Domain Adaptive Representation Learning for Attack Detection in Smart Grids Kaiyao Miao, Meng Zhang, Bo Fan, Xiaohong Guan IEEE Transactions on Smart Grid, 2026 The proliferation of time-synchronized measurements in smart grids has accelerated the development of machine learning-based data-driven attack detection methods, which have been widely adopted due to their powerful feature extraction capabilities. However, these methods typically assume that the data in the training and deployment phases share an identical distribution, which can be violated when the configurations or components of the deployment system change. Undoubtedly, such domain shift scenarios can harm the attack detection performance. In this work, we tackle this issue by performing domain adaptive and representation learning in one coherent framework termed Domain Adaptive Representation Learning (DARL). First, the nonlinear oscillation modes in sensor measurements during system transients are captured by Koopman mode decomposition. Second, DARL incorporates a hierarchical alignment strategy to reduce the cross-domain discrepancy, which is achieved by performing global distribution and instance-wise feature alignment. Then, DARL learns the target intrinsic structure and representation in a self-supervised manner to avoid over-reliance on source supervision. Extensive case studies on IEEE test systems demonstrate that DARL significantly outperforms baselines in various domain shift scenarios, achieving the smallest detection accuracy drops in target domains.
A Hierarchical Spatio-Temporal Graph Attention Network for False Data Injection Attack Detection in Smart Grids Hongjie Zhang, Jichuan Cheng, Xue Bai, Dong Wang, Rixin Gao, Bo Fan Processes, 2026 The increasing digitalization of smart grids has made them vulnerable to false data injection attacks (FDIAs), which can bypass traditional bad data detection (BDD) schemes and compromise grid security. While machine learning offers promising detection capabilities, existing methods often struggle with generalization, interpretability, and the effective integration of the grid’s inherent spatio-temporal properties. To address these challenges, this paper presents a hierarchical spatio-temporal graph attention network (HST-GAT) for FDIA detection in smart grids. The proposed FDIA detection method employs a dedicated two-stage architecture. First, a graph attention network (GAT) explicitly captures the complex spatial dependencies and physical constraints of the grid topology. Second, a temporal module with multi-head self-attention and a gated recurrent unit (GRU) analyzes evolving attack patterns across time steps. This hierarchical separation ensures a more interpretable and physically grounded representation of cyber intrusions compared to joint spatio-temporal models. Explainability analysis using the SHapley Additive exPlanations (SHAP) method reveals the decision-making process of the proposed FDIA detection method, validating its alignment with the grid topology and identifying the key buses that influence its predictions. The results confirm the robustness of the proposed method and its value in improving cybersecurity in modern smart grids.
Edge-Event-Based Distributed Resilient Control of DC Microgrid Against Multipattern Deception Attacks Xiaojie Qiu, Bo Fan, Yingchun Wang, Wenchao Meng, Qinmin Yang IEEE Transactions on Industrial Electronics, 2026 Cyber attacks and limited communication resources are two important problems in cyber–physical dc microgrid (MG). This article proposes an edge-event-triggered-based distributed resilient model-free adaptive control (MFAC) method for the dc MG suffering multipattern deception attacks to achieve secure voltage restoration and proportional current sharing. It is the first time to construct the distributed MFAC framework that can ensure the consensus of input signals, rather than the output bus voltage, to meet the fundamental requirement for distributed secondary controller design in the dc MG. Second, to deal with the multipattern deception attacks characterized by stochastic and successive variables, a novel data-driven predictive algorithm with an adaptive decay rate is developed to compensate for the polluted input signals, thus mitigating the negative impact of the attacks on the system. Moreover, an edge-event-triggered mechanism (EETM) with a switch-adjustable threshold parameter is designed to reduce communication frequency between any pair of adjacent DGs. Through rigorous mathematical analysis, the convergence of the bus voltage tracking errors is proved, and some comparison experiments are offered to verify the theoretical results.
Adaptive dual-layer performance guaranteed sliding mode control for VSWT with EWS prediction Xuguo Jiao, Hao Luo, Guozhong Wang, Haoran Zhao, Wenchao Meng, Bo Fan, Wenfeng Liu Transactions of the Institute of Measurement and Control, 2026 The maximum power point tracking (MPPT) control is a challenging task in wind power technology due to the stochastic and intermittent nature of the wind speed. This study proposes an effective wind speed (EWS) prediction-assisted adaptive performance guaranteed sliding mode controller to improve power capture efficiency for variable-speed wind turbines (VSWT). First, we propose a novel EWS estimation model based on a broad learning system (BLS). The model is trained on data collected from the existing supervisory control and data acquisition (SCADA) system. Furthermore, a BLS-based EWS prediction approach is developed to compensate for time delays present in the estimated EWS time-series. This prediction model can forecast the EWS in real-time to determine the optimal power reference for MPPT. Moreover, to deal with the uncertainties and external disturbances inherent in wind turbine systems and enhance the transient and steady-state performance of the MPPT control scheme, an error transformation technique-based sliding mode controller is developed. Dual-layer adaptation laws are formulated for the switching gain associated with the proposed sliding mode controller to compensate for the uncertainties appropriately and reduce the chattering phenomena. Finally, the FAST (Fatigue, Aerodynamics, Structures, and Turbulence) software is employed to verify the performance of the proposed approach.
Indoor Height Estimation Method for Scissor Lift Based on Sensor Information Fusion Xinhui Song, Yuzhe Li, Qinmin Yang, Bo Fan Proceedings of 2025 3rd International Conference on Communication Networks and Machine Learning Cnml 2025, 2025 Accurate height information is crucial for enhancing the safety and operability of scissor lift in indoor operations. To address issues such as outlier interference, integral drift, and barometric pressure perturbation in existing single-sensor height measurement methods, this paper proposes a height estimation algorithm that fuses accelerometer and barometer data. The dynamic threshold outlier rejection mechanism is developed using jerk analysis combined with the improved Interquartile Range (IQR) outlier detection algorithm, reducing high-frequency noise interference in the accelerometer. The Finite State Machine (FSM)-based adaptive Kalman filter identifies the motion state and dynamically adjusts drift compensation, enhancing the algorithm's stability. An Extended Kalman Filtering (EKF) fusion model is developed to construct an observation equation using the barometer's absolute height measurement and the accelerometer's relative displacement. The height estimation error is kept within 0.14mby leveraging the complementary characteristics of both sensors, providing a high-precision height reference for scissor lift in indoor operations. This method has broad application potential in indoor positioning and navigation.
Deep Synchronization Control of Grid-Forming Converters: A Reinforcement Learning Approach Zhuorui Wu, Meng Zhang, Bo Fan, Yang Shi, Xiaohong Guan IEEE Caa Journal of Automatica Sinica, 2025 Dear Editor, This letter proposes a deep synchronization control (DSC) method to synchronize grid-forming converters with power grids. The method involves constructing a novel controller for grid-forming converters based on the stable deep dynamics model. To enhance the performance of the controller, the dynamics model is optimized within the deep reinforcement learning (DRL) framework. Simulation results verify that the proposed method can reduce frequency deviation and improve active power responses.
Reinforcement Learning-Based Fault-Tolerant Control of Uncertain Strict-Feedback Nonlinear Systems With Intermittent Actuator Faults Qinmin Yang, Huaying Li, Zhengwei Ruan, Bo Fan, Shuzhi Sam Ge IEEE Transactions on Neural Networks and Learning Systems, 2025 In this work, a novel reinforcement learning-based adaptive fault-tolerant control (FTC) scheme with actuator redundancy is presented for a nonlinear strict-feedback system with nonlinear dynamics and uncertainties. A learning-based switching function technique is established to steer different groups of actuators automatically and successively to mitigate the impact of faulty actuators by observing a switching performance index. The optimal tracking control problem (OTCP) of strict-feedback nonlinear systems is transformed into an equivalent optimal regulation problem of each affine subsystem via adaptive feedforward controllers. Subsequently, the designed objective functions associated with Hamilton-Jacobi-Bellman (HJB) estimate errors caused by neural network (NN) approximations can be minimized by the reinforcement learning algorithm without value or policy iterations. It is proved that the tracking objective can be achieved and all signals in the closed-loop system can be guaranteed to be bounded, as long as the minimum time interval between two successive failures is bounded. Theoretical results are verified by simulations.
Observer-Based Robust Switched LPV Energy-to-Peak Control for Path-Following of Autonomous Ground Vehicles Shuai Liu, Chunyu Zhao, Meng Zhang, Chengshuai Wu, Bo Fan, Peng Shi IEEE ASME International Conference on Advanced Intelligent Mechatronics AIM, 2025 This paper studies the path-following problem for autonomous ground vehicles (AGVs) where the modeling uncertainties and time-varying velocity are addressed. An observer-based robust switched linear-parameter-varying (LPV) energy-to-peak controller is proposed to improve the closed-loop robustness and path-following performance. Since the lateral velocity is unmeasurable, an observer-based output feedback framework is exploited. To overcome the wide variations of longitudinal velocity, the path-following system is modeled as a switched LPV system that consists of a series of LPV models to facilitate a switched control design. The energy-to-peak performance criterion is adopted to further limit the overshoot of the path-following error. Stability analysis is carried out mainly based on the average dwell time method for switched systems. Simulations and experiments demonstrate that, compared to conventional gain-scheduling methods, the proposed method can enhance path-following accuracy and robustness, especially in scenarios with a large range of varying velocity.
Distributed Data-Driven Control for Adjustable Current Sharing and Secure Voltage Restoration in DC Microgrids Xiaojie Qiu, Bo Fan, Wenchao Meng, Yingchun Wang, Yan Xu, Zhaoyang Dong IEEE Transactions on Automation Science and Engineering, 2025 For DC microgrids (MGs), real-time adjustment of current sharing ratios and secure voltage restoration are paramount for optimizing load allocation and enhancing dynamic performance. In this paper, a dual-objective distributed model-free adaptive control (MFAC) scheme is designed for the first time to guarantee voltage transient performance and adjustable current sharing. First, an output-constrained nonlinear MG model with ZIP (constant impedance, constant current and constant power) load is established, and subsequently it is converted into an equivalent unconstrained data model using system transformation and dynamic linearization techniques. Second, a new prescribed performance control algorithm with asymmetrical preset boundaries is proposed to restrict voltage transient responses. This algorithm is updated with real-time input and output data at discrete instants, making it independent of line resistance and ZIP load measurements. To enhance the robustness of the control method, an internal observer is designed to actively compensate for the unknown nonlinear dynamics generated by time-varying system parameters. The stability conditions of the transformed systems in the presence of ZIP loads and time-varying line resistance are derived, which can indirectly ensure the prescribed voltage performance of the original system. Finally, the effectiveness of the proposed control method is validated through some simulations and hardware experiments.
Coordinated neural adaptive active power control of wind turbines considering pitch system load reduction X Jiao, H Luo, B Fan, X Chen, J Zhao, R Yin ISA Transactions , 2026 2026
On IDA-PBC with Maximum Energy Shapeability Z Jiao, C Wu, B Fan, M Zhang, R Ortega arXiv preprint arXiv:2601.01385 , 2026 2026
Edge-Event-Based Distributed Resilient Control of DC Microgrid Against Multipattern Deception Attacks X Qiu, B Fan, Y Wang, W Meng, Q Yang IEEE Transactions on Industrial Electronics , 2025 2025 Citations: 2
Domain Adaptive Representation Learning for Attack Detection in Smart Grids K Miao, M Zhang, B Fan, X Guan IEEE Transactions on Smart Grid , 2025 2025 Citations: 1
Distributed Data-Driven Control for Adjustable Current Sharing and Secure Voltage Restoration in DC Microgrids X Qiu, B Fan, W Meng, Y Wang, Y Xu, Z Dong IEEE Transactions on Automation Science and Engineering 22, 23141-23151 , 2025 2025 Citations: 6
Observer-Based Robust Switched LPV Energy-to-Peak Control for Path-Following of Autonomous Ground Vehicles S Liu, C Zhao, M Zhang, C Wu, B Fan, P Shi 2025 IEEE/ASME International Conference on Advanced Intelligent Mechatronics … , 2025 2025 Citations: 1
基于周期事件触发的直流微电网分布式功率分配与电压调节方法 B Fan, M Zhang, X Guan Scientia Sinica Informationis 55 (7), 1701-1722 , 2025 2025
Reinforcement Learning-Based Fault-Tolerant Control of Uncertain Strict-Feedback Nonlinear Systems With Intermittent Actuator Faults Q Yang, H Li, Z Ruan, B Fan, SS Ge IEEE Transactions on Neural Networks and Learning Systems , 2025 2025 Citations: 12
Indoor Height Estimation Method for Scissor Lift Based on Sensor Information Fusion X Song, Y Li, Q Yang, B Fan Proceedings of the 2025 3rd International Conference on Communication … , 2025 2025
Deep Synchronization Control of Grid-Forming Converters: A Reinforcement Learning Approach Z Wu, M Zhang, B Fan, Y Shi, X Guan IEEE/CAA Journal of Automatica Sinica 12 (1), 273-275 , 2025 2025 Citations: 8
Optimal output‐constrained control of medium‐voltage DC shipboard power systems for pulsed power load accommodation Z Tu, J Peng, B Fan, L Liu, W Liu IET Smart Grid 7 (1), 51-62 , 2024 2024 Citations: 1
Closed-form solutions for grid-forming converters: A design-oriented study F Zhao, T Zhu, L Harnefors, B Fan, H Wu, Z Zhou, Y Sun, X Wang IEEE Open Journal of Power Electronics 5, 186-200 , 2024 2024 Citations: 49
A review of current-limiting control of grid-forming inverters under symmetrical disturbances B Fan, T Liu, F Zhao, H Wu, X Wang IEEE Open Journal of Power Electronics 3, 955-969 , 2022 2022 Citations: 438
Fault recovery analysis of grid-forming inverters with priority-based current limiters B Fan, X Wang IEEE Transactions on Power Systems 38 (6), 5102-5112 , 2022 2022 Citations: 146
Optimal Reset-Control-Based Load Frequency Regulation in Isolated Microgrids Z Tu, B Fan, J Khazaei, W Zhang, W Liu IEEE Transactions on Sustainable Energy 13 (4), 2239-2249 , 2022 2022 Citations: 31
Distributed Aperiodic Control of Multibus DC Microgrids With DoS-Attack Resilience Y Li, W Meng, B Fan, S Zhao, Q Yang IEEE Transactions on Smart Grid 13 (6), 4815-4827 , 2022 2022 Citations: 44
Impact of Circular Current Limiters on Transient Stability of Grid-Forming Converters B Fan, X Wang 2022 International Power Electronics Conference (IPEC-Himeji 2022-ECCE Asia … , 2022 2022 Citations: 10
Equivalent circuit model of grid-forming converters with circular current limiter for transient stability analysis B Fan, X Wang IEEE Transactions on Power Systems 37 (4), 3141-3144 , 2022 2022 Citations: 169
Penalty-Based Distributed Optimal Control of DC Microgrids With Enhanced Current Regulation Performance J Peng, B Fan, W Liu IEEE Transactions on Circuits and Systems I: Regular Papers 69 (7), 3026-3036 , 2022 2022 Citations: 24
Distributed Periodic Event-Triggered Optimal Control of DC Microgrids Based on Virtual Incremental Cost J Peng, B Fan, Z Tu, W Zhang, W Liu IEEE/CAA Journal of Automatica Sinica 9 (4), 624-634 , 2022 2022 Citations: 35
MOST CITED SCHOLAR PUBLICATIONS
A review of current-limiting control of grid-forming inverters under symmetrical disturbances B Fan, T Liu, F Zhao, H Wu, X Wang IEEE Open Journal of Power Electronics 3, 955-969 , 2022 2022 Citations: 438
Equivalent circuit model of grid-forming converters with circular current limiter for transient stability analysis B Fan, X Wang IEEE Transactions on Power Systems 37 (4), 3141-3144 , 2022 2022 Citations: 169
A Consensus-Based Algorithm for Power Sharing and Voltage Regulation in DC Microgrids B Fan, S Guo, J Peng, Q Yang, W Liu, L Liu IEEE Transactions on Industrial Informatics , 2019 2019 Citations: 150
Fault recovery analysis of grid-forming inverters with priority-based current limiters B Fan, X Wang IEEE Transactions on Power Systems 38 (6), 5102-5112 , 2022 2022 Citations: 146
Decentralized high-performance control of DC microgrids C Wang, J Duan, B Fan, Q Yang, W Liu IEEE Transactions on Smart Grid 10 (3), 3355-3363 , 2018 2018 Citations: 139
Voltage-Based Distributed Optimal Control for Generation Cost Minimization and Bounded Bus Voltage Regulation in DC Microgrids J Peng, B Fan, W Liu IEEE Transactions on Smart Grid 12 (1), 106-116 , 2020 2020 Citations: 110
Distributed periodic event-triggered algorithm for current sharing and voltage regulation in DC microgrids B Fan, J Peng, Q Yang, W Liu IEEE Transactions on Smart Grid 11 (1), 577-589 , 2019 2019 Citations: 108
Output-constrained control of nonaffine multiagent systems with partially unknown control directions B Fan, Q Yang, S Jagannathan, Y Sun IEEE Transactions on Automatic Control 64 (9), 3936-3942 , 2019 2019 Citations: 107
Robust ADP design for continuous-time nonlinear systems with output constraints B Fan, Q Yang, X Tang, Y Sun IEEE transactions on neural networks and learning systems 29 (6), 2127-2138 , 2018 2018 Citations: 88
Micro-scale wind resource assessment in complex terrain based on CFD coupled measurement from multiple masts XY Tang, S Zhao, B Fan, J Peinke, B Stoevesandt Applied Energy 238, 806-815 , 2019 2019 Citations: 86
Distributed event-triggered control of DC microgrids J Peng, B Fan, Q Yang, W Liu IEEE Systems Journal 15 (2), 2504-2514 , 2020 2020 Citations: 83
Asymptotic tracking controller design for nonlinear systems with guaranteed performance B Fan, Q Yang, S Jagannathan, Y Sun IEEE transactions on cybernetics 48 (7), 2001-2011 , 2017 2017 Citations: 76
Operation loss minimization targeted distributed optimal control of DC microgrids Z Fan, B Fan, J Peng, W Liu IEEE Systems Journal 15 (4), 5186-5196 , 2020 2020 Citations: 73
Distributed Control of DC Microgrids for Optimal Coordination of Conventional and Renewable Generators Z Fan, B Fan, W Liu IEEE Transactions on Smart Grid 12 (6), 4607-4615 , 2021 2021 Citations: 66
Adaptive decentralized output-constrained control of single-bus DC microgrids J Peng, B Fan, J Duan, Q Yang, W Liu IEEE/CAA Journal of Automatica Sinica 6 (2), 424-432 , 2019 2019 Citations: 65
Discrete-Time Self-Triggered Control of DC Microgrids With Data Dropouts and Communication Delays J Peng, B Fan, H Xu, W Liu IEEE Transactions on Smart Grid 11 (6), 4626-4636 , 2020 2020 Citations: 53
Performance guaranteed control of flywheel energy storage system for pulsed power load accommodation B Fan, C Wang, Q Yang, W Liu, G Wang IEEE Transactions on Power Systems 33 (4), 3994-4004 , 2017 2017 Citations: 50
Closed-form solutions for grid-forming converters: A design-oriented study F Zhao, T Zhu, L Harnefors, B Fan, H Wu, Z Zhou, Y Sun, X Wang IEEE Open Journal of Power Electronics 5, 186-200 , 2024 2024 Citations: 49
Distributed control of multiple-bus microgrid with paralleled distributed generators B Fan, J Peng, J Duan, Q Yang, W Liu IEEE/CAA Journal of Automatica Sinica 6 (3), 676-684 , 2019 2019 Citations: 49
Distributed Privacy-Preserving Active Power Sharing and Frequency Regulation in Microgrids B Fan, X Wang IEEE Transactions on Smart Grid , 2021 2021 Citations: 46
