Power Curve Modelling for Wind Turbines

Teyabeen, Alhassan Ali; Akkari, Fathi; Jwaid, Ali

doi:10.1109/uksim.2017.30

Cited by 26 publications

(15 citation statements)

References 16 publications

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“…One of the most important parameters in determining electric power obtained from wind-based resources is wind speed. The general equation relating wind power to swept area ( ), wind speed ( ), density of air ( ) , wind turbine power coefficient ( ) and rated power is [39]:…”

Section: Wind Speed Effect Functionmentioning

confidence: 99%

Maintenance Optimization of Wind Turbines Using Weather-Dependent Equivalent Age Model

Aldubaisi¹,

Valenzuela²

2021

JEPT

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Aging models are important input into wind farm maintenance and financial viability models. Aging of wind turbines depends on many factors, including both ambient and usage conditions. This paper presents a virtual age based maintenance model for wind turbines considering the effect of wind speed and ambient air temperature on turbine aging. Two maintenance thresholds (i.e., corrective threshold and preventive threshold) and three repair actions (i.e., unscheduled corrective, scheduled corrective and preventive actions) are integrated into the maintenance model. The objective is to determine the optimal thresholds values that minimize the expected total maintenance costs. A discreet time simulation model is developed to produce 20 years of weather and usage scenarios for a single onshore wind turbine. The optimization model is formulated as a mixed-integer nonlinear problem and solved using the Nelder–Mead method. A numerical example is presented to highlight the benefits of the proposed approach. Compared with traditional age-based maintenance, the proposed approach can achieve improvement in both availability and costs. The results show up to 50% reduction in maintenance cost as well as the significance of the effects of wind speed and ambient air temperature in maintenance planning.

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Section: Wind Speed Effect Functionmentioning

confidence: 99%

Maintenance Optimization of Wind Turbines Using Weather-Dependent Equivalent Age Model

Aldubaisi¹,

Valenzuela²

2021

JEPT

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“…The calculation of wind turbine power output should be accomplished based on the wind turbine power curve provided by the manufacturer. In this study, the wind turbine power output calculation is conducted using the following relation [42]:…”

Section: Univariate Wind Prediction Modelmentioning

confidence: 99%

“…where P r is the maximum power output of a wind turbine rated at wind velocity v r , v min, and v max are the cut-in and cut-off wind velocities, respectively. Finally, P f (v) is the non-linear part of the power curve, representing the power output at wind velocity v. In this study, we utilize the power coefficient-based model given by reference [42]:…”

Section: Univariate Wind Prediction Modelmentioning

confidence: 99%

Optimal Allocation of Spinning Reserves in Interconnected Energy Systems with Demand Response Using a Bivariate Wind Prediction Model

2019

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The proposed study presents a novel probabilistic method for optimal allocation of spinning reserves taking into consideration load, wind and solar forecast errors, inter-zonal spinning reserve trading, and demand response provided by consumers as a single framework. The model considers the system contingencies due to random generator outages as well as the uncertainties caused by load and renewable energy forecast errors. The study utilizes a novel approach to model wind speed and its direction using the bivariate parametric model. The proposed model is applied to the IEEE two-area reliability test system (RTS) to analyze the influence of inter-zonal power generation and demand response (DR) on the adequacy and economic efficiency of energy systems. In addition, the study analyzed the effect of the bivariate wind prediction model on obtained results. The results demonstrate that the presence of inter-zonal capacity in ancillary service markets reduce the total expected energy not supplied (EENS) by 81.66%, while inclusion of a DR program results in an additional 1.76% reduction of EENS. Finally, the proposed bivariate wind prediction model showed a 0.27% reduction in spinning reserve requirements, compared to the univariate model.

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“…Besides the theoretical model, there are some other models, which are predicted by sampling the turbine output power at various wind speed. In [35], nine power curves models have been presented and compared, including linear model, general model, quadratic model, cubic-I model, cubic-II model, exponential model, power-coefficient model, approximate power-coefficient model, and polynomial model. Through the comparison results, it is observed that the power-coefficient model has the most accurate results.…”

Section: Power Curve Modeling Of the Offshore Turbinesmentioning

confidence: 99%

“…where C p,eq is the equivalent power coefficient, which is a constant for the variable-speed wind turbines operating in the range between the cut-in and rated wind speed [35,36]. In this study, it is assumed to be 0.42 [36].…”

Section: Power Curve Modeling Of the Offshore Turbinesmentioning

confidence: 99%

Optimal Design of Rated Wind Speed and Rotor Radius to Minimizing the Cost of Energy for Offshore Wind Turbines

et al. 2018

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As onshore wind energy has depleted, the utilization of offshore wind energy has gradually played an important role in globally meeting growing green energy demands. However, the cost of energy (COE) for offshore wind energy is very high compared to the onshore one. To minimize the COE, implementing optimal design of offshore turbines is an effective way, but the relevant studies are lacking. This study proposes a method to minimize the COE of offshore wind turbines, in which two design parameters, including the rated wind speed and rotor radius are optimally designed. Through this study, the relation among the COE and the two design parameters is explored. To this end, based on the power-coefficient power curve model, the annual energy production (AEP) model is designed as a function of the rated wind speed and the Weibull distribution parameters. On the other hand, the detailed cost model of offshore turbines developed by the National Renewable Energy Laboratory is formulated as a function of the rated wind speed and the rotor radius. Then, the COE is formulated as the ratio of the total cost and the AEP. Following that, an iterative method is proposed to search the minimal COE which corresponds to the optimal rated wind speed and rotor radius. Finally, the proposed method has been applied to the wind classes of USA, and some useful findings have been obtained.

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Power Curve Modelling for Wind Turbines

Cited by 26 publications

References 16 publications

Maintenance Optimization of Wind Turbines Using Weather-Dependent Equivalent Age Model

Maintenance Optimization of Wind Turbines Using Weather-Dependent Equivalent Age Model

Optimal Allocation of Spinning Reserves in Interconnected Energy Systems with Demand Response Using a Bivariate Wind Prediction Model

Optimal Design of Rated Wind Speed and Rotor Radius to Minimizing the Cost of Energy for Offshore Wind Turbines

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