针对目前风电机组控制性能诊断中缺少有效控制性能综合量化评估方法的问题,基于高斯二次型（LQG）基准提出一种综合功率、载荷及振动等多方面性能的风电机组控制评估方法。该评估方法通过子空间辨识方法,解决评估中风电机组子空间矩阵难以准确建立的问题,同时依据LQG评估基准对机组控制性能进行多目标的综合量化评估,并建立一种风电机组控制性能诊断方法。最后,以某2 MW风电机组为仿真算例,进行某降载策略的控制性能后评估与多台同类型机组的控制性能诊断。结果表明：所提诊断方法可有效识别控制性能劣化机组,而基于LQG基准的控制性能评估方法有助于量化分析机组综合控制性能,从而实现风电机组控制优化。
Abstract
In response to the lack of effective comprehensive quantitative evaluation methods for control performance in current wind turbine control performance diagnosis, this paper proposes a wind turbine control evaluation method based on the linear quadratic Gaussian （LQG） benchmark, which integrates multiple performance aspects such as power, load, and vibration. This evaluation method solves the problem of the difficulty of accurately establishing the subspace matrix of wind turbine units through subspace identification. At the same time, based on the LQG evaluation benchmark, a multi-objective comprehensive quantitative evaluation of unit control performance is carried out, and a wind turbine control performance diagnosis method is established. Finally, using a 2 MW wind turbine as a simulation example, the control performance post-assessment of a specific load reduction strategy and the control performance diagnosis of multiple units of the same type are carried out. The results indicate that the proposed diagnostic method can effectively identify units with degraded control performance, while the LQG-based control performance evaluation method aids in the quantitative analysis of the overall control performance of the units, thereby facilitating the optimization of wind turbine control.
关键词
风电机组 /
性能评估 /
量化 /
高斯二次型（LQG基准） /
子空间辨识 /
性能诊断
Key words
wind turbines /
performance evaluation /
quantification /
LQG benchmark /
subspace identification /
performance diagnostics
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参考文献
[1] 国家能源局发布关于2021年风电、光伏发电开发建设有关事项的通知[EB/OL]. https://zfxxgk.nea.gov.cn/2021-05/11/c_139958210.htm.
The National Energy Administration issued a notice on matters related to the development and construction of wind power and photovoltaic power generation in2021[EB/OL]. https://zfxxgk.nea.gov.cn/2021-05/11/c_139958210.htm.
[2] GAO Z W, et al.An overview on fault diagnosis, prognosis and resilient control for wind turbine systems[J]. Processes, 2021, 9(2): 300.
[3] 刘军, 安柏任, 张维博, 等. 大型风力发电机组健康状态评价综述[J]. 电力系统保护与控制, 2023, 51(1): 176-187.
LIU J, AN B R, ZHANG W B, et al.Review of health status evaluation of large wind turbines[J]. Power system protection and control, 2023, 51(1): 176-187.
[4] 肖运启, 王昆朋, 贺贯举, 等. 基于趋势预测的大型风电机组运行状态模糊综合评价[J]. 中国电机工程学报, 2014, 34(13): 2132-2139.
XIAO Y Q, WANG K P, HE G J, et al.Fuzzy comprehensive evaluation for operating condition of large-scale wind turbines based on trend predication[J]. Proceedings of the CSEE, 2014, 34(13): 2132-2139.
[5] 万书亭, 万杰, 张成杰. 基于灰色理论和变权模糊综合评判的风电机组性能评估[J]. 太阳能学报, 2015, 36(9): 2285-2291.
WAN S T, WAN J, ZHANG C J.Comprehensive evaluation of wind power unit performance evaluation based on grey theory and variable weight fuzzy mathematics[J]. Acta energiae solaris sinica, 2015, 36(9): 2285-2291.
[6] CHENG Y, HU Y, SONG Z Q.Fuzzy comprehensive evaluation of wind turbine operation condition based on weights calculation via factor analysis[C]//2019 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC). Macao, China, 2020: 1-5.
[7] 胡姚刚, 李辉, 刘海涛, 等. 基于多类证据体方法的风电机组健康状态评估[J]. 太阳能学报, 2018, 39(2): 331-341.
HU Y G, LI H, LIU H T, et al.Evaluation of health status of wind turbine based on multiple evidence method[J]. Acta energiae solaris sinica, 2018, 39(2): 331-341.
[8] 周湶, 徐清鹏, 李剑, 等. 融合集对分析和证据理论的风电机组运行状态评估[J]. 电力自动化设备, 2017, 37(7): 38-45.
ZHOU Q, XU Q P, LI J, et al.Operating state assessment based on set-pair analysis and evidential reasoning decision-making for wind turbine generator unit[J]. Electric power automation equipment, 2017, 37(7): 38-45.
[9] 李进友, 李媛, 冯冰, 等. 基于随机组合赋权模糊评价的风电机组健康状态评估[J]. 太阳能学报, 2022, 43(8): 340-351.
LI J Y, LI Y, FENG B, et al.Wind turbine health state assessment based on stochastic combination weighting fuzzy evaluation[J]. Acta energiae solaris sinica, 2022, 43(8): 340-351.
[10] 杨超, 王志, 叶小广, 等. 基于SCADA数据的风电机组技术改造后评估方法研究[J]. 华电技术, 2017, 39(1): 21-23, 77.
YANG C, WANG Z, YE X G, et al.SCADA-data-based wind turbine generator post-technical-improvement evaluating method study[J]. Huadian technology, 2017, 39(1): 21-23, 77.
[11] 韩斌, 赵文超, 刘庆元, 等. 风电机组改造后的评估技术及方法[J]. 船舶工程, 2019, 41(增刊S1): 372-374.
HAN B, ZHAO W C, LIU Q Y, et al.Evaluation techniques and methods for technical transformation of wind turbine[J]. Ship engineering, 2019, 41(S1): 372-374.
[12] 孙栋健, 冯震, 叶杭冶, 等. 基于运行数据的风电机组转矩控制性能评估[J]. 太阳能, 2020(5): 74-79.
SUN D J, FENG Z, YE H Y, et al.Performance evaluation method of wind turbine torque control based on operation data[J]. Solar energy, 2020(5): 74-79.
[13] 黄必清, 何焱, 王婷艳. 基于模糊综合评价的海上直驱风电机组运行状态评估[J]. 清华大学学报(自然科学版), 2015, 55(5): 543-549.
HUANG B Q, HE Y, WANG T Y.Fuzzy synthetic evaluation of the operational status of offshore direct-drive wind turbines[J]. Journal of Tsinghua University (science and technology), 2015, 55(5): 543-549.
[14] LIU Y Q, WANG H J, CHEN Z X, et al.Evaluation model of power generation performance for wind turbine based on entropy weight method and contingency theory[C]//8th Renewable Power Generation Conference (RPG 2019). Shanghai, China, 2020: 1-8.
[15] OUYANG T H, KUSIAK A, HE Y S.Modeling wind-turbine power curve: a data partitioning and mining approach[J]. Renewable energy, 2017, 102: 1-8.
[16] ZHAO Z H, SUN X J.Wind power generation condition assessment method based on multidimensional data mining[C]//2022 2nd International Conference on Algorithms, High Performance Computing and artificial Intelligence (AHPCAI). Guangzhou, China, 2023: 301-304.
[17] LI R X, WU F, HOU P Z, et al.Performance assessment of FO-PID temperature control system using a fractional order LQG benchmark[J]. IEEE access, 2020, 8: 116653-116662.
[18] SHI Y, ZHANG Z M, HU X R, et al.ILC-based two-layer economic performance assessment and improvement strategy for large-scale industrial distributed MPC systems[C]//2022 IEEE International Symposium on Advanced Control of Industrial Processes (AdCONIP). Vancouver, BC, Canada, 2022: 144-150.
[19] 唐炬, 董玉林, 樊雷, 等. 基于Hankel矩阵的复小波-奇异值分解法提取局部放电特征信息[J]. 中国电机工程学报, 2015, 35(7): 1808-1817.
TANG J, DONG Y L, FAN L, et al.Feature information extraction of partial discharge signal with complex wavelet transform and singular value decomposition based on Hankel matrix[J]. Proceedings of the CSEE, 2015, 35(7): 1808-1817.
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