直驱式波浪发电系统无源-滑模控制策略

doi:10.19912/j.0254-0096.tynxb.2021-0448

摘要/Abstract

摘要： 针对不规则波输入激励工况下的直驱式波浪发电系统,提出一类无源-滑模控制策略。利用双线性幅度法计算不规则波浪主频,结合波能捕获策略得到期望跟踪曲线,设计无源控制器实时跟踪期望曲线;为削减外部扰动影响并提高系统稳定性,在保证系统无源下引入滑模控制策略。采用扩张状态观测器代替速度传感器,辨识动子速度。仿真结果表明：双线性幅度法能预估不规则波浪主频,扩张状态观测器观测到的速度误差小,所提控制方案能快速响应期望电流变化,准确跟踪期望曲线,鲁棒性强。

关键词: 波浪能, 直驱式, 最大功率点跟踪, 滑模控制, 双线性幅度法, 无速度传感器

Abstract: For the direct-drive wave power system whose input excitation is irregular wave, a passive-sliding mode control strategy is proposed. The bilinear amplitude method is used to calculate the dominant frequency of irregular waves, combined with the wave energy capture strategy to obtain the desired tracking curve, design a passive controller to track the desired curve in real time. In order to reduce the influence of external disturbances and improve the stability of the system, a sliding mode control strategy is introduced to ensure that the system is passive. The resulting impact improves system stability. The expanded state observer is used to replace the speed sensor to realize the identification of the mover's speed. The simulation results show that the bilinear amplitude method can realize the dominant frequency estimation of irregular waves, the speed error observed by the expanded state observer is small, and the proposed control scheme can quickly respond to the expected current changes, accurately track the expected curve, and has strong robustness.

中图分类号:

TM619

邱孟, 杨俊华, 林汇金, 谢子森, 黄纬邦. 直驱式波浪发电系统无源-滑模控制策略[J]. 太阳能学报, 2022, 43(10): 357-363.

Qiu Meng, Yang Junhua, Lin Huijin, Xie Zisen, Huang Weibang. PASSIVE-SLIDING MODE CONTROL STRATEGY OF DIRECT-DRIVE WAVE POWER SYSTEM[J]. Acta Energiae Solaris Sinica, 2022, 43(10): 357-363.

参考文献

[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 X, BAI N Z, KANG Q, 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] CUADRA L, SALCEDO-SANZ S, NIETO-BORGE J C, et al. Computational intelligence in wave energy: comprehensive review and case study[J]. Renewable and sustainable energy reviews, 2016, 58: 1223-1246.
[3] AMON E, BREKKEN T, SCHACHER A.Maximum power point tracking for ocean wave energy conversion[J]. IEEE transactions on industry applications, 2012, 48(3): 1079-1086.
[4] 肖晓龙, 肖龙飞, 杨立军. 串联直驱浮子式波浪能发电装置能量捕获研究[J]. 太阳能学报, 2018, 39(2): 398-405.
XIAO X L, XIAO L F, YANG L J.Energy harvesting study of series direct drivenfloat wave energy converter[J]. Acta energiae solaris sinica, 2018, 39(2): 398-405.
[5] 蔡浩然, 杨俊华, 林巧梅, 等. 傅氏分析反步法直驱型海浪发电系统功率优化控制[J]. 电测与仪表, 2018, 55(18): 57-63.
CAI H R, YANG J H, LIN Q M, et al.An optimal control strategy for output power of directly driven wave generationsystem based on Fourier analysis back-stepping method[J]. Electrical measurement & instrumentation, 2018, 55(18): 57-63.
[6] NGUYEN H, TONA P.An efficiency-aware continuous adaptive proportional-integral velocity-feedback control for wave energy converters[J]. Renewable energy, 2020, 146: 1596-1608.
[7] NGUYEN H N, TONA P.Robust adaptive PI control of wave energy converters with uncertain PTO systems[C]//2018 IEEE Conference on Decision and Control(CDC), Florida, USA, 2018.
[8] 任思敏, 陈仁文, 刘宋祥. 基于LabVIEW的波浪能测控系统的设计[J]. 电子测量技术, 2019, 42(14): 43-51.
REN S M, CHEN R W, LIU S X.Design of wave entergy measurement and control sytem based on LabVIEW[J]. Lectronic measurement technology, 2019, 42(14): 43-51.
[9] HUANG X, SUN K, XIAO X.A neural network-based power control method for direct-drive wave energy converters in irregular waves[J]. IEEE transactions on sustainable energy, 2020, 11(4): 2962-2971.
[10] ZHAN S, LI G.Linear optimal noncausal control of wave energy converters[J]. IEEE transactions on control systems technology, 2018, 27(4): 1526-1536.
[11] 黄俊豪, 杨俊华, 蔡浩然, 等. 基于WFT的直驱式波浪能发电系统自抗扰功率优化控制[J]. 可再生能源, 2021, 39(9): 1271-1278.
HUANG J H, YANG J H, CAI H R, et al.Optimal power control of active disturbance rejection fordirect drive wave power generation system based on WFT[J]. Renewable energy resources, 2021, 39(9): 1271-1278.
[12] LING B A, BOSMA B, BREKKEN T K A, et al. Experimental validation of model predictive control applied to the Azura wave energy converter[J]. IEEE transactions on sustainable energy, 2020, 11(4): 2274-2293.
[13] 杨俊华, 邹子君, 杨金明, 等. 基于人群搜索算法的波浪发电系统最优负载[J]. 太阳能学报, 2019, 40(10): 2725-2731.
YANG J H, ZOU Z J, YANG J M, et al.Optimal load of wave power generation system based onseeker optimization algorithm[J]. Acta energiae solaris sinica, 2019, 40(10): 2725-2731.
[14] 潘海鹏, 丁海波, 雷美珍, 等. 基于模糊逻辑的直驱式海浪发电系统最大功率跟踪控制[J]. 太阳能学报, 2021, 42(6): 21-28.
PAN H P, DING H B, LEI M Z, et al.Maximum power tracking control of direct driveocean wave power system based on fuzzy logic[J]. Acta energiae solaris sinica, 2021, 42(6): 21-28.
[15] 黄秀秀, 杨金明, 陈渊睿, 等. 基于PCHD模型的振荡浮子式波浪发电系统的无源控制[J]. 电测与仪表, 2019, 56(7): 107-112.
HUANG X X, YANG J M, CHEN Y R, et al.Passivity based control of oscillating buoy wave power system based on PCHD model[J]. Electrical measurement & instrumentation, 2019, 56(7): 107-112.
[16] LIAO Z, GAI N, STANSBY P P, et al.Linear non-causal optimal control of an attenuator type wave energy converter M4[J]. IEEE transactions on sustainable energy, 2020, 11(3): 1278-1286.
[17] ANDERLINI E, FOREHAND D I M, BANNON E,et al. Control of a realistic wave energy converter model using least-squares policy iteration[J]. IEEE transactions on sustainable energy, 2017, 8(4): 1618-1628.
[18] 詹启东, 涂亚庆. 基于Rife法的线性调频连续波雷达测距算法及实现[J]. 兵工学报, 2014, 35(5): 748-752. ZHAN Q D,TU Y Q. Analysis and implementation of rife-based ranging algorithm for linear frequency modulated continuous wave radar[J]. Acta armamentarii, 2014, 35(5): 748-752.
[19] 林伟斌, 陈垒, 肖勇, 等. 典型电能计量算法数值仿真及性能比较研究[J]. 电测与仪表, 2016, 53(21): 1-5. LIN W B, CHEN L, XIAO Y, et al. Simulation and performance comparisonof typical electric energy metering algorithms[J]. Electrical measurement & instrumentation, 2016, 53(21): 1-5.
[20] ANDERLINI E, FOREHAND D, BANNON E, et al.Reactive control of a wave energy converter using artificial neural networks[J]. International journal of marine energy, 2017, 19: 207-220.
[21] ANDERLINI E, FOREHAND D I M, STANSELL P, et al. Control of a point absorber using reinforcement learning[J]. IEEE transactions on sustainable energy, 2017, 7(4): 1681-1690.