A Generalized Approach to Estimating Capacity Factor of Fixed Speed Wind Turbines

Ditkovich, Y.; Kuperman, Alon; Yahalom, Asher; Byalsky, Michael

doi:10.1109/tste.2012.2187126

Cited by 26 publications

(13 citation statements)

References 7 publications

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“…Using a wind turbine power curve and wind speed characteristics at any geographical site, a wind turbine capacity factor can be determined 74,75 . The capacity factor is defined as the ratio between the turbine's actual energy production and the energy produced if the turbine operates at rated power for the same period of time (e.g., a year).…”

Section: Simulation Results and Analysismentioning

confidence: 99%

Extremum‐seeking control for energy‐harvesting enhancement of wind turbines with hydromechanical drivetrains

2020

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Efficiency and reliability enhancement of distributed wind energy systems have been the subject of extensive research effort for the purpose of energy cost reduction and power availability. This work investigates the integration of a hydromechanical power split transmission into the drivetrain of a stall-regulated wind turbine with a fixedspeed generator to enhance the overall system efficiency without compromising its reliability. The proposed drivetrain configuration introduces a variable transmission ratio capability, thus enabling the turbine rotor to cope with varying wind speeds independent of the fixed generator speed. An extremum-seeking control approach is adopted to maximize energy harvesting by controlling a variable geometric volume hydraulic pump without the need for wind speed measurement. Compared with a stall-regulated fixed-speed wind turbine, the performance of the proposed wind turbine configuration is evaluated in simulation environment under different wind speed conditions. The results demonstrate the ability of the proposed wind turbine configuration to significantly improve the system overall efficiency in partial and full-load regions of operation. Consequently, the proposed wind turbine configuration with extremum-seeking control can be considered as an efficient and reliable possible alternative for future distributed and isolated wind energy systems. K E Y W O R D S distributed wind turbines, extremum-seeking control, fatigue loading, hydromechanical drivetrain, power splitting, wind turbine efficiency 1 | INTRODUCTION Wind energy represents one of the major renewable energy resources available for sustainable power generation. The installed wind power capacity and the annual wind power generation have grown rapidly worldwide in the last few decades. 1 However, the future growth in wind energy projects is contingent upon the reduction of its cost. Ultimately, the enhancement of both efficiency and reliability of wind energy systems leads to significant reduction in wind power cost and maintains its availability. This objective motivates the implementation of novel wind energy system configurations and the adoption of advanced control algorithms. Based on their electrical configurations, wind energy systems can be classified as either fixed-speed or variable-speed. 2 Basically, stall-regulated fixed-speed wind energy systems are equipped with squirrel-cage induction generators and operate efficiently in the vicinity of a specific wind speed. As the wind speed varies away from that specified value, the efficiency of these systems drops significantly. Stall-regulated fixed-speed configuration represented a viable choice for small and medium-scale wind turbines (i.e., less than 1 MW) due to its low cost and relatively high reliability, 3 especially in distributed and isolated power systems. However, the relatively low efficiency of stall-regulated fixed-speed wind turbines hinders the growth of their installation.

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Section: Simulation Results and Analysismentioning

confidence: 99%

Extremum‐seeking control for energy‐harvesting enhancement of wind turbines with hydromechanical drivetrains

2020

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“…In power system planning, CF serves as a vital index for evaluating the economic viability, the ability to meet power system load and utilization efficiency of wind power. Various methods have been proposed for calculating the CF [32,33]. However, these wind models depend greatly on the historical data, while the data are unfortunately incomplete or very limited [25] in many cases.…”

Section: A Wind Generation Modelmentioning

confidence: 99%

Optimal wind capacity integration considering the possibilistic uncertainty of wind resources

Sun

Xie

Bie

et al. 2015

2015 IEEE Innovative Smart Grid Technologies - Asia (ISGT ASIA)

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Due to the energy crisis, environmental contamination and other problems related with conventional generations, it is essential to consider the integration of renewable energy, especially wind power. This paper, therefore, outlines the design and study of an interval optimization model to determine optimal wind capacity in an existing power grid. Interval wind capacity factor is proposed to analyze and propagate the uncertainty of wind resources. This model aims at minimizing the overall cost including the variable cost of coal-fired generation, start-up/shutdown cost of generation units, capital investment of wind farm, reserve cost, penalty cost of wind curtailment and fixed operational cost of wind power. The effectiveness of the proposed model is validated by its application in a real-life power grid. Compared with previous research, this method can aid power system planners to make decisions about wind capacity considering the possibilistic uncertainty of wind resource.

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“…The power curve data, provided by the manufacturer, may be extrapolated using either Zero Order Hold (ZOH) method [ 4 ] or polynomial fitting [ 3 ], as shown in Figure 3 . In the former case, each data point P ( v i ) is replaced by a 1 m·s −1 wide discrete bin, having the same magnitude, thus creating the following staircase approximation of the power curve:

for i = 1,…, N .…”

Section: Turbine Power Curvementioning

confidence: 99%

“…The approximate solution for capacity factor is obtained by substituting ( 3 ) and ( 9 ) into ( 1 ) as [ 3 ]

where

is the incomplete Gamma function.…”

Section: Capacity Factor Calculationmentioning

confidence: 99%

“…Hence, ( 1 ) is actually an approximation. Nevertheless, several analytic solutions of ( 1 ) have been presented in the literature [ 2 , 3 ], allowing thorough understanding of the factors, affecting the CF. The more accurate (referred to as quasiexact thereafter) solution, taking into account the original rather than processed wind speed and power curve data, exists in a spreadsheet form only [ 4 ] and its outcome is given by a numerical value without any insight into the CF formation.…”

Section: Introductionmentioning

confidence: 99%

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Comparison of Three Methods for Wind Turbine Capacity Factor Estimation

Ditkovich

Kuperman

2014

The Scientific World Journal

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Three approaches to calculating capacity factor of fixed speed wind turbines are reviewed and compared using a case study. The first “quasiexact” approach utilizes discrete wind raw data (in the histogram form) and manufacturer-provided turbine power curve (also in discrete form) to numerically calculate the capacity factor. On the other hand, the second “analytic” approach employs a continuous probability distribution function, fitted to the wind data as well as continuous turbine power curve, resulting from double polynomial fitting of manufacturer-provided power curve data. The latter approach, while being an approximation, can be solved analytically thus providing a valuable insight into aspects, affecting the capacity factor. Moreover, several other merits of wind turbine performance may be derived based on the analytical approach. The third “approximate” approach, valid in case of Rayleigh winds only, employs a nonlinear approximation of the capacity factor versus average wind speed curve, only requiring rated power and rotor diameter of the turbine. It is shown that the results obtained by employing the three approaches are very close, enforcing the validity of the analytically derived approximations, which may be used for wind turbine performance evaluation.

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A Generalized Approach to Estimating Capacity Factor of Fixed Speed Wind Turbines

Cited by 26 publications

References 7 publications

Extremum‐seeking control for energy‐harvesting enhancement of wind turbines with hydromechanical drivetrains

Extremum‐seeking control for energy‐harvesting enhancement of wind turbines with hydromechanical drivetrains

Optimal wind capacity integration considering the possibilistic uncertainty of wind resources

Comparison of Three Methods for Wind Turbine Capacity Factor Estimation

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