Optimal choice of wind turbine generator based on Monte-Carlo method

Wu, Yue; Ding, Ming

doi:10.1109/drpt.2008.4523829

Cited by 11 publications

(6 citation statements)

References 8 publications

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“…The function ( ) denotes the wind turbine's output power in the interval between cut-in and cutoff speeds. In recent years, several studies have been conducted by scholars aiming to approximate this function [48,[90][91][92][93]. The six most widely used functions for this purpose are listed as follows from Eq.…”

Section: Capacity Factormentioning

confidence: 99%

A Comparative Study of Different Wind Speed Distribution Models for Accurate Evaluation of Onshore Wind Energy Potential: A Case Study on the Southern Coasts of Iran

Filom¹,

Radfar²,

Panahi³

2020

Preprint

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Wind power output is highly dependent on the wind speed at the selected site, therefore wind-speed distribution modeling is the most important step in the assessment of wind energy potential. This study aims at accurate evaluation of onshore wind energy potential in seven coastal cities in the south of Iran. Six Probability Distribution Functions (PDFs) were examined over representative stations. It has been deduced that the Weibull function, which was the most used PDF in similar studies, was only applicable to one station. Here, Gamma offered the best fit for three stations and for the other ones, Generalized Extreme Value (GEV) performed better. Considering the ranking of six examined PDFs and the simplicity of Gamma, it was identified as the effective function in the southern coasts of Iran bearing in mind the geographic distribution of stations. Besides, six turbine power curve functions were contributed to investigate the capacity factor. That was very important, as using only one function could cause under- or over-estimation. Then, stations were classified based on the National Renewable Energy Laboratory system. Last but not least, examining a range of wind turbines enabled scholars to extend this study into the practice and prioritize development of stations considering budget limits.

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Section: Capacity Factormentioning

confidence: 99%

A Comparative Study of Different Wind Speed Distribution Models for Accurate Evaluation of Onshore Wind Energy Potential: A Case Study on the Southern Coasts of Iran

Filom¹,

Radfar²,

Panahi³

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Recently, more and more attentions are paid to the power quality problems of grid-connected wind farms. The stochastic volatility of wind power and the use of power electronic devices may cause voltage fluctuation and flicker [1,2], harmonic [3] and other power quality problems. According to the characteristics of wind power generation, reference [4] established a wind farm simulation platform in Matlab and analyzed the influence of voltage fluctuation and flicker.…”

Section: Introductionmentioning

confidence: 99%

Study for Wind Farm Power Quality Based on Wind Turbine’s Characteristic Parameters

Zhao

Zhang

Wang

2014

AMR

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The grid-connected wind farms bring power quality problems to power system due to the volatility and random of wind power generation. Analysis and calculation are carried out in this paper for the main influence factors of wind power harmonics, voltage fluctuations and flicker, and the assessment methods of the above power quality problems. For a certain wind farm, the corresponding power quality index limits can be calculated. Assessment conclusions can also be given by comparing the calculated power quality index values and the index limits.

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“…In paper [3], Wind speed within the assessment period is divided into several periods, but this method is only applicable to long-term evaluation, it is difficult to be divided into several sections because of the randomness of the larger of a short period of time wind speed changes. In paper [4], wind speed Weibull distribution is introduced to establish the reliability model of wind turbine, but this model does not take the failures and derating of wind turbine into account. Taking into account the stochastic nature of wind energy and wind power generating units forced outage, the reliability model of wind turbine is establishe, but it did not consider the derating state [5].…”

Section: Introductionmentioning

confidence: 99%

Reliability Assessment of Power Generation and Transmission System Considering Wind Farm Based on Monte-Carlo Methods

Zhang

Wang

Jiang

2013

AMR

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According to the weibull distribution of the wind speed, this research establishes the probability outrage model of the wind generator. Besides, this paper establishes a three-state Markov reliability model of the wind generator taking the drop running state into account. And the load model is built considering the randomness of the prediction error. This paper takes the improved IEEE RTS79 system for example, the reliability index of loss of load probability and loss of load expectation are calculated. Because the wind turbine derating state and load error are taken consideration, the proposed model is more accurate and the assessment results have high practical significance.

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Optimal choice of wind turbine generator based on Monte-Carlo method

Cited by 11 publications

References 8 publications

A Comparative Study of Different Wind Speed Distribution Models for Accurate Evaluation of Onshore Wind Energy Potential: A Case Study on the Southern Coasts of Iran

A Comparative Study of Different Wind Speed Distribution Models for Accurate Evaluation of Onshore Wind Energy Potential: A Case Study on the Southern Coasts of Iran

Study for Wind Farm Power Quality Based on Wind Turbine’s Characteristic Parameters

Reliability Assessment of Power Generation and Transmission System Considering Wind Farm Based on Monte-Carlo Methods

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