Loading [MathJax]/extensions/MathMenu.js

Jianjun Chen - IEEE Xplore Author Profile

Scheduled Maintenance on Monday 1/13/2025

Single article sales and account management will be unavailable from 5:00 AM - 7:00 PM ET (09:00 - 23:00 UTC). We apologize for the inconvenience.

IEEE.org
IEEE Xplore
IEEE SA
IEEE Spectrum
More Sites

- Donate
- Personal Sign In

Access provided by:

MIT Libraries

Access provided by:

MIT Libraries

ADVANCED SEARCH

Author details

Jianjun Chen

Publications

12

Citations

109

Publications by Year

20202025

Co-Authors:

Sayed AbulanwarFrede BlaabjergDi CaoZhe ChenHuiwen Deng

Show All Co-Authors (29)

Jianjun Chen

Affiliation

University of Electronic Science and Technology of China, Chengdu 611731, China

Publication Topics

Bearing Fault Diagnosis,
Fault Diagnosis,
Bearing Fault,
Convolutional Layers,
Convolutional Neural Network,
Distribution System State Estimation,
Empirical Mode Decomposition,
Estimation Method,
Fast Fourier Transform,
Fault Diagnosis Method,
Fault Diagnosis Model,
Fault Location

Biography

Jianjun Chen received the B.S. degree from the Chongqing University of Technology, Chongqing, China, in 2018. He is currently working toward the Ph.D. degree in control science and engineering with the University of Electronic Science and Technology of China, Chengdu, China. His research interests include mechanical fault diagnosis and deep learning.(Based on document published on 31 March 2021).

Publications

12

Citations

109

Publications by Year

20202025

Co-Authors:

Sayed Abulanwar
Frede Blaabjerg
Di Cao
Zhe Chen
Huiwen Deng

Show All Co-Authors (29)

Author's Published Works

Search History

Showing 1-16 of 16 results

Journals (9)

Conferences (6)

Early Access Articles (1)

Sort

Filter Results

Show

Subscribed Content

Open Access Only

Range
Single Year
Guozhou Zhang(16)
Weihao Hu(15)
Zhe Chen(14)
Qi Huang(10)
Di Cao(7)
School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China(12)
Department of Energy Technology, Aalborg University, Aalborg, Denmark(11)
Aalborg University, Aalborg, Denmark(2)
School of Information Engineering, Southwest University of Science and Technology, Mianyang, China(2)
University of Electronic Science and Technology of China, Chengdu, China(2)
IEEE Transactions on Power Systems(5)
2019 IEEE Innovative Smart Grid Technologies - Asia (ISGT Asia)(1)
2020 5th International Conference on Power and Renewable Energy (ICPRE)(1)
2020 IEEE International Conference on Applied Superconductivity and Electromagnetic Devices (ASEMD)(1)
2021 IEEE Power & Energy Society General Meeting (PESGM)(1)
IEEE(16)
Chengdu, China(3)
Shanghai, China(1)
Tianjin, China(1)
Washington, DC, USA(1)
Power System(12)
Deep Reinforcement Learning(9)
Markov Decision Process(7)
Power System Stability(7)
Deep Reinforcement Learning Algorithm(6)

Select All on Page

Sort By

Results

Knowledge-Informed Deep Learning Method for Multiple Oscillation Sources Localization

Zhenjie Cui;Weihao Hu;Guozhou Zhang;Qi Huang;Zhe Chen;Frede Blaabjerg

IEEE Transactions on Power Systems

Year: 2025 | Early Access Article |

This letter presents a novel knowledge-informed deep learning method for the fine-grained localization of forced oscillation sources (FOSs). This method can effectively identify multiple FOSs under anomalous measurements. First, a knowledge-guided block based on dissipated energy flow (DEF) is proposed. In this block, phasor measurement unit (PMU) signals are disassembled and reconstructed in the ...Show More

Knowledge-Informed Deep Learning Method for Multiple Oscillation Sources Localization

Zhenjie Cui;Weihao Hu;Guozhou Zhang;Qi Huang;Zhe Chen;Frede Blaabjerg

IEEE Transactions on Power Systems

Year: 2025 | Early Access Article |

Quick Evaluation of Renewable Energy Accommodation Based on Lagrange-Soft Actor Critic Method

Xiaofei Li;Jun Zhao;Xinwu Sun;Chun Liu;Jinping Zhang;Ming Cheng;Guozhou Zhang;Weihao Hu

2024 6th Asia Energy and Electrical Engineering Symposium (AEEES)

Year: 2024 | Conference Paper |

HTML

With the amounts renewable energy connected with the power system, the evaluation of renewable energy accommodation is important for reducing the redundant capacity of renewable energy unit and improving the stability of power system. This paper proposes a safe deep reinforcement learning-based approach for assessing regional yearly renewable energy accommodation. The proposed method trains an age...Show More

Quick Evaluation of Renewable Energy Accommodation Based on Lagrange-Soft Actor Critic Method

Xiaofei Li;Jun Zhao;Xinwu Sun;Chun Liu;Jinping Zhang;Ming Cheng;Guozhou Zhang;Weihao Hu

2024 6th Asia Energy and Electrical Engineering Symposium (AEEES)

Year: 2024 | Conference Paper |

A Novel Data-Driven Online Model Estimation Method for Renewable Energy Integrated Power Systems With Random Time Delay

Zhenjie Cui;Weihao Hu;Guozhou Zhang;Qi Huang;Zhe Chen;Frede Blaabjerg

IEEE Transactions on Power Systems

Year: 2023 | Volume: 38, Issue: 6 | Journal Article |

Cited by: Papers (4)

HTML

This letter presents a novel data-driven model estimation method for renewable energy source (RES) integrated system with random time delay. The proposed method exploits the theoretical properties of stochastic systems to achieve real-time estimation of the state matrix and the input matrix of a closed-loop system, and does not require any system model information. Experimental results demonstrate...Show More

A Novel Data-Driven Online Model Estimation Method for Renewable Energy Integrated Power Systems With Random Time Delay

Zhenjie Cui;Weihao Hu;Guozhou Zhang;Qi Huang;Zhe Chen;Frede Blaabjerg

IEEE Transactions on Power Systems

Year: 2023 | Volume: 38, Issue: 6 | Journal Article |

Meta-Learning Based Voltage Control for Renewable Energy Integrated Active Distribution Network Against Topology Change

Yincheng Zhao;Guozhou Zhang;Weihao Hu;Qi Huang;Zhe Chen;Frede Blaabjerg

IEEE Transactions on Power Systems

Year: 2023 | Volume: 38, Issue: 6 | Journal Article |

Cited by: Papers (20)

HTML

This letter presents a meta-learning based voltage control strategy for renewable energy integrated active distribution network. The multiple interference self-supervised method is first applied to extract features from unlabeled data. Then, an efficient channel attention convolutional neural network is adopted to select targeted information that is most related to topology change from the feature...Show More

Meta-Learning Based Voltage Control for Renewable Energy Integrated Active Distribution Network Against Topology Change

Yincheng Zhao;Guozhou Zhang;Weihao Hu;Qi Huang;Zhe Chen;Frede Blaabjerg

IEEE Transactions on Power Systems

Year: 2023 | Volume: 38, Issue: 6 | Journal Article |

A DRL-enabled Adaptive Sparsity Promoting Wide-area Damping Controller for Power System with Time-delay and Packet Dropout

Di Cao;Guozhou Zhang;Jiaxiang Hu;Weihao Hu;Yuehui Huang;Zhe Chen

2023 Panda Forum on Power and Energy (PandaFPE)

Year: 2023 | Conference Paper |

HTML

This paper proposes a deep reinforcement learning (DRL) based adaptive sparsity promoting wide-area damping controller for power system. Different from traditional fixed wide-area control structure, the proposed method employs DRL algorithm to identify the optimal sparsity promoting control structure and obtain corresponding parameter settings under different operating conditions considering the t...Show More

A DRL-enabled Adaptive Sparsity Promoting Wide-area Damping Controller for Power System with Time-delay and Packet Dropout

Di Cao;Guozhou Zhang;Jiaxiang Hu;Weihao Hu;Yuehui Huang;Zhe Chen

2023 Panda Forum on Power and Energy (PandaFPE)

Year: 2023 | Conference Paper |

A Novel Knowledge Sharing Method for Rolling Bearing Fault Detection Against Impact of Different Signal Sampling Frequencies

Jianjun Chen;Weihao Hu;Guozhou Zhang;Jiaxiang Hu;Qi Huang;Zhe Chen;Frede Blaabjerg

IEEE Transactions on Instrumentation and Measurement

Year: 2023 | Volume: 72 | Journal Article |

Cited by: Papers (6)

HTML

Accurate and efficient bearing fault diagnosis method plays an important role in the modern industrial system. In practical applications, a large number of data are employed for fault analysis. However, the performance of the fault diagnosis model varies with the sampling frequency of the training data. Theoretically, higher sampling frequency (HSF) data contains much richer fault information, the...Show More

A Novel Knowledge Sharing Method for Rolling Bearing Fault Detection Against Impact of Different Signal Sampling Frequencies

Jianjun Chen;Weihao Hu;Guozhou Zhang;Jiaxiang Hu;Qi Huang;Zhe Chen;Frede Blaabjerg

IEEE Transactions on Instrumentation and Measurement

Year: 2023 | Volume: 72 | Journal Article |

A Novel Data-Driven Self-Tuning SVC Additional Fractional-Order Sliding Mode Controller for Transient Voltage Stability With Wind Generations

Guozhou Zhang;Junbo Zhao;Weihao Hu;Di Cao;Bendong Tan;Qi Huang;Zhe Chen

IEEE Transactions on Power Systems

Year: 2023 | Volume: 38, Issue: 6 | Journal Article |

Cited by: Papers (4)

HTML

This paper proposes a novel data-driven self-tuning additional sliding mode controller for power system transient voltage stability enhancement considering wind integration. We first develop a new additional fractional-order sliding mode controller (FOSMC) for the static var compensator (SVC). The tuning of FOSMC parameter settings is then reformulated as a Markov decision process (MDP) and solved...Show More

A Novel Data-Driven Self-Tuning SVC Additional Fractional-Order Sliding Mode Controller for Transient Voltage Stability With Wind Generations

Guozhou Zhang;Junbo Zhao;Weihao Hu;Di Cao;Bendong Tan;Qi Huang;Zhe Chen

IEEE Transactions on Power Systems

Year: 2023 | Volume: 38, Issue: 6 | Journal Article |

A Multiagent Deep Reinforcement Learning-Enabled Dual-Branch Damping Controller for Multimode Oscillation

Guozhou Zhang;Junbo Zhao;Weihao Hu;Di Cao;Innocent Kamwa;Nan Duan;Zhe Chen

IEEE Transactions on Control Systems Technology

Year: 2023 | Volume: 31, Issue: 1 | Journal Article |

Cited by: Papers (4)

HTML

This study develops a multiagent deep reinforcement learning (MADRL)-enabled framework for the decentralized cooperative control of a novel dual-branch (DB) damping controller for both low-frequency oscillation (LFO) and ultralow-frequency oscillation (ULFO). It has two branches, each of which consists of a proportional resonance (PR) and a second-order polynomial that is designed to handle target...Show More

A Multiagent Deep Reinforcement Learning-Enabled Dual-Branch Damping Controller for Multimode Oscillation

Guozhou Zhang;Junbo Zhao;Weihao Hu;Di Cao;Innocent Kamwa;Nan Duan;Zhe Chen

IEEE Transactions on Control Systems Technology

Year: 2023 | Volume: 31, Issue: 1 | Journal Article |

Decentralized Voltage Control of Large-Scale Distribution System with PVs Based on MADRL

Di Cao;Junbo Zhao;Weihao Hu;Fei Ding;Guozhou Zhang;Qi Huang;Zhe Chen

2021 IEEE Power & Energy Society General Meeting (PESGM)

Year: 2021 | Conference Paper |

HTML

This paper proposes a model-free decentralized control framework for the voltage regulation of large-scale distribution systems through the coordinated control of PV inverters. This is achieved by developing a novel interaction mechanism between the surrogate model and the centralized training and decentralized execution multiagent deep reinforcement learning framework. Specifically, the sparse Ga...Show More

Decentralized Voltage Control of Large-Scale Distribution System with PVs Based on MADRL

Di Cao;Junbo Zhao;Weihao Hu;Fei Ding;Guozhou Zhang;Qi Huang;Zhe Chen

2021 IEEE Power & Energy Society General Meeting (PESGM)

Year: 2021 | Conference Paper |

SMES Damping Controller Design and Real-Time Parameters Tuning for Low-Frequency Oscillation

Tao Li;Weihao Hu;Bin Zhang;Guozhou Zhang;Zhenyuan Zhang;Zhe Chen

IEEE Transactions on Applied Superconductivity

Year: 2021 | Volume: 31, Issue: 8 | Journal Article |

Cited by: Papers (4)

HTML

A novel superconducting magnet energy storage (SMES) damping controller is designed in this paper, which adopts the method of combining integral clearing loop with PI controller, to make sure that the net energy deviation of SMES is zero and avoid deep charging-discharging of SMES during an oscillation process. Moreover, in order to appropriately set the controller parameters considering the uncer...Show More

SMES Damping Controller Design and Real-Time Parameters Tuning for Low-Frequency Oscillation

Tao Li;Weihao Hu;Bin Zhang;Guozhou Zhang;Zhenyuan Zhang;Zhe Chen

IEEE Transactions on Applied Superconductivity

Year: 2021 | Volume: 31, Issue: 8 | Journal Article |

Mechanism Analysis and Real-time Control of Energy Storage Based Grid Power Oscillation Damping: A Soft Actor-Critic Approach

Tao Li;Weihao Hu;Bin Zhang;Guozhou Zhang;Jian Li;Zhe Chen;Frede Blaabjerg

IEEE Transactions on Sustainable Energy

Year: 2021 | Volume: 12, Issue: 4 | Journal Article |

Cited by: Papers (16)

HTML

In this paper, the mechanism of energy storage (ES)-based power oscillation damping is derived by the small signal and the classical electric torque method. And then, by cooperating PI with an integral reduction loop, a controller is designed to form a novel PI-IR controller to guarantee that the energy variation of ES damper is zero at the end of one oscillation. Furthermore, for the controller p...Show More

Mechanism Analysis and Real-time Control of Energy Storage Based Grid Power Oscillation Damping: A Soft Actor-Critic Approach

Tao Li;Weihao Hu;Bin Zhang;Guozhou Zhang;Jian Li;Zhe Chen;Frede Blaabjerg

IEEE Transactions on Sustainable Energy

Year: 2021 | Volume: 12, Issue: 4 | Journal Article |

A Novel Deep Reinforcement Learning Enabled Multi-Band PSS for Multi-Mode Oscillation Control

Guozhou Zhang;Weihao Hu;Junbo Zhao;Di Cao;Zhe Chen;Frede Blaabjerg

IEEE Transactions on Power Systems

Year: 2021 | Volume: 36, Issue: 4 | Journal Article |

Cited by: Papers (27)

HTML

To better damp out the multi-mode oscillations in an uncertain environment, a novel multi-band power system stabilizer (MBPSS) is proposed. Compared with other MBPSSs, the proposed controller has a well-balanced structure, and each band is designed to address a target low-frequency oscillation (LFO) mode. A deep reinforcement learning-enabled agent is developed to effectively tune the control para...Show More

A Novel Deep Reinforcement Learning Enabled Multi-Band PSS for Multi-Mode Oscillation Control

Guozhou Zhang;Weihao Hu;Junbo Zhao;Di Cao;Zhe Chen;Frede Blaabjerg

IEEE Transactions on Power Systems

Year: 2021 | Volume: 36, Issue: 4 | Journal Article |

Deep Reinforcement Learning-based Approach for Online Tuning SMES Damping Controller Parameters

Tao Li;Weihao Hu;Guozhou Zhang;Jian Li;Qi Huang;Zhe Chen

2020 IEEE International Conference on Applied Superconductivity and Electromagnetic Devices (ASEMD)

Year: 2020 | Conference Paper |

Cited by: Papers (2)

HTML

With the penetration of the power electronics dominated large power system, the design of the damping controller needs to ensure the stability of the power system at any disturbance condition. In this context, the closed-loop control agent trained via deep deterministic policy gradient (DDPG) by interacting with massive simulations, for tuning the superconducting magnet energy storage (SMES)-based...Show More

Deep Reinforcement Learning-based Approach for Online Tuning SMES Damping Controller Parameters

Tao Li;Weihao Hu;Guozhou Zhang;Jian Li;Qi Huang;Zhe Chen

2020 IEEE International Conference on Applied Superconductivity and Electromagnetic Devices (ASEMD)

Year: 2020 | Conference Paper |

Deep Reinforcement Learning Based Optimization Strategy for Hydro-Governor PID Parameters to Suppress ULFO

Guozhou Zhang;Weihao Hu;Di Cao;Shi Jing;Qi Huang;Zhe Chen

2020 5th International Conference on Power and Renewable Energy (ICPRE)

Year: 2020 | Conference Paper |

Cited by: Papers (5)

HTML

In this paper, the mechanism of ultra-low frequency oscillation (ULFO) is studied according to the damping torque theory and the influence of proportional, integral and differential parts (PID) parameter settings of hydro-governor on ULFO is investigated. After that, a deep Q network (DQN) based method is proposed for the hydro-governor PID parameter settings self-tuning, which considering the unc...Show More

Deep Reinforcement Learning Based Optimization Strategy for Hydro-Governor PID Parameters to Suppress ULFO

Guozhou Zhang;Weihao Hu;Di Cao;Shi Jing;Qi Huang;Zhe Chen

2020 5th International Conference on Power and Renewable Energy (ICPRE)

Year: 2020 | Conference Paper |

Deep Reinforcement Learning-Based Approach for Proportional Resonance Power System Stabilizer to Prevent Ultra-Low-Frequency Oscillations

Guozhou Zhang;Weihao Hu;Di Cao;Qi Huang;Jianbo Yi;Zhe Chen;Frede Blaabjerg

IEEE Transactions on Smart Grid

Year: 2020 | Volume: 11, Issue: 6 | Journal Article |

Cited by: Papers (71)

HTML

Recent studies have shown that due to the hammer effect of the governor, hydropower units are easily creating negative damping torque at the common mode frequency (below 0.1 Hz). Therefore, there is a risk of ultra low frequency oscillations (ULFO) in hydropower-dominated systems. ULFO is a small-signal frequency oscillation problem, which is quite different from low frequency oscillations (LFO). ...Show More

Deep Reinforcement Learning-Based Approach for Proportional Resonance Power System Stabilizer to Prevent Ultra-Low-Frequency Oscillations

Guozhou Zhang;Weihao Hu;Di Cao;Qi Huang;Jianbo Yi;Zhe Chen;Frede Blaabjerg

IEEE Transactions on Smart Grid

Year: 2020 | Volume: 11, Issue: 6 | Journal Article |

Multi-machine Power System Stabilizers Optimal Design Considering Multiple Operating Modes

Jianbo Yi;Guozhou Zhang;Qi Huang;Yufei Teng;Peng Zhang

2019 IEEE Innovative Smart Grid Technologies - Asia (ISGT Asia)

Year: 2019 | Conference Paper |

HTML

This paper studies the problem of coordination and optimization of multi-machine PSS parameters under multiple operating modes. Firstly, there employs principal component analysis method based on trajectory sensitivity to select the optimal feedback input signals of PSS. Secondly, this paper proposes multi-objective optimization constraints including stability objective function and robust objecti...Show More

Multi-machine Power System Stabilizers Optimal Design Considering Multiple Operating Modes

Jianbo Yi;Guozhou Zhang;Qi Huang;Yufei Teng;Peng Zhang

2019 IEEE Innovative Smart Grid Technologies - Asia (ISGT Asia)

Year: 2019 | Conference Paper |

IEEE Personal Account

Change username/password

Purchase Details

Payment Options
View Purchased Documents

Profile Information

Communications Preferences
Profession and Education
Technical interests

Need Help?

US & Canada: +1 800 678 4333
Worldwide: +1 732 981 0060
Contact & Support

Follow

About IEEE Xplore | Contact Us | Help | Accessibility | Terms of Use | Nondiscrimination Policy | IEEE Ethics Reporting | Sitemap | IEEE Privacy Policy

A public charity, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.

© Copyright 2025 IEEE - All rights reserved, including rights for text and data mining and training of artificial intelligence and similar technologies.

IEEE Account

Change Username/Password
Update Address

Purchase Details

Payment Options
Order History
View Purchased Documents

Profile Information

Communications Preferences
Profession and Education
Technical Interests

Need Help?

US & Canada: +1 800 678 4333
Worldwide: +1 732 981 0060
Contact & Support

About IEEE Xplore
Contact Us
Help
Accessibility
Terms of Use
Nondiscrimination Policy
Sitemap
Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.
© Copyright 2025 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.

Test Whats new message.