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A Novel power curve Prediction Method for Horizontal-Axis Wind Turbines Using Artificial Neural Networks

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Energy Engineering 2021年第3期118卷 507-516页

作者： Vin Cent Tai Yong Chai Tan Nor Faiza Abd Rahman Chee Ming Chia Mirzhakyp Zhakiya Lip Huat Saw Centre for Modelling and Simulation Faculty of EngineeringBuilt Environment and Information TechnologySEGi UniversityPetaling Jaya47810Malaysia Mechanical Engineering Department Faculty of EngineeringBuilt Environment and Information TechnologySEGi UniversityPetaling Jaya47810Malaysia Lee Kong Chian Faculty of Engineering and Science UTARKajang43200Malaysia

Accurate prediction of wind turbine power curve is essential for wind farm planning as it influences the expected power production.Existing methods require detailed wind turbine geometry for performance evaluation,which most of the time unattainable and impractical in early stage of wind farm planning.While significant amount of work has been done on fitting of wind turbine power curve using parametric and non-parametric models,little to no attention has been paid for power curve modelling that relates the wind turbine design information.This paper presents a novel method that employs artificial neural network to learn the underlying relationships between 6 turbine design parameters and its power curve.A total of 198 existing pitch-controlled and active stall-controlled horizontal-axis wind turbines have been used for model training and validation.The results showed that the method is reliable and reasonably accurate,with average R^(2)score of 0.9966.

关键词： Wind turbine power curve artificial neural network HAWT

Transfer Strategy for power Output Estimation of Wind Farm at Planning Stage Based on a SVR Model

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CSEE Journal of power and Energy Systems 2023年第4期9卷 1460-1471页

作者： Zihao Li Wei Sun Yue Xiang Gareth P.Harrison the School of Engineering University of EdinburghEdinburghEH93DWUK College of Electrical Engineering Sichuan UniversityChengdu 610065China

The conventional wind farm(WF)power generation modelling method highly relies on wind hindcast produced by record time-series data or numerical weather modelling.However,estimating production at future sites is challenging in the absence of local wind monitoring.To address this,a data-driven WF modelling and model transfer strategy is proposed in this work.It considers the challenge of how to transpose metered data from existing operational WFs to sites that might feature as a prospective site for a new WF.By modelling 14 WFs distributed across Scotland using a machine learning(ML)approach,this study proved it was possible to effectively model metered production at a site using modelled wind speed and direction.In addition,this study also found when the latitude difference between two WFs is less than 0.2 degrees and the distance is less than 5o km,two WFs in non-mountainous areas can share an ML model.The results of the shared ML model remain superior to the results of the given power curve from manufacturers,after adjusting the results by the ratio of the power curve in these two WFs.The WF model transfer strategy investigated in this work offered a novel approach to transposing WF production estimates to new sites and appeared to offer better value than simple power curves,which is of importance at the early planning stage for site selection,although it would likely not fully replace detailed micro-siting modelling which are well established in the industry.Index Terms-Machine learning,model transfer strategy,power curve,power output estimation,wind farm.

关键词： Machine learning model transfer strategy power curve power output estimation wind farm.

Investigating the impacts of spatial-temporal variation features of air density on assessing wind power generation and its fluctuation in China

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Science China(Technological Sciences) 2023年第6期66卷 1797-1814页

作者： REN GuoRui WANG Wei WAN Jie HONG Feng YANG Ke YU DaRen School of Control and Computer Engineering North China Electric Power UniversityBeijing 102206China School of Electrical Engineering and Automation Harbin Institute of TechnologyHarbin 150001China Xi’an Thermal power Research Institute Co. Ltd.Xi’an 710054China School of Energy Science and Engineering Harbin Institute of TechnologyHarbin 150001China

Air density plays an important role in assessing wind resource.Air density significantly fluctuates both spatially and temporally.But literature typically used standard air density or local annual average air density to assess wind resource.The present study investigates the estimation errors of the potential and fluctuation of wind resource caused by neglecting the spatial-temporal variation features of air density in China.The air density at 100 m height is accurately calculated by using air temperature,pressure,and humidity.The spatial-temporal variation features of air density are firstly analyzed.Then the wind power generation is modeled based on a 1.5 MW wind turbine model by using the actual air density,standard air densityρst,and local annual average air densityρsite,respectively.Usingρstoverestimates the annual wind energy production(AEP)in 93.6%of the study area.Humidity significantly affects AEP in central and southern China areas.In more than 75%of the study area,the winter to summer differences in AEP are underestimated,but the intra-day peak-valley differences and fluctuation rate of wind power are overestimated.Usingρsitesignificantly reduces the estimation error in AEP.But AEP is still overestimated(0-8.6%)in summer and underestimated(0-11.2%)in winter.Except for southwest China,it is hard to reduce the estimation errors of winter to summer differences in AEP by usingρsite.Usingρsitedistinctly reduces the estimation errors of intra-day peak-valley differences and fluctuation rate of wind power,but these estimation errors cannot be ignored as well.The impacts of air density on assessing wind resource are almost independent of the wind turbine types.

关键词： air density spatial-temporal variation power curve wind resource assessment fluctuation ERA-5

Comparison of modeling methods for wind power prediction:a critical study

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Frontiers in Energy 2020年第2期14卷 347-358页

作者： Rashmi P.SHETTY A.SATHYABHAMA P.Srinivasa PAI Department of Mechanical Engineering National Institute of Technology KarnatakaSurathkal 575025India Department of Mechanical Engineering NMAMITNitteKarkalaTaluk 574110India

Prediction of power generation of a wind turbine is crucial,which calls for accurate and reliable models.In this work,six different models have been developed based on wind power equation,concept of power curve,response surface methodology(RSM)and artificial neural network(ANN),and the results have been compared.To develop the models based on the concept of power curve,the manufacturer’s power curve,and to develop RSM as well as ANN models,the data collected from supervisory control and data acquisition(SCADA)of a 1.5 MW turbine have been used.In addition to wind speed,the air density,blade pitch angle,rotor speed and wind direction have been considered as input variables for RSM and ANN models.Proper selection of input variables and capability of ANN to map input-output relationships have resulted in an accurate model for wind power prediction in comparison to other methods.

关键词： power curve method of least squares cubic spline interpolation response surface methodology artificial neural network(ANN)

Volatilization behaviors of diesel oil from the soils

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Journal of Environmental Sciences 2004年第6期16卷 1033-1036页

作者： LIYu-ying ZHENGXi-lai LIBing MAYu-xin CAOJing-hua InstituteofEnvironmentalScienceandEngineering OceanUniversityofChinaQingdao266003China.

The volatilization of diesel oil, Shengli crude oil and 90# gasoline on glass surface of petri dishes were conducted at the ambient temperature of 25℃. Diesel oil evaporates in a power manner, where the loss of mass is approximately power with time. 90# gasoline evaporates in a logarithmic with time. Where as the volatilization of Shengli crude oil fit either the logarithmic or power equation after different time, and has similar R2. And the effects of soil type and diesel oil and water content on volatilization behavior in unsaturated soil were studied in this paper. Diesel oil and water content in the soils play a large role in volatilization from soils. Appropriate water helps the wicking action but too much water stops it. The wicking action behaves differently in four different types of soils in the same volatilization experiment of 18% diesel oil content and air-dry condition.

关键词： volatilization diesel oil power curve

Wind speed equalization-based incoming wind classification by aggregating DFIGs

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Journal of Modern power Systems and Clean Energy 2013年第1期1卷 42-48页

作者： Zhaojun MENG Feng XUE Xueming LI Group Corporation(State Grid Electric power Research Institute) Nanjing 210003JiangsuChina

With the development of wind energy,it is necessary to develop equivalent models to represent dynamic behaviors of wind farms in power systems.The equivalent wind method is investigated for the aggregation of doubly-fed induction generator wind turbines.The detailed procedures for the calculation of equivalent wind are analyzed.The necessity of classifying incoming winds is shown.To improve the performances of the method,incoming winds are classified according to mean wind speeds and positive/negative semi-variances of wind speeds,and a group of turbines with similar incoming winds are aggregated together.The effectiveness of the method is verified through simulations in MATLAB/Simulink.

关键词： DFIG power curve Equivalent wind Aggregated wind turbine