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publications
Stability analysis of nonlinear oscillator networks based on the mechanism of cascading failures
Published in Chaos, Solitons & Fractals, 2019
Complex networks; Power grid
Recommended citation: Huang Y, Dong H, Zhang W, et al. Stability analysis of nonlinear oscillator networks based on the mechanism of cascading failures[J]. Chaos, Solitons & Fractals, 2019, 128: 5-15. https://www.sciencedirect.com/science/article/pii/S0960077919302826
Comprehensive identification and isolation policies have effectively suppressed the spread of COVID-19
Published in Chaos, Solitons & Fractals, 2020
Virus spread; Complex networks
Recommended citation: Huang Y, Wu Y, Zhang W. Comprehensive identification and isolation policies have effectively suppressed the spread of COVID-19[J]. Chaos, Solitons & Fractals, 2020, 139: 110041. https://www.sciencedirect.com/science/article/pii/S0960077920304392
Soft policy optimization using dual-track advantage estimator
Published in IEEE International Conference on Data Mining (ICDM), 2020
Reinforcement learning
Recommended citation: Huang Y, Wang X, Zou L, et al. Soft policy optimization using dual-track advantage estimator[C]//2020 IEEE International Conference on Data Mining (ICDM). IEEE, 2020: 1064-1069. https://ieeexplore.ieee.org/abstract/document/9338396
Multi-agent reinforcement learning control of a hydrostatic wind turbine-based farm
Published in IEEE Transactions on Sustainable Energy, 2023
Reinforcement learning; Wind Farm Control
Recommended citation: Huang Y, Lin S, Zhao X. Multi-agent reinforcement learning control of a hydrostatic wind turbine-based farm[J]. IEEE Transactions on Sustainable Energy, 2023. https://ieeexplore.ieee.org/abstract/document/10109125
Reinforcement learning-based multi-objective control of grid-connected wind farms
Published in IEEE Transactions on Industrial Informatics, 2024
Reinforcement learning; Wind Farm Control
Recommended citation: Huang Y, Zhao X. Reinforcement Learning-Based Multiobjective Control of Grid-Connected Wind Farms[J]. IEEE Transactions on Industrial Informatics, 2024. https://ieeexplore.ieee.org/abstract/document/10436393
Wind Farm Control via Offline Reinforcement Learning with Adversarial Training
Published in IEEE Transactions on Automation Science and Engineering, 2025
Reinforcement learning; Wind Farm Control
Recommended citation: Huang Y, Zhao X. Wind Farm Control via Offline Reinforcement Learning with Adversarial Training[J]. IEEE Transactions on Automation Science and Engineering, 2025. https://ieeexplore.ieee.org/abstract/document/10912435
talks
DeepWake: Deep learning for wind farm wake modelling
Published:
In this meeting, I have introduced our lastest study in wind energy, DeepWake. DeepWake means that we use physics-informed deep learning to train a wind farm wake model. Additionally, we also expect to deeply reveal the wake generated by wind turbines using this model.
CuWind – CUDA (GPU)-Based High-Fidelity Wind Farm Simulator
Published:
In this workshop, I have introduced how to use CUDA to complete the Wind Farm Simulation to our collaborators with GE.
teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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