. 36, no. 4, pp. 3794-3797, 2021.
- C. Chen, Y. Chen, J. B. Zhao, K. Zhang, M. Ni, B. Ren, “Data-Driven Resilient Automatic Generation Control Against False Data Injection Attacks“, IEEE Trans. Industrial Informatics, vol. 17, no. 2, pp. 8092-8101, 2021.
- D. Cao, J. B. Zhao, W. Hu, F. Ding, Q. Huang, Z. Chen, “Attention Enabled Multi-agent DRL for Decentralized Volt-VAR Control of Active Distribution System Using PV Inverters and SVCs,” IEEE Trans. Sustainable Energy, vol. 12, no. 3, pp. 1582-1592, 2021.
- Q. Gao, Y. Liu, J. B. Zhao, J. Liu, C. Y. Chung, “Hybrid Deep Learning for Dynamic Total Transfer Capability Control“, IEEE Trans. Power Systems, vol. 36, no. 3, pp. 2733-2736, 2021.
- X. Lei, Z. Yang, J. Yu, J. B. Zhao, Q. Gao, H. Yu, “Data-driven Optimal Power Flow: A Physics-Informed Machine Learning Approach,” IEEE Trans. Power Systems, vol. 36, no. 1, pp. 346-354, 2021. (2022 IEEE Transactions on Power Systems Best Paper)
- J. Li, D. Deng, J. B. Zhao, et. al, “A novel hybrid short-term load forecasting method of smart grid using MLR and LSTM neural network,” IEEE Transactions on Industrial Informatics, vol. 17, no. 4, pp. 2443-2452, 2021. (Highly Cited Paper according to Web of Science since March 2023)
- L. Zhao, Y. Liu, J. B. Zhao, Y. Zhang, L. Xu, J. Liu, “Robust PCA-Deep Belief Network Surrogate Model for Distribution System Topology Identification with DERs“, International Journal of Electrical Power & Energy Systems, vol. 125 (106441), 2021.
- T. Ding, Z. Zeng, B. Qin, J. B. Zhao, Y. Yang, F. Blaabjerg, and Z. Dong, “Quantifying Cyber Attacks on Industrial MMC-HVDC Control System Using Structured Pseudospectrum,” IEEE Trans. Power Electronics, vol. 36, no. 5, pp. 4915-4920, 2021.
- H. Liu, Y. Qi, J. B. Zhao, T. Bi, “Data-Driven Subsynchronous Oscillation Identification Using Field Synchrophasor Measurements,” IEEE Trans. Power Delivery, vol. 37, no. 1, pp. 165-175, 2022.
- T. Su, Y. Liu, J. B. Zhao, J. Liu, “Probabilistic Stacked Denoising Autoencoder for Power System Transient Stability Prediction with Wind Farms“, IEEE Trans. Power Systems, vol. 36, no. 4, pp. 3786-3789, 2021.
- G. Qiu, Y. Liu, J. B. Zhao, J. Liu, L. Wang, T. Liu, H. Gao, “Analytic Deep Learning-based Surrogate Model for Operational Planning with Dynamic TTC Constraints,” IEEE Trans. Power Systems, vol. 36, no. 4, pp. 3507-3519, 2021.
- D. Cao, W. Hu, J. B. Zhao, G. Zhang, B. Zhang, Z. Liu, Z. Chen, F. Blaabjerg, “Reinforcement Learning and its Applications in Modern Power and Energy Systems: A Review,” Journal of Modern Power Systems and Clean Energy, vol. 8, no. 6, pp. 1029-1042, 2020 (2020 Best Paper Award and ESI Highly Cited Paper-May 2024-present)
- D. Cao, W. Hu, J. B. Zhao, Q. Huang, Z. Chen, F. Blaabjerg, “A multi-agent deep reinforcement learning based voltage regulation using coordinated PV inverters,” IEEE Trans. Power Systems, vol. 35, no. 5, pp. 4120-4123, 2020.
- Y. Liu, J. B. Zhao, X. Liu, Tingjian Liu, Gao Qiu, J. Liu, ”Online TTC Estimation using Nonparametric Analytics Considering Wind Energy Integration,” IEEE Trans. Power Systems, vol. 34, no. 1, pp. 494-505, 2019.
- L Che, X Liu, Z Shuai, J. B. Zhao, “The Impact of Ramp-Induced Data Attacks on Power System Operational Security,” IEEE Trans. Industrial Informatics, vol. 15, no. 9, pp. 5064-5075, 2019.
- B. Li, T. Ding, C. Huang, J. B. Zhao, Yongheng Yang, Ying Chen, “Detecting False Data Injection Attacks Against Power System State Estimation with Fast Go-Decomposition (GoDec) Approach,” IEEE Trans. Industrial Informatics, vol. 15, no. 5, pp. 2892-2904, 2019.
- J. B. Zhao, L. Mili, M. Wang, “A Generalized False Data Injection Attacks Against Power System Nonlinear State Estimator and Countermeasures,” IEEE Trans. Power Systems, vol. 33, no. 5, pp. 4868-4877, 2018.
- X. Wei, J. B. Zhao, T. Huang, E. Bompard, “A Novel Cascading Faults Graph Based Transmission Network Vulnerability Assessment Method,” IEEE Trans. Power Systems, vol. 33, no. 3, pp. 2995-3000, 2018.
- J. B. Zhao, G. X. Zhang, R. Jabr, “Robust Detection of Cyber Attacks on State Estimators Using Phasor Measurements“, IEEE Trans. Power Systems, vol. 32, no. 3, pp. 2468-2470, 2017.
- J. B. Zhao, G. X. Zhang, M. La Scala, Z. Dong, C. Chen, J. Wang, “Short-Term State Forecasting-Aided Method for Detection of Smart Grid General False Data Injection Attacks“, IEEE Trans. Smart Grid, vol. 8, no. 4, pp. 1580-1590, 2017. (One of the most popular papers on July, August 2017)
- J. B. Zhao, G. X. Zhang, Z.Y.Dong, K.P. Wong, ” Forecasting-Aided Imperfect False Data Injection Attacks Against Power System Nonlinear State Estimation“, IEEE Trans. Smart Grid, vol. 7, no. 1, pp. 6-8, 2016.