Wind Power Curve Modeling with Hybrid Copula and Grey Wolf Optimization

2021 ◽  
pp. 1-1
Author(s):  
Danxiang Wei ◽  
Jianzhou Wang ◽  
Zhiwu Li ◽  
Rui Wang
Keyword(s):  
Wind Power ◽  
Power Curve ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Curve Modeling

Multi-objective grey wolf optimization of solar chimneys based on an improved model incorporating a wind turbine power curve

2021 ◽  
Vol 239 ◽  
pp. 114231
Author(s):  
Ali Habibollahzade ◽  
Iman Fakhari ◽  
Saeed Mohsenian ◽  
Hossein Aberoumand ◽  
Robert A. Taylor
Keyword(s):  
Wind Turbine ◽  
Power Curve ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Multi Objective ◽  
Improved Model

Wind Power Curve Modeling With Asymmetric Error Distribution

2020 ◽  
Vol 11 (3) ◽  
pp. 1199-1209 ◽  
Author(s):  
Yun Wang ◽  
Qinghua Hu ◽  
Shenglei Pei
Keyword(s):  
Wind Power ◽  
Error Distribution ◽  
Power Curve ◽  
Curve Modeling

A novel hybrid grey wolf optimization algorithm and artificial neural network technique for stochastic unit commitment problem with uncertainty of wind power

2021 ◽  
pp. 014233122110019
Author(s):  
C. Venkatesh Kumar ◽  
M. Ramesh Babu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wind Power ◽  
Optimization Algorithm ◽  
Unit Commitment ◽  
Operating Cost ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Stochastic Unit Commitment ◽  
Hybrid Methodology

The unit commitment (UC) is highly complex to solve the increasing integrations of wind farm due to intermittent wind power fluctuation in nature. This paper presents a hybrid methodology to solve the stochastic unit commitment (SUC) problem depending on binary mixed integer generator combination with renewable energy sources (RESs). In this combination, ON/OFF tasks of the generators are likewise included to satisfy the load requirement as for the system constraints. The proposed hybrid methodology is the consolidation of grey wolf optimization algorithm (GWOA) and artificial neural network (ANN), hence it is called the hybrid GWOANN (HGWOANN) technique. Here, the GWOA algorithm is used to optimizing the best combination of thermal generators depending on uncertain wind power, minimum operating cost and system constraints – that is, thermal generators limits, start-up cost, ramp-up time, ramp-down time, etc. ANN is utilized to capture the uncertain wind power events, therefore the system ensures maximal application of wind power. The combination of HGWOANN technique guarantees the prominent use of sustainable power sources to diminish the thermal generators unit operating cost. The proposed technique is implemented in MATLAB/Simulink site and the efficiency is assessed with different existing methods. The comparative analysis demonstrates that the proposed HGWOANN approach is proficient to solve unit commitment problems and wind integration. Here, the HGWOANN method is compared with existing techniques such as PSO, BPSO, IGSA to assess the overall performance using various metrics viz. RMSE, MAPE, MBE under 50 and 100 count of trials. In the proposed approach, the range of RMSE achieves 9.26%, MAPE achieves 0.95%, MBE achieves 1% in 50 count of trials. Moreover, in 100 count of trials, the range of RMSE achieves 7.38%, MAPE achieves 1.91%, MBE achieves 2.87%.

scholarly journals Wind Turbine Power Curve Modeling with a Hybrid Machine Learning Technique

2019 ◽  
Vol 9 (22) ◽  
pp. 4930 ◽  
Author(s):  
Shenglei Pei ◽  
Yifen Li
Keyword(s):  
Wind Turbine ◽  
Wind Power ◽  
Power Output ◽  
Parametric Models ◽  
Wind Data ◽  
Support Vector ◽  
Power Curve ◽  
Hybrid Strategy ◽  
Curve Modeling ◽  
Power Curves

A power curve of a wind turbine describes the nonlinear relationship between wind speed and the corresponding power output. It shows the generation performance of a wind turbine. It plays vital roles in wind power forecasting, wind energy potential estimation, wind turbine selection, and wind turbine condition monitoring. In this paper, a hybrid power curve modeling technique is proposed. First, fuzzy c-means clustering is employed to detect and remove outliers from the original wind data. Then, different extreme learning machines are trained with the processed data. The corresponding wind power forecasts can also be obtained with the trained models. Finally, support vector regression is used to take advantage of different forecasts from different models. The results show that (1) five-parameter logistic function is superior to the others among the parametric models; (2) generally, nonparametric power curve models perform better than parametric models; (3) the proposed hybrid model can generate more accurate power output estimations than the other compared models, thus resulting in better wind turbine power curves. Overall, the proposed hybrid strategy can also be applied in power curve modeling, and is an effective tool to get better wind turbine power curves, even when the collected wind data is corrupted by outliers.

scholarly journals Wind Turbine Power Curve Modelling with Logistic Functions Based on Quantile Regression

2021 ◽  
Vol 11 (7) ◽  
pp. 3048
Author(s):  
Bo Jing ◽  
Zheng Qian ◽  
Hamidreza Zareipour ◽  
Yan Pei ◽  
Anqi Wang
Keyword(s):  
Quantile Regression ◽  
Wind Turbine ◽  
Wind Power ◽  
Power Distribution ◽  
Wind Farms ◽  
Power Curve ◽  
Energy Assessment ◽  
Curve Modeling ◽  
Relationship Of ◽  
Logistic Functions

The wind turbine power curve (WTPC) is of great significance for wind power forecasting, condition monitoring, and energy assessment. This paper proposes a novel WTPC modelling method with logistic functions based on quantile regression (QRLF). Firstly, we combine the asymmetric absolute value function from the quantile regression (QR) cost function with logistic functions (LF), so that the proposed method can describe the uncertainty of wind power by the fitting curves of different quantiles without considering the prior distribution of wind power. Among them, three optimization algorithms are selected to make comparative studies. Secondly, an adaptive outlier filtering method is developed based on QRLF, which can eliminate the outliers by the symmetrical relationship of power distribution. Lastly, supervisory control and data acquisition (SCADA) data collected from wind turbines in three wind farms are used to evaluate the performance of the proposed method. Five evaluation metrics are applied for the comparative analysis. Compared with typical WTPC models, QRLF has better fitting performance in both deterministic and probabilistic power curve modeling.

scholarly journals Wind Power Assessment Based on a WRF Wind Simulation with Developed Power Curve Modeling Methods

2014 ◽  
Vol 2014 ◽  
pp. 1-15 ◽  
Author(s):  
Zhenhai Guo ◽  
Xia Xiao
Keyword(s):  
Wind Speed ◽  
Wind Power ◽  
Distribution Functions ◽  
Good Effect ◽  
Detailed Knowledge ◽  
Power Curve ◽  
Accurate Assessment ◽  
Local Wind ◽  
Modeling Methods ◽  
Curve Modeling

The accurate assessment of wind power potential requires not only the detailed knowledge of the local wind resource but also an equivalent power curve with good effect for a local wind farm. Although the probability distribution functions (pdfs) of the wind speed are commonly used, their seemingly good performance for distribution may not always translate into an accurate assessment of power generation. This paper contributes to the development of wind power assessment based on the wind speed simulation of weather research and forecasting (WRF) and two improved power curve modeling methods. These approaches are improvements on the power curve modeling that is originally fitted by the single layer feed-forward neural network (SLFN) in this paper; in addition, a data quality check and outlier detection technique and the directional curve modeling method are adopted to effectively enhance the original model performance. The proposed two methods, named WRF-SLFN-OD and WRF-SLFN-WD, are able to avoid the interference from abnormal output and the directional effect of local wind speed during the power curve modeling process. The data examined are from three stations in northern China; the simulation indicates that the two developed methods have strong abilities to provide a more accurate assessment of the wind power potential compared with the original methods.

scholarly journals Wind Power Curve Modeling in Simple and Complex Terrain

2015 ◽  
Author(s):  
V. Bulaevskaya ◽  
S. Wharton ◽  
Z. Irons ◽  
G. Qualley
Keyword(s):  
Wind Power ◽  
Complex Terrain ◽  
Power Curve ◽  
Curve Modeling

scholarly journals Wind power curve modeling in complex terrain using statistical models

2015 ◽  
Vol 7 (1) ◽  
pp. 013103 ◽  
Author(s):  
V. Bulaevskaya ◽  
S. Wharton ◽  
A. Clifton ◽  
G. Qualley ◽  
W. O. Miller
Keyword(s):  
Wind Power ◽  
Statistical Models ◽  
Complex Terrain ◽  
Power Curve ◽  
Curve Modeling

scholarly journals Multi-Area Load Frequency Control of Hydro-Thermal-Wind Power Based on Improved Grey Wolf Optimization Algorithm

2020 ◽  
Vol 26 (6) ◽  
pp. 32-39
Author(s):  
Fannie Kong ◽  
Jinfang Li ◽  
Daliang Yang
Keyword(s):  
Wind Power ◽  
Optimization Algorithm ◽  
Pid Controller ◽  
Frequency Control ◽  
Load Frequency Control ◽  
Grey Wolf Optimizer ◽  
Random Disturbance ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Load Frequency

The mathematical model of load frequency control is established in the interconnected power system of hydro, thermal, and wind for solving the problem of frequency instability in this paper. Besides, the improved grey wolf optimization algorithm (GWO) is presented based on the offspring grey wolf optimizer (OGWO) search strategy to handle local convergence for the GWO algorithm in the later stage. The experimental results show that the improved grey wolf algorithm has a superior optimization ability for the standard test function. The traditional proportional integral derivative (PID) controller cannot track the random disturbance of wind power in the hydro, thermal, and wind interconnected power grid. However, the proposed OGWO dynamically adjusts the PID controller control parameters to follow the wind power random disturbance, regional frequency deviation, and tie-line power deviation.

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