Wind characterization analysis incorporating genetic algorithm: A case study in Taiwan Strait

Liu, Fengjiao; Chen, Pai-Hsun; Kuo, Shyi-Shiun; Su, De-Chuan; Chang, Tian-Pau; Yu, Yu-Hua; Lin, Tsung-Chi

doi:10.1016/j.energy.2011.02.001

Cited by 29 publications

(9 citation statements)

References 34 publications

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“…The wind power density, which is proportional to the air density and the cube of the wind speed [ 25 ], can be calculated using the following equation for actual time-series data by [ 19 , 21 ]: where P is the wind power density (units: W/m 2 ); ρ is the air density (units: kg / m 3 ); and is the mean wind speed (units: m/s). For actual time-series data, is simply the average value of wind speeds: …”

Section: Methodsmentioning

confidence: 99%

“…The Weibull function was also applied in Akpınar [ 10 ] for wind power potential assessment and wind characteristic analyses for 6 coastal locations in northeastern Turkey. When the shape parameter in the Weibull distribution is kept at a constant value of 2, the Weibull distribution becomes the Rayleigh function [ 10 , 19 ]. IEC suggested that the 10-min mean wind speed could be modeled by the Rayleigh function [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

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Normal and Extreme Wind Conditions for Power at Coastal Locations in China

Gao

Ning

2015

PLoS ONE

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In this paper, the normal and extreme wind conditions for power at 12 coastal locations along China’s coastline were investigated. For this purpose, the daily meteorological data measured at the standard 10-m height above ground for periods of 40–62 years are statistically analyzed. The East Asian Monsoon that affects almost China’s entire coastal region is considered as the leading factor determining wind energy resources. For most stations, the mean wind speed is higher in winter and lower in summer. Meanwhile, the wind direction analysis indicates that the prevalent winds in summer are southerly, while those in winter are northerly. The air densities at different coastal locations differ significantly, resulting in the difference in wind power density. The Weibull and lognormal distributions are applied to fit the yearly wind speeds. The lognormal distribution performs better than the Weibull distribution at 8 coastal stations according to two judgement criteria, the Kolmogorov–Smirnov test and absolute error (AE). Regarding the annual maximum extreme wind speed, the generalized extreme value (GEV) distribution performs better than the commonly-used Gumbel distribution. At these southeastern coastal locations, strong winds usually occur in typhoon season. These 4 coastal provinces, that is, Guangdong, Fujian, Hainan, and Zhejiang, which have abundant wind resources, are also prone to typhoon disasters.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Normal and Extreme Wind Conditions for Power at Coastal Locations in China

Gao

Ning

2015

PLoS ONE

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“…The key step of the statistical downscaling approach is to acquire near-surface wind speed observations on a time scale of decades, and then construct a downscaling model between the near-surface measured data and the variables of GCMs under control period. On the other hand, due to the lack of long-term measured wind speed data or sufficient offshore meteorological stations, offshore wind energy studies [4,11,12] mostly target on the characterizations or distributions of offshore wind power, but the predictions of near-surface wind data under future period. Li et al used wind data observed at a wind farm in the Taiwan Strait from 2006 to 2008 to compute the Weibull parameters for wind characterization analysis [11].…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, due to the lack of long-term measured wind speed data or sufficient offshore meteorological stations, offshore wind energy studies [4,11,12] mostly target on the characterizations or distributions of offshore wind power, but the predictions of near-surface wind data under future period. Li et al used wind data observed at a wind farm in the Taiwan Strait from 2006 to 2008 to compute the Weibull parameters for wind characterization analysis [11]. Oh et al analyzed marine buoy dataset measured at 5 positions over the period of 12 years, QuikSCAT satellite data measured over 9 years, and a numerical wind map based on meteorological data measured for 4 years to provide a summary of the offshore wind resources of the Korean Peninsula [12].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the climate change impact on wind resources in Taiwan Strait

Chang

Chen

et al. 2015

Energy Conversion and Management

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“…A number of studies have used the PSO algorithm in different problems related to wind energy, such as finding the optimal distribution of wind turbines in a farm, 39 determining wind turbine characteristics to increase the wind power efficiency, 40 and designing wind turbine blades to reduce production costs 41 . It was also employed by Liu et al 42 to predict nonstationary wind speeds. An ANN with PSO and backpropagation (BP) algorithms was implemented in the current study in order to postprocess wind speed forecasts and reduce its errors.…”

Section: Introductionmentioning

confidence: 99%

Improving wind speed forecasts from the Weather Research and Forecasting model at a wind farm in the semiarid Coquimbo region in central Chile

et al. 2020

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Accurate predictions of the wind field are key for better wind power forecasts. Wind speed forecasts from numerical weather models present differences with observations, especially in places with complex topography, such as the north of Chile. The present study has two goals: (a) to find the WRF model boundary layer (PBL) scheme that best reproduces the observations at the Totoral Wind Farm, located in the semiarid Coquimbo region in north‐central Chile, and (b) to use an artificial neural network (ANN) to postprocess wind speed forecasts from different model domains to analyze the sensitivity to horizontal resolution. The WRF model was run with three different PBL schemes (MYNN, MYNN3, and QNSE) for 2013. The WRF simulation with the QNSE scheme showed the best agreement with observations at the wind farm, and its outputs were postprocessed using two ANNs with two algorithms: backpropagation (BP) and particle swarm optimization (PSO). These two ANNs were applied to the innermost WRF domains with 3‐km (d03) and 1‐km (d04) horizontal resolutions. The root‐mean‐square errors (RMSEs) between raw WRF forecasts and observations for d03 and d04 were 2.7 and 2.4 ms−1, respectively. When both ANN models (BP and PSO) were applied to Domains d03 and d04, the RMSE decreased to values lower than 1.7 ms−1, and they showed similar performances, supporting the use of an ANN to postprocess a three‐nested WRF domain configuration to provide more accurate forecasts in advance for the region.

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Wind characterization analysis incorporating genetic algorithm: A case study in Taiwan Strait

Cited by 29 publications

References 34 publications

Normal and Extreme Wind Conditions for Power at Coastal Locations in China

Normal and Extreme Wind Conditions for Power at Coastal Locations in China

Evaluation of the climate change impact on wind resources in Taiwan Strait

Improving wind speed forecasts from the Weather Research and Forecasting model at a wind farm in the semiarid Coquimbo region in central Chile

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