Deep optimization of model predictive control performance for wind turbine yaw system based on intelligent fuzzy deduction
Because wind direction has the trait of time varying, yaw is a common state of wind turbine (WT). To improve the energy capture and reduce the yaw actuator usage time, a Model Predictive Control (MPC) method based on the Fuzzy Deduction Weight Coefficient Evaluator (FDWE) is proposed. Specifically,...
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| Published in: | Expert systems with applications Vol. 221; p. 119705 |
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| Main Authors: | , , , , , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier Ltd
01.07.2023
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| ISSN: | 0957-4174, 1873-6793 |
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| Abstract | Because wind direction has the trait of time varying, yaw is a common state of wind turbine (WT). To improve the energy capture and reduce the yaw actuator usage time, a Model Predictive Control (MPC) method based on the Fuzzy Deduction Weight Coefficient Evaluator (FDWE) is proposed. Specifically, in view of the two contradictory control objectives of energy capture loss ratio and yaw actuator usage ratio in MPC, FDWE is designed to dynamically adjusts the weight coefficient connecting the two objectives according to the predicted wind direction. On this basis, to fully exert the advantage of FDWE, the fuzzy rule and membership function (MF) are tried to be simultaneously optimized. For this complex optimization problem involving different types of multiple design variables, three different solving strategies are proposed: fuzzy rule-MF ordered optimization, mixed integer optimization, and association optimization. Finally, an improved Adaptive Grid Algorithm Multi-Objective Particle Swarm Optimization is presented to solve the formulated optimization problems. The results indicate that the optimized FDWE-MPC using the three strategies not only improves the energy capture of the WT, but also reduces the use of yaw actuator compared to the baseline MPC. Consequently, the proposed method is promising in reducing the production cost for the WTs. |
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| AbstractList | Because wind direction has the trait of time varying, yaw is a common state of wind turbine (WT). To improve the energy capture and reduce the yaw actuator usage time, a Model Predictive Control (MPC) method based on the Fuzzy Deduction Weight Coefficient Evaluator (FDWE) is proposed. Specifically, in view of the two contradictory control objectives of energy capture loss ratio and yaw actuator usage ratio in MPC, FDWE is designed to dynamically adjusts the weight coefficient connecting the two objectives according to the predicted wind direction. On this basis, to fully exert the advantage of FDWE, the fuzzy rule and membership function (MF) are tried to be simultaneously optimized. For this complex optimization problem involving different types of multiple design variables, three different solving strategies are proposed: fuzzy rule-MF ordered optimization, mixed integer optimization, and association optimization. Finally, an improved Adaptive Grid Algorithm Multi-Objective Particle Swarm Optimization is presented to solve the formulated optimization problems. The results indicate that the optimized FDWE-MPC using the three strategies not only improves the energy capture of the WT, but also reduces the use of yaw actuator compared to the baseline MPC. Consequently, the proposed method is promising in reducing the production cost for the WTs. |
| ArticleNumber | 119705 |
| Author | Huang, Lingxiang Li, Ziqun Yang, Jian Deng, Xiaofei Joo, Younghoon Dong, Mi Su, Mei Song, Dongran |
| Author_xml | – sequence: 1 givenname: Dongran orcidid: 0000-0002-3982-8388 surname: Song fullname: Song, Dongran email: songdongran@csu.edu.cn organization: School of Automation, Central South University, Changsha, China – sequence: 2 givenname: Ziqun surname: Li fullname: Li, Ziqun email: kimlee1874@qq.com organization: School of Automation, Central South University, Changsha, China – sequence: 3 givenname: Xiaofei orcidid: 0000-0001-6600-4918 surname: Deng fullname: Deng, Xiaofei email: xiaofei0228@163.com organization: School of Information Technology and Management, Hunan University of Finance and Economics, Changsha, China – sequence: 4 givenname: Mi surname: Dong fullname: Dong, Mi email: 178120900@qq.com organization: School of Information Technology and Management, Hunan University of Finance and Economics, Changsha, China – sequence: 5 givenname: Lingxiang surname: Huang fullname: Huang, Lingxiang email: huanglingxiang@hewp.com.cn organization: Harbin Electric Corporation Wind Power CO., LTD., Xiangtan, China – sequence: 6 givenname: Jian surname: Yang fullname: Yang, Jian email: jian.yang@csu.edu.cn organization: School of Automation, Central South University, Changsha, China – sequence: 7 givenname: Mei surname: Su fullname: Su, Mei email: sumeicsu@csu.edu.cn organization: School of Automation, Central South University, Changsha, China – sequence: 8 givenname: Younghoon orcidid: 0000-0002-4662-1916 surname: Joo fullname: Joo, Younghoon email: yhjoo@kunsan.ac.kr organization: School of IT Information and Control Engineering, Kunsan National University, Kunsan, Korea |
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| Keywords | MPC Membership function optimization Fuzzy rule optimization AGA-MOPSO Wind turbine Yaw system |
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