Optimization of Short-Term Overproduction Response of Variable Speed Wind Turbines

被引:39
作者
Altin, Mufit [1 ]
Hansen, Anca D. [1 ]
Barlas, Thanasis K. [1 ]
Das, Kaushik [1 ]
Sakamuri, Jayachandra N. [2 ]
机构
[1] Tech Univ Denmark, Dept Wind Energy, DK-4000 Roskilde, Denmark
[2] ABB HVDC, S-77180 Ludviga, Sweden
关键词
Wind energy integration; heuristic optimization; wind energy; power generation control; short-term overproduction; genetic algorithm; inertial response; FREQUENCY CONTROL; INERTIA; ENERGY;
D O I
10.1109/TSTE.2018.2810898
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Emphasis in this paper is on the optimization of the short-term overproduction response of variable speed wind turbines for synthetic inertia provision or fast frequency control. The short-term overproduction response of wind turbines plays a crucial role in the enhancement of the resilience of future power systems with low inertia especially during large frequency disturbances. Novel optimization approaches employing the genetic algorithm are proposed to maximize the released energy from the wind turbine during its overproduction period considering the electrical and mechanical constraints. Based on the optimization results, the paper identifies and analyzes a set of relevant aspects to be taken into account by power system operators and wind turbine developers in the process of designing the synthetic inertia provision or the fast frequency control. Additionally, the impact of the short-term over production response on the wind turbine structural loading is analyzed through a set of aeroelastic simulations to further investigate aerodynamic limitations.
引用
收藏
页码:1732 / 1739
页数:8
相关论文
共 50 条
[41]   Short-Term Wind Speed Prediction Using EEMD-LSSVM Model [J].
Kang, Aiqing ;
Tan, Qingxiong ;
Yuan, Xiaohui ;
Lei, Xiaohui ;
Yuan, Yanbin .
ADVANCES IN METEOROLOGY, 2017, 2017
[42]   Short-term wind speed forecasting based on the Jaya-SVM model [J].
Liu, Mingshuai ;
Cao, Zheming ;
Zhang, Jing ;
Wang, Long ;
Huang, Chao ;
Luo, Xiong .
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, 2020, 121
[43]   Fine tuning support vector machines for short-term wind speed forecasting [J].
Zhou, Junyi ;
Shi, Jing ;
Li, Gong .
ENERGY CONVERSION AND MANAGEMENT, 2011, 52 (04) :1990-1998
[44]   Analytical Approach to Understanding the Effects of Implementing Fast-Frequency Response by Wind Turbines on the Short-Term Operation of Power Systems [J].
Ochoa, Danny ;
Martinez, Sergio .
ENERGIES, 2021, 14 (12)
[45]   Application of Gaussian Mixture Regression Model for Short-Term Wind Speed Forecasting [J].
Hossain, Md Emrad .
2017 NORTH AMERICAN POWER SYMPOSIUM (NAPS), 2017,
[46]   Very short-term wind speed forecasting with Bayesian structural break model [J].
Jiang, Yu ;
Song, Zhe ;
Kusiak, Andrew .
RENEWABLE ENERGY, 2013, 50 :637-647
[47]   A novel method based on Weibull distribution for short-term wind speed prediction [J].
Kaplan, Orhan ;
Temiz, Murat .
INTERNATIONAL JOURNAL OF HYDROGEN ENERGY, 2017, 42 (28) :17793-17800
[48]   Observer based Primary Control Structure without Wind Speed Measurement for Variable Speed Wind Turbines [J].
Spichartz, Benedikt ;
Sourkounis, Constantinos .
IECON 2020: THE 46TH ANNUAL CONFERENCE OF THE IEEE INDUSTRIAL ELECTRONICS SOCIETY, 2020, :1878-1883
[49]   Transient analysis of variable-speed wind turbines at wind speed disturbances and a pitch control malfunction [J].
Melicio, R. ;
Mendes, V. M. F. ;
Catalao, J. P. S. .
APPLIED ENERGY, 2011, 88 (04) :1322-1330
[50]   Overview of Frequency Control Techniques for DFIG- Based Variable Speed Wind Turbines [J].
Baolong Phung Nguyen ;
Wu, Yuan-kang ;
Manh-Hai Pham .
2023 ASIA MEETING ON ENVIRONMENT AND ELECTRICAL ENGINEERING, EEE-AM, 2023,