Optimization Design of Indoor Substation Ventilation and Noise Reduction Based on Deep Reinforcement Learning
In view of the equipment safety problems caused by the high operation temperature of the indoor substations and the disturbing noise problems caused by relevant heat dissipation measures, this paper proposes a method for optimizing the design parameters of indoor substation air inlets based on finit...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | zho |
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State Grid Energy Research Institute
2023-01-01
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| Series: | Zhongguo dianli |
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| Online Access: | https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202211051 |
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| _version_ | 1850069071749447680 |
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| author | Jinhui TANG Fayuan WU Yanli ZHI Mengting MAO Xiaomin DAI |
| author_facet | Jinhui TANG Fayuan WU Yanli ZHI Mengting MAO Xiaomin DAI |
| author_sort | Jinhui TANG |
| collection | DOAJ |
| description | In view of the equipment safety problems caused by the high operation temperature of the indoor substations and the disturbing noise problems caused by relevant heat dissipation measures, this paper proposes a method for optimizing the design parameters of indoor substation air inlets based on finite element simulation and deep reinforcement learning to obtain the optimal ventilation and heat dissipation effects. Firstly, the temperature field, fluid field and sound field of the indoor substations are modeled and simulated with the finite element analysis method. Then, based on a large number of simulation data, the convolutional neural network is used to establish the prediction model of temperature and noise. Finally, considering the noise constraint, the maximum entropy reinforcement learning framework based SAC algorithm is used to optimize the design parameters of the air inlets with the goal of minimizing the indoor temperature of the substation. The research results show that the optimized air inlet design scheme can effectively reduce the indoor temperature in the substation, and at the same time make the noise meet the requirements of national regulations. |
| format | Article |
| id | doaj-art-dc06be978712456bb3f457662c9da59f |
| institution | DOAJ |
| issn | 1004-9649 |
| language | zho |
| publishDate | 2023-01-01 |
| publisher | State Grid Energy Research Institute |
| record_format | Article |
| series | Zhongguo dianli |
| spelling | doaj-art-dc06be978712456bb3f457662c9da59f2025-08-20T02:47:52ZzhoState Grid Energy Research InstituteZhongguo dianli1004-96492023-01-015619610510.11930/j.issn.1004-9649.202211051zgdl-56-01-tangjinhuiOptimization Design of Indoor Substation Ventilation and Noise Reduction Based on Deep Reinforcement LearningJinhui TANG0Fayuan WU1Yanli ZHI2Mengting MAO3Xiaomin DAI4Electric Power Research Institute of State Grid Jiangxi Electric Power Co., Ltd., Nanchang 330096, ChinaElectric Power Research Institute of State Grid Jiangxi Electric Power Co., Ltd., Nanchang 330096, ChinaSchool of Electrical and Automation Engineering, East China Jiaotong University, Nanchang 330013, ChinaElectric Power Research Institute of State Grid Jiangxi Electric Power Co., Ltd., Nanchang 330096, ChinaElectric Power Research Institute of State Grid Jiangxi Electric Power Co., Ltd., Nanchang 330096, ChinaIn view of the equipment safety problems caused by the high operation temperature of the indoor substations and the disturbing noise problems caused by relevant heat dissipation measures, this paper proposes a method for optimizing the design parameters of indoor substation air inlets based on finite element simulation and deep reinforcement learning to obtain the optimal ventilation and heat dissipation effects. Firstly, the temperature field, fluid field and sound field of the indoor substations are modeled and simulated with the finite element analysis method. Then, based on a large number of simulation data, the convolutional neural network is used to establish the prediction model of temperature and noise. Finally, considering the noise constraint, the maximum entropy reinforcement learning framework based SAC algorithm is used to optimize the design parameters of the air inlets with the goal of minimizing the indoor temperature of the substation. The research results show that the optimized air inlet design scheme can effectively reduce the indoor temperature in the substation, and at the same time make the noise meet the requirements of national regulations.https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202211051indoor substationfinite element methodventilation and noise reductionreinforcement learningoptimization design |
| spellingShingle | Jinhui TANG Fayuan WU Yanli ZHI Mengting MAO Xiaomin DAI Optimization Design of Indoor Substation Ventilation and Noise Reduction Based on Deep Reinforcement Learning Zhongguo dianli indoor substation finite element method ventilation and noise reduction reinforcement learning optimization design |
| title | Optimization Design of Indoor Substation Ventilation and Noise Reduction Based on Deep Reinforcement Learning |
| title_full | Optimization Design of Indoor Substation Ventilation and Noise Reduction Based on Deep Reinforcement Learning |
| title_fullStr | Optimization Design of Indoor Substation Ventilation and Noise Reduction Based on Deep Reinforcement Learning |
| title_full_unstemmed | Optimization Design of Indoor Substation Ventilation and Noise Reduction Based on Deep Reinforcement Learning |
| title_short | Optimization Design of Indoor Substation Ventilation and Noise Reduction Based on Deep Reinforcement Learning |
| title_sort | optimization design of indoor substation ventilation and noise reduction based on deep reinforcement learning |
| topic | indoor substation finite element method ventilation and noise reduction reinforcement learning optimization design |
| url | https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202211051 |
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