Reinforcement-Learning-Based Intelligent Maximum Power Point Tracking Control for Wind Energy Conversion Systems
Abstract
This paper proposes an intelligent maximum power point tracking (MPPT) algorithm for variable-speed wind energy conversion systems (WECSs) based on the reinforcement learning (RL) method. The model-free Q-learning algorithm is used by the controller of the WECS to learn a map from states to optimal control actions online by updating the action values according to the received rewards. The experienced action values are stored in a Q-table, based on which the maximum power points (MPPs) are obtained after a certain period of online learning. The learned MPPs are then used to generate an optimum speed–power curve for fast MPPT control of the WECS. Since RL enables the WECS to learn by directly interacting with the environment, knowledge of wind turbine parameters or wind speed information is not required. The proposed MPPT control algorithm is validated by simulation studies for a 1.5-MW doubly-fed induction generator-based WECS and experimental results for a 200-W permanent-magnet synchronous generator-based WECS emulator.
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Prior work has attempted to maximize electricity production by controlling solar panels or wind turbine blades using RL or Bayesian optimization.
“Prior work has attempted to maximize electricity production by controlling solar panels [1,3,678] or wind turbine blades [2,833] using RL or Bayesian optimization.”
From Tackling climate change with machine learning (2023) · cites this paper as [833]
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- Authors
- Wei, C., Zhang, Z., Qiao, W., & Qu, L.
- Affiliations
- University of Nebraska–Lincoln (US)
- Venue
- IEEE Transactions on Industrial Electronics
- Published
- 2015-10
- Volume
- 62
- Issue
- 10
- Pages
- 6360-6370
- ISSN
- 0278-0046 (ISSN-L)
- 1557-9948
- OpenAlex
- W2025823405
- Cited by
- 178
- References
- 31
- Citations by year
- 2026: 7 · 2025: 16 · 2024: 27 · 2023: 21 · 2022: 27 · 2021: 18 · 2020: 22 · 2019: 12 · 2018: 12 · 2017: 7 · 2016: 5 · 2015: 4
- FWCI
- 4.50
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Industrial Electronics (publishedVersion)
- Funders
- National Science Foundation (ECCS-0954938)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Wind Turbine Control Systems
- Physical Sciences › Energy › Renewable Energy, Sustainability and the Environment › Photovoltaic System Optimization Techniques
- Physical Sciences › Engineering › Aerospace Engineering › Wind Energy Research and Development
- Keywords
- maximum power point tracking, wind energy conversion system, reinforcement learning, Q-learning, variable speed wind turbine, doubly fed induction generator, permanent magnet synchronous generator, online learning, wind turbine emulator
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