Optimal Power Control Technology of Direct-Drive Wave Power Generation System Based on Model Prediction
Huang Lei1, Wei Lai1, Yang Jianlong1, Hu Minqiang2
1. School of electrical Engineering Southeast University Nanjing 210096 China; 2. School of Electrical and Automation Engineering Nanjing Normal University Nanjing 210023 China
Abstract:The direct-drive wave power generation system has the advantages of a simple structure, low cost, and high efficiency. Permanent magnet linear generators are implemented in direct-drive wave power generation systems for their high-power density, and the electromagnetic force of the linear generator is controlled to realize maximum energy capture of the system. However, the power capture strategy of the direct-drive wave power generation system greatly affects the energy obtained from the system. Predictive models with multiple constraints can solve the safety problems of wave power generation devices in extreme sea conditions, which is suitable for power capture control in direct-drive wave power generation systems. Therefore, based on Model Prediction, this paper proposes an optimal power control strategy for direct-drive wave power generation systems to improve the energy capture of generators. Firstly, the hydrodynamic mathematical model of the direct-drive wave power generation device is established, and the frequency response of the wave-front equation to an excitation force is obtained using the boundary element method. In addition, the dynamic mathematical model of the permanent magnet linear generator is analyzed. Secondly, a power control strategy based on model prediction is derived for the direct-drive wave power generation system. The power control strategy is divided into power capture and power tracking control strategies to convert wave energy into electrical energy and import it into the DC bus. This paper analyzes the optimal power capture strategy from three perspectives: control strategy, prediction interval, and cost function. The state-space equations, cost function, and constraints are analyzed. Then, the model predictive current control is implemented for dynamic power tracking. Thirdly, based on the theoretical derivation, the control block diagram of the direct-drive wave power generation system is established. The simulation model of the system is built, and simulation parameters of the wave power generation are listed. Moreover, the energy captured by the PMLG under different control strategies is compared, and the prediction interval effects on power capture under different cost functions are analyzed. The optimized prediction interval parameters are obtained through comparative analysis. Different cost functions are compared, considering the most power in the DC bus and the electromagnetic power of PMLG. All the simulation models are built. Simulation results show that the optimal power capture strategy based on model prediction effectively improves the energy capture of generators under irregular waves from wave power generation systems. In addition, maximizing the power imported into the DC bus can increase the energy imported into the DC bus by the generator, which has significant practical value. Experimental results under different driving speeds and dynamic wave condition adjustments are obtained. The following conclusions can be drawn. (1) The proposed optimal power control strategy improves the captured energy from the waves, making it suitable for direct-drive wave power generation systems. (2) The prediction interval of EMPC greatly affects the energy absorption of the system, and the cost functions significantly affect the energy feeds into the DC bus. Therefore, selecting an appropriate prediction interval and cost function is helpful for performance improvement. (3) Experimental results show that the system effectively achieves tracking control of current under different working conditions using the proposed control strategy.
黄磊, 魏莱, 杨建龙, 胡敏强. 基于模型预测的直驱式波浪发电机机侧最优功率控制技术[J]. 电工技术学报, 2024, 39(14): 4391-4404.
Huang Lei, Wei Lai, Yang Jianlong, Hu Minqiang. Optimal Power Control Technology of Direct-Drive Wave Power Generation System Based on Model Prediction. Transactions of China Electrotechnical Society, 2024, 39(14): 4391-4404.
[1] 肖曦, 摆念宗, 康庆, 等. 波浪发电系统发展及直驱式波浪发电系统研究综述[J]. 电工技术学报, 2014, 29(3): 1-11. Xiao Xi, Bai Nianzong, Kang Qing, et al.A review of the development of wave power system and the research on direct-drive wave power system[J]. Transactions of China Electrotechnical Society, 2014, 29(3): 1-11. [2] 黄磊, 胡敏强, 余海涛, 等. 直驱式波浪发电用全超导初级励磁直线发电机的设计与分析[J]. 电工技术学报, 2015, 30(2): 80-86. Huang Lei, Hu Minqiang, Yu Haitao, et al.Design and analysis of a fully-superconducting primary-excitation linear generator for direct-driven wave energy generation[J]. Transactions of China Electro-technical Society, 2015, 30(2): 80-86. [3] 洪岳, 潘剑飞, 刘云, 等. 直驱波浪能发电系统综述[J]. 中国电机工程学报, 2019, 39(7): 1886-1900. Hong Yue, Pan Jianfei, Liu Yun, et al.A review on linear generator based wave energy conversion systems[J]. Proceedings of the CSEE, 2019, 39(7): 1886-1900. [4] 张静, 余海涛, 陈琦, 等. 一种海浪发电用永磁单相直线电机的工作特性与实验分析[J]. 电工技术学报, 2013, 28(7): 110-116. Zhang Jing, Yu Haitao, Chen Qi, et al.Dynamic characteristics and experiment analysis of a single phase permanent magnet linear generator for wave energy conversion[J]. Transactions of China Electro-technical Society, 2013, 28(7): 110-116. [5] O'Sullivan A C M, Lightbody G. Co-design of a wave energy converter using constrained predictive control[J]. Renewable Energy, 2017, 102: 142-156. [6] 黄宣睿, 林泽川, 肖曦. 双浮体直驱波浪发电装置建模分析与基于模型预测控制的能量提取算法研究[J]. 电工技术学报, 2024, 39(2): 445-454. Huang Xuanrui, Lin Zechuan, Xiao Xi.Modelling and analysis of the two-body direct-drive wave energy converter and optimal energy extraction method based on model predictive control[J]. Transactions of China Electrotechnical Society, 2024, 39(2): 445-454. [7] Li Guang, Belmont M R.Model predictive control of sea wave energy converters-part I: a convex approach for the case of a single device[J]. Renewable Energy, 2014, 69: 453-463. [8] 卢思灵, 杨俊华, 沈辉, 等. 直驱式波浪发电系统的经济模型预测控制[J]. 电测与仪表, 2021, 58(3): 131-138. Lu Siling, Yang Junhua, Shen Hui, et al.Economic model predictive control of direct-drive wave power generation systems[J]. Electrical Measurement & Instrumentation, 2021, 58(3): 131-138. [9] Wang Zhenchun, Luan Feng, Wang Nianguo.An improved model predictive control method for wave energy converter with sliding mode control[J]. Ocean Engineering, 2021, 240: 109881. [10] Eriksson M, Isberg J, Leijon M.Theory and experi-ment on an elastically moored cylindrical buoy[J]. IEEE Journal of Oceanic Engineering, 2006, 31(4): 959-963. [11] Hai Ling, Göteman M, Leijon M.A methodology of modelling a wave power system via an equivalent RLC circuit[J]. IEEE Transactions on Sustainable Energy, 2016, 7(4): 1362-1370. [12] Guo B, Ringwood J V.A review of wave energy technology from a research and commercial perspe-ive[J]. IET Renewable Power Generation, 2021, 15(14): 3065-3090. [13] Babarit A, Delhommeau G.Theoretical and numerical aspects of the open source BEM solver NEMOH[R]// 11th European Wave and Tidal Energy Conference, Nantes ,France, 2015. [14] Davis A F, Fabien B C.Wave excitation force esti-mation of wave energy floats using extended Kalman filters[J]. Ocean Engineering, 2020, 198: 106970. [15] 苏光靖, 李红梅, 李争, 等. 永磁同步直线电机无模型电流控制[J]. 电工技术学报, 2021, 36(15): 3182-3190. Su Guangjing, Li Hongmei, Li Zheng, et al.Research on model-free current control of permanent magnet synchronous linear motor[J]. Transactions of China Electrotechnical Society, 2021, 36(15): 3182-3190. [16] 王明杰, 贾宛英, 张志艳, 等. 永磁直线同步电机空载反电动势和推力的解析计算[J]. 电工技术学报, 2021, 36(5): 954-963. Wang Mingjie, Jia Wanying, Zhang Zhiyan, et al.Analytical calculation of no-load eletromotive force and thrust in permanent magnet linear synchronous motors[J]. Transactions of China Electrotechnical Society, 2021, 36(5): 954-963. [17] 康庆, 肖曦, 聂赞相, 等. 直驱型海浪发电系统输出功率优化控制策略[J]. 电力系统自动化, 2013, 37(3): 24-29. Kang Qing, Xiao Xi, Nie Zanxiang, et al.An optimal control strategy for output power of the directly driven wave power generation system[J]. Automation of Electric Power Systems, 2013, 37(3): 24-29.