Distributed deep reinforcement learning for optimal voltage control of PEMFC
Abstract Proton exchange membrane fuel cells (PEMFCs) are promising components in the renewable energy field due to their high energy efficiency and low pollution output. However, these cells are also characterized by considerable nonlinearity, which in turn adversely affects the PEMFC output voltag...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-09-01
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| Series: | IET Renewable Power Generation |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/rpg2.12202 |
| _version_ | 1828147532014813184 |
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| author | Jiawen Li Tao Yu |
| author_facet | Jiawen Li Tao Yu |
| author_sort | Jiawen Li |
| collection | DOAJ |
| description | Abstract Proton exchange membrane fuel cells (PEMFCs) are promising components in the renewable energy field due to their high energy efficiency and low pollution output. However, these cells are also characterized by considerable nonlinearity, which in turn adversely affects the PEMFC output voltage. Conventional control algorithms cannot guarantee sufficient output voltage control, as they lack the robust adaptive ability required for adapting to these fluctuations in PEMFCs. In this paper, an optimal output voltage controller based on distributed deep reinforcement learning, which controls the output voltage by regulating the fuel input of the PEMFC, is proposed. In addition, an ensemble intelligence exploration multi‐delay deep deterministic policy gradient (EIM‐DDPG) algorithm is proposed for this controller. An ensemble intelligence exploration policy plus a classification experience replay mechanism are included within the EIM‐DDPG algorithm to improve the exploration ability of the algorithm and thus increase the robustness and adaptive capability of the controller. As a result, the model‐free optimal output voltage controller offers high robustness and adaptability. The simulation results in this paper demonstrate that the proposed optimal controller can realize the effective control of PEMFC output voltage. |
| first_indexed | 2024-04-11T21:02:47Z |
| format | Article |
| id | doaj.art-3f2b86317d894cb4b7922a8e92e1bf7c |
| institution | Directory Open Access Journal |
| issn | 1752-1416 1752-1424 |
| language | English |
| last_indexed | 2024-04-11T21:02:47Z |
| publishDate | 2021-09-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Renewable Power Generation |
| spelling | doaj.art-3f2b86317d894cb4b7922a8e92e1bf7c2022-12-22T04:03:27ZengWileyIET Renewable Power Generation1752-14161752-14242021-09-0115122778279810.1049/rpg2.12202Distributed deep reinforcement learning for optimal voltage control of PEMFCJiawen Li0Tao Yu1College of Electric Power South China University of Technology Guangzhou ChinaCollege of Electric Power South China University of Technology Guangzhou ChinaAbstract Proton exchange membrane fuel cells (PEMFCs) are promising components in the renewable energy field due to their high energy efficiency and low pollution output. However, these cells are also characterized by considerable nonlinearity, which in turn adversely affects the PEMFC output voltage. Conventional control algorithms cannot guarantee sufficient output voltage control, as they lack the robust adaptive ability required for adapting to these fluctuations in PEMFCs. In this paper, an optimal output voltage controller based on distributed deep reinforcement learning, which controls the output voltage by regulating the fuel input of the PEMFC, is proposed. In addition, an ensemble intelligence exploration multi‐delay deep deterministic policy gradient (EIM‐DDPG) algorithm is proposed for this controller. An ensemble intelligence exploration policy plus a classification experience replay mechanism are included within the EIM‐DDPG algorithm to improve the exploration ability of the algorithm and thus increase the robustness and adaptive capability of the controller. As a result, the model‐free optimal output voltage controller offers high robustness and adaptability. The simulation results in this paper demonstrate that the proposed optimal controller can realize the effective control of PEMFC output voltage.https://doi.org/10.1049/rpg2.12202Fuel cellsOptimisation techniquesVoltage controlOptimisation techniquesFuel cellsOptimal control |
| spellingShingle | Jiawen Li Tao Yu Distributed deep reinforcement learning for optimal voltage control of PEMFC IET Renewable Power Generation Fuel cells Optimisation techniques Voltage control Optimisation techniques Fuel cells Optimal control |
| title | Distributed deep reinforcement learning for optimal voltage control of PEMFC |
| title_full | Distributed deep reinforcement learning for optimal voltage control of PEMFC |
| title_fullStr | Distributed deep reinforcement learning for optimal voltage control of PEMFC |
| title_full_unstemmed | Distributed deep reinforcement learning for optimal voltage control of PEMFC |
| title_short | Distributed deep reinforcement learning for optimal voltage control of PEMFC |
| title_sort | distributed deep reinforcement learning for optimal voltage control of pemfc |
| topic | Fuel cells Optimisation techniques Voltage control Optimisation techniques Fuel cells Optimal control |
| url | https://doi.org/10.1049/rpg2.12202 |
| work_keys_str_mv | AT jiawenli distributeddeepreinforcementlearningforoptimalvoltagecontrolofpemfc AT taoyu distributeddeepreinforcementlearningforoptimalvoltagecontrolofpemfc |