Site matching of wind turbine generators: a case study

Jangamshetti, Suresh H.; Rau, V. G.

doi:10.1109/60.815102

Cited by 191 publications

(114 citation statements)

References 7 publications

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“…In [19], these WECS parameters are investigated in different farms, and the results show that the cut-in wind speed is generally 0.6v m , the rated wind speed ranges from 1.6v m to 1.75 v m , and the cut-out wind speed is generally set at 3v m . In this case, v m is the annual mean wind speed.…”

Section: Effects Of Wecs Parameters On the Failure Rate Of Wtpcs Pmentioning

confidence: 99%

A Reliability Evaluation Model for the Power Devices Used in Power Converter Systems Considering the Effect of the Different Time Scales of the Wind Speed Profile

Ji¹,

Li²,

Yang³

et al. 2016

Journal of Power Electronics

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This paper presents a reliability assessment model for the power semiconductors used in wind turbine power converters. In this study, the thermal loadings at different timescales of wind speed are considered. First, in order to address the influence of long-term thermal cycling caused by variations in wind speed, the power converter operation state is partitioned into different phases in terms of average wind speed and wind turbulence. Therefore, the contributions can be considered separately. Then, in regards to the reliability assessment caused by short-term thermal cycling, the wind profile is converted to a wind speed distribution, and the contribution of different wind speeds to the final failure rate is accumulated. Finally, the reliability of an actual power converter semiconductor for a 2.5 MW wind turbine is assessed, and the failure rates induced by different timescale thermal behavior patterns are compared. The effects of various parameters such as cut-in, rated, cut-out wind speed on the failure rate of power devices are also analyzed based on the proposed model.

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Section: Effects Of Wecs Parameters On the Failure Rate Of Wtpcs Pmentioning

confidence: 99%

A Reliability Evaluation Model for the Power Devices Used in Power Converter Systems Considering the Effect of the Different Time Scales of the Wind Speed Profile

Ji¹,

Li²,

Yang³

et al. 2016

Journal of Power Electronics

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show abstract

“…The wind speed distribution depends on the measured height and the site, which affect the Weibull distribution parameters. k can be obtained using the standard deviation and the mean wind speed, while c can be obtained using the mean wind speed and the gamma function of k [9]. Fig.…”

Section: Procedures For Calculating the Cf Of A Wgmentioning

confidence: 99%

“…Several studies on the CF of WGs have been conducted [8][9][10][11]. In [8], the potential assessment of the wind resource from the HEMOSU-1 was performed on Korea's southwestern coast in 2011.…”

Section: Introductionmentioning

confidence: 99%

“…The CFs of six WGs from three WG manufacturers with rated wind speeds above 12 m/s were calculated with the wind speed data measured on Korea's southwestern coast. In [9], the installation site's potential was evaluated by monthly and annual CFs using three kinds of mean wind speed data: arithmetic mean, root mean, and cubic mean wind speed. In [10], the factors influencing the CF were analyzed.…”

Section: Introductionmentioning

confidence: 99%

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Power Curve of a Wind Generator Suitable for a Low Wind Speed Site to Achieve a High Capacity Factor

Yoon¹,

Lee²,

Lee³

et al. 2014

Journal of Electrical Engineering and Technology

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-It is well known that energy generated by a wind generator (WG) depends on the wind resources at the installation site. In other words, a WG installed in a high wind speed area can produce more energy than that in a low wind speed area. However, a WG installed at a low wind site can produce a similar amount of energy to that produced by a WG installed at a high wind site if the WG is designed with a rated wind speed corresponding to the mean wind speed of the site. In this paper, we investigated the power curve of a WG suitable for Korea's southwestern coast with a low mean wind speed to achieve a high capacity factor (CF). We collected the power curves of the 11 WGs of the 6 WG manufacturers. The probability density function of the wind speed on Korea's southwestern coast was modeled using the Weibull distribution. The annual energy production by the WG was calculated and then the CFs of all of the WGs were estimated and compared. The results indicated that the WG installed on the Korea's southwestern coast could obtain a CF higher than 40 % if it was designed with the lower rated speed corresponding to the mean wind speed at the installation site.

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“…Therefore, a variety of PDFs have been proposed in literature to describe wind , are also applied to wind energy analysis recently, and they have been proved to be more effective than unimodal types for wind regimes with bimodal distribution. A number of detailed reviews on modeling the probability distributions of wind speed in wind energy analysis can be found in references [20][21][22][23][24][25][26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Wind speed model based on kernel density estimation and its application in reliability assessment of generating systems

Li²,

Yang

et al. 2016

J. Mod. Power Syst. Clean Energy

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An accurate probability distribution model of wind speed is critical to the assessment of reliability contribution of wind energy to power systems. Most of current models are built using the parametric density estimation (PDE) methods, which usually assume that the wind speed are subordinate to a certain known distribution (e.g. Weibull distribution and Normal distribution) and estimate the parameters of models with the historical data. This paper presents a kernel density estimation (KDE) method which is a nonparametric way to estimate the probability density function (PDF) of wind speed. The method is a kind of data-driven approach without making any assumption on the form of the underlying wind speed distribution, and capable of uncovering the statistical information hidden in the historical data. The proposed method is compared with three parametric models using wind data from six sites.The results indicate that the KDE outperforms the PDE in terms of accuracy and flexibility in describing the longterm wind speed distributions for all sites. A sensitivity analysis with respect to kernel functions is presented and Gauss kernel function is proved to be the best one. Case studies on a standard IEEE reliability test system (IEEE-RTS) have verified the applicability and effectiveness of the proposed model in evaluating the reliability performance of wind farms.

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Site matching of wind turbine generators: a case study

Cited by 191 publications

References 7 publications

A Reliability Evaluation Model for the Power Devices Used in Power Converter Systems Considering the Effect of the Different Time Scales of the Wind Speed Profile

A Reliability Evaluation Model for the Power Devices Used in Power Converter Systems Considering the Effect of the Different Time Scales of the Wind Speed Profile

Power Curve of a Wind Generator Suitable for a Low Wind Speed Site to Achieve a High Capacity Factor

Wind speed model based on kernel density estimation and its application in reliability assessment of generating systems

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