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Jun Zhang

Also published under: Jun J. Zhang, Jun Jason Zhang, J. J. Zhang

Publications
131
Citations
2,323
Publications by Year
20072024
Co-Authors:
Show All Co-Authors (233)
Image of author Jun Zhang

Jun Zhang

Also published under: Jun J. Zhang, Jun Jason Zhang, J. J. Zhang

Affiliation

School of Electrical Engineering and Automation
Wuhan University
Wuhan, China

Publication Topics

Biography

Jun Jason Zhang (S’06–M’09–SM’14) received the B.E. and M.E. degrees in electrical engineering from the Huazhong University of Science and Technology, Wuhan, China, in 2003 and 2005, respectively, and the Ph.D. degree from Arizona State University, Tempe, AZ, USA, in 2008.,He has authored/co-authored over 100 peer-reviewed publications.,Dr. Zhang is an Editorial Committee Member of Acta Automatica Sinica, the IEEE Transactions on Computational Social Systems, and the IEEE Internet of Things Magazine.(Based on document published on 12 September 2018).
Publications
131
Citations
2,323
Publications by Year
20072024
Co-Authors:
Show All Co-Authors (233)
Author's Published Works

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AI4S: Artificial Intelligence Empowering the Development of New Power Systems

Wanya Yu;Haozhe Zhan;Jun Zhang;Qi Zhao;Sheng Chen;Huan Ma;Yuyang Bai;Tianlu Gao;Xinying Wang

2024 4th International Conference on Intelligent Power and Systems (ICIPS)
Year: 2024 | Conference Paper |
With the continuous growth of global energy demand and the transformation of the energy mix, the power system is facing unprecedented challenges, including the integration of renewable energy, the intelligent upgrading of the grid, and the enhancement of power supply reliability. Against this backdrop, this paper discusses the challenges faced by the power system with the integration of renewable ...Show More

AI4S: Artificial Intelligence Empowering the Development of New Power Systems

Wanya Yu;Haozhe Zhan;Jun Zhang;Qi Zhao;Sheng Chen;Huan Ma;Yuyang Bai;Tianlu Gao;Xinying Wang

Year: 2024 | Conference Paper |

Research on Quantitative Evaluation Methods for Artificial Intelligence in Electric Power

Yun Zhang;Haozhe Zhan;Jun Zhang;Qi Zhao;Xiao Han;Huan Ma;Tianyun Zhang;Zeyu Mu;Xinying Wang

2024 4th International Conference on Intelligent Power and Systems (ICIPS)
Year: 2024 | Conference Paper |
The advancement of artificial intelligence technology offers a novel approach to addressing the complexities inherent in electricity regulation. As the application of artificial intelligence technology in power regulation continues to expand, there is a growing demand for comprehensive evaluations of this technology's capabilities. In light of this context, this paper introduces an objective quant...Show More

Research on Quantitative Evaluation Methods for Artificial Intelligence in Electric Power

Yun Zhang;Haozhe Zhan;Jun Zhang;Qi Zhao;Xiao Han;Huan Ma;Tianyun Zhang;Zeyu Mu;Xinying Wang

Year: 2024 | Conference Paper |

Research on Active Power Corrective Control of the Power System Based on Deep Reinforcement Learning Model

Ke Zhang;Yangwu Shen;Yuting Wang;Jun Jason Zhang

2024 IEEE 4th International Conference on Digital Twins and Parallel Intelligence (DTPI)
Year: 2024 | Conference Paper |
The application of artificial intelligence (AI) technology in active power corrective control (APCC) within power systems can significantly enhance decision-making efficiency, high-dimensional data processing capability, and the intelligence level of the power grid. By establishing mechanisms for safety operation decision support analysis and autonomous knowledge learning, AI can improve the power...Show More

Research on Active Power Corrective Control of the Power System Based on Deep Reinforcement Learning Model

Ke Zhang;Yangwu Shen;Yuting Wang;Jun Jason Zhang

Year: 2024 | Conference Paper |

Photovoltaic Power Generation Forecasting Based on TCN-Transformer Model

Linhan Wu;Jizhou Yu;Yuxin Dai;Tianlu Gao;Jun Zhang

2024 5th International Conference on Artificial Intelligence and Electromechanical Automation (AIEA)
Year: 2024 | Conference Paper |
This paper proposes a TCN-Transformer hybrid model based on the Temporal Convolutional Network (TCN) and the Transformer model to enhance the accuracy of photovoltaic (PV) power forecasting. Given the volatility and randomness of photovoltaic power generation, accurate power prediction is crucial for the scheduling and stability of the power system. The model combines TCN’s sensitivity in capturin...Show More

Photovoltaic Power Generation Forecasting Based on TCN-Transformer Model

Linhan Wu;Jizhou Yu;Yuxin Dai;Tianlu Gao;Jun Zhang

Year: 2024 | Conference Paper |

High-Dimensional Steady-State Security Region Boundary Approximation in Power Systems Using Feature Non-Linear Converter and Improved Oblique Decision Tree

Yuxin Dai;Jun Zhang;Peidong Xu;Tianlu Gao;David Wenzhong Gao

Journal of Modern Power Systems and Clean Energy
Year: 2024 | Volume: 12, Issue: 6 | Journal Article |
The steady-state security region (SSR) offers robust support for the security assessment and control of new power systems with high uncertainty and fluctuation. However, accurately solving the steady-state security region boundary (SS-RB), which is high-dimensional, non-convex, and non-linear, presents a significant challenge. To address this problem, this paper proposes a method for approximating...Show More

High-Dimensional Steady-State Security Region Boundary Approximation in Power Systems Using Feature Non-Linear Converter and Improved Oblique Decision Tree

Yuxin Dai;Jun Zhang;Peidong Xu;Tianlu Gao;David Wenzhong Gao

Year: 2024 | Volume: 12, Issue: 6 | Journal Article |

Risk Warning of Power Grid Congestion Events Based on Probabilistic Power Flow

Haoran Zhang;Jilin Chen;Zeyu Mu;Qinxin Li;Peidong Xu;Jun Zhang

2024 7th International Conference on Energy, Electrical and Power Engineering (CEEPE)
Year: 2024 | Conference Paper |
To address power grid congestion events stemming from the uncertainty of wind power output, this paper presents a risk early warning model for power grid congestion events based on probability power flow. Employing the deep mixture density network algorithm to compute probability power flow (PPF) enables accurate early warning of power system congestion events. Experimental results conducted on th...Show More

Risk Warning of Power Grid Congestion Events Based on Probabilistic Power Flow

Haoran Zhang;Jilin Chen;Zeyu Mu;Qinxin Li;Peidong Xu;Jun Zhang

Year: 2024 | Conference Paper |

Real-Time Robust State Estimation for Large-Scale Low-Observability Power-Transportation System Based on Meta Physics-Informed Graph TimesNet

Shijie Li;Weijian Li;Lulu Chen;Huaiguang Jiang;Jun Zhang;David Wenzhong Gao

IEEE Transactions on Smart Grid
Year: 2024 | Volume: 15, Issue: 6 | Journal Article |
Cited by: Papers (1)
The ever-increasing penetration of Electric Vehicles (EVs) has emerged as an evident challenge within the Power Distribution System (PDS). Existing Distribution System State Estimation (DSSE) models are constrained by the low-observability and often neglect the spatio-temporal correlations inherent in the PDS. To address this, our study proposes a robust learning architecture named meta physics-in...Show More

Real-Time Robust State Estimation for Large-Scale Low-Observability Power-Transportation System Based on Meta Physics-Informed Graph TimesNet

Shijie Li;Weijian Li;Lulu Chen;Huaiguang Jiang;Jun Zhang;David Wenzhong Gao

Year: 2024 | Volume: 15, Issue: 6 | Journal Article |

High Wind Power Penetration System Power Flow Off-Limit Early Warning Based on GCN and Rule Guidance

Zyu Mu;Jian Xu;Jun Zhang;Siyuan Chen;Riqi Si;Qining Wang

2023 IEEE 7th Conference on Energy Internet and Energy System Integration (EI2)
Year: 2023 | Conference Paper |
With the large-scale grid connection of new energy sources, the possibility of local power flow off-limit evolving into a global security risk has significantly increased, and the risk of power flow off-limit faults in the new transmission network is also increasing. However, traditional physical models for calculating power flow off-limit face issues such as slow calculation speed, high complexit...Show More

High Wind Power Penetration System Power Flow Off-Limit Early Warning Based on GCN and Rule Guidance

Zyu Mu;Jian Xu;Jun Zhang;Siyuan Chen;Riqi Si;Qining Wang

Year: 2023 | Conference Paper |

Wind Power Scenario Generation Based on Denoising Diffusion Probabilistic Model

Chenglong Xu;Yuxin Dai;Peidong Xu;Tianlu Gao;Jun Zhang

2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC)
Year: 2023 | Conference Paper |
Cited by: Papers (1)
The intermittency and randomness of wind power output have a negative impact on the stable operation of the power grid. Accurately modeling the uncertainty of wind power output is essential, and the primary method to achieve this is through scenario generation. Traditional scenario generation methods suffer from limitations such as low accuracy and high computational complexity. In this paper, a n...Show More

Wind Power Scenario Generation Based on Denoising Diffusion Probabilistic Model

Chenglong Xu;Yuxin Dai;Peidong Xu;Tianlu Gao;Jun Zhang

Year: 2023 | Conference Paper |

Cascading Fault Early Warning and Location Method of Transmission Networks Based on Wide Area Time-Series Power System State

Zeyu Mu;Peidong Xu;Ke Zhang;Tianlu Gao;Jun Zhang

IEEE Journal of Radio Frequency Identification
Year: 2023 | Volume: 7 | Journal Article |
Cited by: Papers (7)
With the promotion of energy security strategy and the access of the high penetration of renewable energy, the related methods of situational awareness in the traditional transmission network may be inapplicable for modern power grids. For the early warning and location of the cascading faults caused by power flow shift, a novel method for security situation prediction in the transmission network ...Show More

Cascading Fault Early Warning and Location Method of Transmission Networks Based on Wide Area Time-Series Power System State

Zeyu Mu;Peidong Xu;Ke Zhang;Tianlu Gao;Jun Zhang

Year: 2023 | Volume: 7 | Journal Article |

HackGAN: Harmonious Cross-Network Mapping Using CycleGAN With Wasserstein–Procrustes Learning for Unsupervised Network Alignment

Linyao Yang;Xiao Wang;Jun Zhang;Jun Yang;Yancai Xu;Jiachen Hou;Kejun Xin;Fei-Yue Wang

IEEE Transactions on Computational Social Systems
Year: 2023 | Volume: 10, Issue: 2 | Journal Article |
Cited by: Papers (10)
Network alignment (NA) that identifies equivalent nodes across networks is an effective tool for integrating knowledge from multiple networks. The state-of-the-art NA methods learn inter-network node similarities based on labeled anchor links, which are costly, time-consuming, and difficult to acquire. Therefore, a few unsupervised network alignment (UNA) methods propose solving NA problems withou...Show More

HackGAN: Harmonious Cross-Network Mapping Using CycleGAN With Wasserstein–Procrustes Learning for Unsupervised Network Alignment

Linyao Yang;Xiao Wang;Jun Zhang;Jun Yang;Yancai Xu;Jiachen Hou;Kejun Xin;Fei-Yue Wang

Year: 2023 | Volume: 10, Issue: 2 | Journal Article |

Design of Trans-Provincial Power Spot Trading Mechanism and Provincial Power Allocation System

Hao Zhang;Hang Zhao;Jiahao Xiong;Peidong Xu;Ke Zhang;Yuxin Dai;Haoran Zhang;Jun Zhang;Tianlu Gao

2022 IEEE 2nd International Conference on Digital Twins and Parallel Intelligence (DTPI)
Year: 2022 | Conference Paper |
With the wake of development of the market-oriented power sector reform and the gradual deepening of new energy, the energy structure and its supply-demand relationship in China are experiencing dramatic changes. Under such circumstance, the allocation of electric power and its surplus-shortage coordination are facing unprecedented challenges. However, the market trading mechanism concerning the s...Show More

Design of Trans-Provincial Power Spot Trading Mechanism and Provincial Power Allocation System

Hao Zhang;Hang Zhao;Jiahao Xiong;Peidong Xu;Ke Zhang;Yuxin Dai;Haoran Zhang;Jun Zhang;Tianlu Gao

Year: 2022 | Conference Paper |

An Artificial Intelligence Driven Method for Power System Control Based on the Cloud-Edge Collaboration Architecture

Wenchen Li;Yanhao Huang;Chunjiang He;Chenglong Xu;Peidong Xu;Tianlu Gao;Jun Zhang

2022 IEEE 2nd International Conference on Digital Twins and Parallel Intelligence (DTPI)
Year: 2022 | Conference Paper |
The new-type power system with the high penetration of renewable energy accessed is of strong uncertainty and complexity, which can be challenging for the traditional methods to control. It's significant to introduce artificial intelligence to meet the challenge. This paper proposes a cloud-edge collaborative framework based on multi-agent deep reinforcement learning for power system regulation. U...Show More

An Artificial Intelligence Driven Method for Power System Control Based on the Cloud-Edge Collaboration Architecture

Wenchen Li;Yanhao Huang;Chunjiang He;Chenglong Xu;Peidong Xu;Tianlu Gao;Jun Zhang

Year: 2022 | Conference Paper |

Evolutionary Deep Reinforcement Learning for Volt-VAR Control in Distribution Network

Ruiqi Si;Tianlu Gao;Yuxin Dai;Yuyang Bai;Yuqi Jiang;Jun Zhang

2022 IEEE 2nd International Conference on Digital Twins and Parallel Intelligence (DTPI)
Year: 2022 | Conference Paper |
Cited by: Papers (4)
As an important form of renewable energy integrated to the power system, distribution network is being challenged by voltage violation and network loss increase. Currently, model-based Vol-Var control (VVC) methods are widely used to reduce voltage violation and network loss. However, model-based methods need accurate parameters of distribution network. In practice, accurate model is difficult to ...Show More

Evolutionary Deep Reinforcement Learning for Volt-VAR Control in Distribution Network

Ruiqi Si;Tianlu Gao;Yuxin Dai;Yuyang Bai;Yuqi Jiang;Jun Zhang

Year: 2022 | Conference Paper |

Residential Short Term Load Forecasting Based on Federated Learning

Jiuxiang Chen;Tianlu Gao;Ruiqi Si;Yuxin Dai;Yuqi Jiang;Jun Zhang

2022 IEEE 2nd International Conference on Digital Twins and Parallel Intelligence (DTPI)
Year: 2022 | Conference Paper |
Cited by: Papers (3)
Load forecasting is an essential task in the power industry as an important means to assist the grid to balance supply demand. A large amount of user data monitored by smart grids can support deep learning models for load prediction, but accurate and fine-grained user data may reveal consumers' electricity consumption behaviors, which brings privacy and security issues. Federated Learning (FL) is ...Show More

Residential Short Term Load Forecasting Based on Federated Learning

Jiuxiang Chen;Tianlu Gao;Ruiqi Si;Yuxin Dai;Yuqi Jiang;Jun Zhang

Year: 2022 | Conference Paper |

Short-Term State Electricity Load Forecasting Based on Transfer-Informer

Yuqi Jiang;Yuxin Dai;Ruiqi Si;Jiuxiang Chen;Tianlu Gao;Jun Zhang

2022 IEEE 2nd International Conference on Digital Twins and Parallel Intelligence (DTPI)
Year: 2022 | Conference Paper |
Cited by: Papers (2)
The worldwide COVID-19 pandemic has caused an enormous impact on the operation mode of human society. Such sudden events bring sharp fluctuations and data inadequacy in datasets of several areas, which leads to challenges in solving related problems. Traditional deep learning models like CNN have shown relatively poor performance with small datasets during the COVID-19 pandemic. This is because th...Show More

Short-Term State Electricity Load Forecasting Based on Transfer-Informer

Yuqi Jiang;Yuxin Dai;Ruiqi Si;Jiuxiang Chen;Tianlu Gao;Jun Zhang

Year: 2022 | Conference Paper |

A Physics-Informed Deep Learning Paradigm for Transient Power Angle Stability Assessment

Xiang Li;Siyuan Chen;Jun Zhang;Jiemai Gao;Yuyang Bai

IEEE Journal of Radio Frequency Identification
Year: 2022 | Volume: 6 | Journal Article |
Cited by: Papers (3)
Transient stability assessment (TSA) plays an essential role in the safe and stable operation of power system. The traditional time-domain simulation method and direct method have great limitation in modeling and computing, which can not meet the need of fast and accurate assessment of power system stability. Therefore, combined physics model with data-driven algorithm, this paper proposes a physi...Show More

A Physics-Informed Deep Learning Paradigm for Transient Power Angle Stability Assessment

Xiang Li;Siyuan Chen;Jun Zhang;Jiemai Gao;Yuyang Bai

Year: 2022 | Volume: 6 | Journal Article |

Transient Voltage Control Based on Physics-Informed Reinforcement Learning

Jiemai Gao;Siyuan Chen;Xiang Li;Jun Zhang

IEEE Journal of Radio Frequency Identification
Year: 2022 | Volume: 6 | Journal Article |
Cited by: Papers (4)
With the continuous expansion of power grid scale and the continuous implementation of Energy Internet construction, the long-distance and large-capacity electric energy exchange between regional power grids is increasingly frequent, which makes the stability problem to a wide range of attention. Therefore, this paper proposes a transient voltage control method based on physics information and rei...Show More

Transient Voltage Control Based on Physics-Informed Reinforcement Learning

Jiemai Gao;Siyuan Chen;Xiang Li;Jun Zhang

Year: 2022 | Volume: 6 | Journal Article |

CNN-BiLSTM Short-Term Wind Power Forecasting Method Based on Feature Selection

Yufeng Chen;Hang Zhao;Rui Zhou;Peidong Xu;Ke Zhang;Yuxin Dai;Haoran Zhang;Jun Zhang;Tianlu Gao

IEEE Journal of Radio Frequency Identification
Year: 2022 | Volume: 6 | Journal Article |
Cited by: Papers (14)
Wind power has great uncertainty and short-term wind power forecasting technology can provide great help to power system scheduling after wind power integration. In this paper, a Convolutional neural network - bidirectional long and short-term memory network combination model (CNN-BiLITM) based on feature selection is proposed. Firstly, high correlation feature parameters were optimized based on e...Show More

CNN-BiLSTM Short-Term Wind Power Forecasting Method Based on Feature Selection

Yufeng Chen;Hang Zhao;Rui Zhou;Peidong Xu;Ke Zhang;Yuxin Dai;Haoran Zhang;Jun Zhang;Tianlu Gao

Year: 2022 | Volume: 6 | Journal Article |

Collective Entity Alignment for Knowledge Fusion of Power Grid Dispatching Knowledge Graphs

Linyao Yang;Chen Lv;Xiao Wang;Ji Qiao;Weiping Ding;Jun Zhang;Fei-Yue Wang

IEEE/CAA Journal of Automatica Sinica
Year: 2022 | Volume: 9, Issue: 11 | Journal Article |
Cited by: Papers (14)
Knowledge graphs (KGs) have been widely accepted as powerful tools for modeling the complex relationships between concepts and developing knowledge-based services. In recent years, researchers in the field of power systems have explored KGs to develop intelligent dispatching systems for increasingly large power grids. With multiple power grid dispatching knowledge graphs (PDKGs) constructed by dif...Show More

Collective Entity Alignment for Knowledge Fusion of Power Grid Dispatching Knowledge Graphs

Linyao Yang;Chen Lv;Xiao Wang;Ji Qiao;Weiping Ding;Jun Zhang;Fei-Yue Wang

Year: 2022 | Volume: 9, Issue: 11 | Journal Article |

A Power System Corrective Control Method Based on Evolutionary Reinforcement Learning

Haoran Zhang;Peidong Xu;Ke Zhang;Hang Zhao;Yuxin Dai;Tianlu Gao;Jun Zhang

IEEE Journal of Radio Frequency Identification
Year: 2022 | Volume: 6 | Journal Article |
Corrective control becomes more and more important for power systems due to the increasing penetration of renewable energy. As an effective method in control problems, the Reinforcement learning method is considered to provide decisions for corrective control in power networks. However, the large size of action and state space, as well as the sparse reward problem in corrective control limits the ...Show More

A Power System Corrective Control Method Based on Evolutionary Reinforcement Learning

Haoran Zhang;Peidong Xu;Ke Zhang;Hang Zhao;Yuxin Dai;Tianlu Gao;Jun Zhang

Year: 2022 | Volume: 6 | Journal Article |

A Self-Evolving Agent System for Power System Online Corrective Control

Tianlu Gao;Tianyun Zhang;Ruiqi Si;Peidong Xu;Chen Lv;Jun Zhang

IEEE Journal of Radio Frequency Identification
Year: 2022 | Volume: 6 | Journal Article |
Power system corrective control is a great important strategy to ensure the safety of a grid; however, with the high penetration of distributed energy resources such as solar, wind, hydro, etc., the difficulty of control with active correction of power system dramatically increases. In the mean times, model-free methods such as Deep Reinforcement Learning (DRL) are being extensively studied by man...Show More

A Self-Evolving Agent System for Power System Online Corrective Control

Tianlu Gao;Tianyun Zhang;Ruiqi Si;Peidong Xu;Chen Lv;Jun Zhang

Year: 2022 | Volume: 6 | Journal Article |

Communication-Efficient Federated Edge Learning for NR-U-Based IIoT Networks

Qimei Chen;Xiaoxia Xu;Zehua You;Hao Jiang;Jun Zhang;Fei-Yue Wang

IEEE Internet of Things Journal
Year: 2022 | Volume: 9, Issue: 14 | Journal Article |
Cited by: Papers (13)
As a key infrastructural technology, Industrial Internet of Things (IIoT) and its related techniques have emerged in the age of Industrial Internet. Among them, an increasing popular and attractive federated edge learning (FEL) mechanism, which performs data analysis and inference at the edge devices distributedly, and aggregates local FEL units at a centralized controller, is introduced to meet t...Show More

Communication-Efficient Federated Edge Learning for NR-U-Based IIoT Networks

Qimei Chen;Xiaoxia Xu;Zehua You;Hao Jiang;Jun Zhang;Fei-Yue Wang

Year: 2022 | Volume: 9, Issue: 14 | Journal Article |

Explainable AI in Deep Reinforcement Learning Models for Power System Emergency Control

Ke Zhang;Jun Zhang;Pei-Dong Xu;Tianlu Gao;David Wenzhong Gao

IEEE Transactions on Computational Social Systems
Year: 2022 | Volume: 9, Issue: 2 | Journal Article |
Cited by: Papers (52)
Artificial intelligence (AI) technology has become an important trend to support the analysis and control of complex and time-varying power systems. Although deep reinforcement learning (DRL) has been utilized in the power system field, most of these DRL models are regarded as black boxes, which are difficult to explain and cannot be used on occasions when human operators need to participate. Usin...Show More

Explainable AI in Deep Reinforcement Learning Models for Power System Emergency Control

Ke Zhang;Jun Zhang;Pei-Dong Xu;Tianlu Gao;David Wenzhong Gao

Year: 2022 | Volume: 9, Issue: 2 | Journal Article |

A Trustworthy Framework of Artificial Intelligence for Power Grid Dispatching Systems

Ke Zhang;Peidong Xu;Tianlu Gao;Jun ZHANG

2021 IEEE 1st International Conference on Digital Twins and Parallel Intelligence (DTPI)
Year: 2021 | Conference Paper |
Cited by: Papers (5)
With the widespread application of artificial intelligence (AI) technologies in power systems, the properties of lack of reliability and transparency for AI technologies have revealed gradually. Here, how to build a trustworthy-AI framework based on the power system is the focus. Due to the multidimensional and heterogeneous information of power grid data, the heterogeneous graph attention network...Show More

A Trustworthy Framework of Artificial Intelligence for Power Grid Dispatching Systems

Ke Zhang;Peidong Xu;Tianlu Gao;Jun ZHANG

Year: 2021 | Conference Paper |

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