Rüzgâr enerjisi potansiyelini değerlendirirken önemli hususlar

Yıl 2023, Cilt: 38 Sayı: 2, 947 - 962, 07.10.2022

Mohammed Wadi Wisam Elmasry Furkan Ahmet Tamyiğit

https://doi.org/10.17341/gazimmfd.1066351

Öz

Rüzgâr rejimi dağılım modelinin belirlenmesi birkaç nedenden dolayı gereklidir, rüzgâr gücü çıktısını tahmin etmek en önemli konulardan biridir. Bu açıdan rüzgâr hızı dağılımını modellemek için Weibull, Gamma ve Rayleigh dağılımları en yaygın olarak kullanılan dağılımlardır. Ancak, tüm rüzgâr modellerini modellemede üstün olmayabilirler. Sonuç olarak, yerine geçecek dağılım fonksiyonlarının çalışılması gerekmektedir. Bu makale, rüzgâr hızı dağılımını tanımlamak için Weibull, Uç Değer, Ters Gauss, Lojistik, Log-Lojistik, Yarı-Normal, Burr Tipi XII, Genelleştirilmiş Uç Değer, Genelleştirilmiş Pareto ve T Konum-Ölçeği adlı on farklı dağılım fonksiyonlarını kapsamlı bir şekilde sunar. Ayrıca, her dağılımın parametre değerlerini optimize etmek için iki metasezgisel optimizasyon yöntemi olan Genetik Algoritması ve Parçacık Sürü Optimizasyonu kullanılmaktadır. Sunulan dağılımların iyi durumlarını (good-of-fitness) karşılaştırmak için yedi istatistiksel tanımlayıcı ile birlikte altı hata kriteri kullanılmıştır.

Anahtar Kelimeler

CDF, Rüzgâr Enerjisi Modellemesi, İstatistiksel Dağılımlar, PDF, TCDF, GA, PSO

Destekleyen Kurum

Yok

Proje Numarası

Yok

Important considerations while evaluating wind energy potential

Öz

Rayleigh, Gamma, and Weibull distributions are the most widely-used distributions for modeling wind speed distribution. However, they may not be outstanding for modeling all wind patterns. Consequently, substitute distribution functions are required to be studied. This study presents a comprehensive analysis of ten different distributions to represent wind speed patterns: Weibull, Extreme Value, Inverse Gaussian, Logistic, Log-Logistic, Half-Normal, Burr Type XII, Generalized Extreme Value, Generalized Pareto, and T Location-Scale. Additionally, two optimization methods, Genetic Algorithms and Particle Swarm Optimization, are utilized to select the optimal parameter values for each distribution. The good-of-fitness, six error measures, and seven statistical descriptors are employed.

Anahtar Kelimeler

CDF, Wind Energy Modeling, Probability Distribution Function, ICDF, GA, PSO

Proje Numarası

Yok

Kaynakça

• Chang TP. Estimation of wind energy potential using different probability density functions. Applied Energy 2011; 88(5): 1848–1856.
• Wadi M, Elmasry W. Statistical analysis of wind energy potential using different estimation methods for Weibull parameters: a case study. Electrical Engineering 2021: 1–22. doi: https://doi.org/10.1007/s00202-021-01254-0
• Pishgar-Komleh S, Keyhani A, Sefeedpari P. Wind speed and power density analysis based on Weibull and Rayleigh distributions (a case study: Firouzkooh county of Iran). Renewable and sustainable energy reviews 2015; 42: 313–322.
• Morgan EC, Lackner M, Vogel RM, Baise LG. Probability distributions for offshore wind speeds. Energy Conversion and Management 2011; 52(1): 15–26.
• Crutcher HL, Baer L. Computations from elliptical wind distribution statistics. Journal of Applied Meteorology and Climatology 1962; 1(4): 522–530.
• Dutta S, Genton MG. A non-Gaussian multivariate distribution with all lower-dimensional Gaussians and related families. Journal of Multivariate Analysis 2014; 132: 82–93.
• Yuan K, Zhang K, Zheng Y, Li D, Wang Y, Yang Z. Irregular distribution of wind power prediction. Journal of Modern Power Systems and Clean Energy 2018; 6(6): 1172–1180.
• Garcia A, Torres J, Prieto E, De Francisco A. Fitting wind speed distributions: a case study. Solar energy 1998; 62(2): 139–144.
• Scerri E, Farrugia R. Wind data evaluation in the Maltese Islands. Renewable energy 1996; 7(1): 109–114.
• Ahsanullah M, Alzaatreh A. Some Characterizations of the Log-Logistic Distribution. Stochastics and Quality Control 2018; 33(1): 23–29.
• Yilmaz V, Çelik HE. A statistical approach to estimate the wind speed distribution: the case of Gelibolu region. Doğuş Üniversitesi Dergisi 2011; 9(1): 122–132.
• Alavi O, Mohammadi K, Mostafaeipour A. Evaluating the suitability of wind speed probability distribution models: A case of study of east and southeast parts of Iran. Energy Conversion and Management 2016; 119: 101–108.
• Mert I, Karakuş C. A statistical analysis of wind speed data using Burr, generalized gamma, and Weibull distributions in Antakya, Turkey. Turkish Journal of Electrical Engineering & Computer Sciences 2015; 23(6): 1571–1586.
• Rajabi M, Modarres R. Extreme value frequency analysis of wind data from Isfahan, Iran. Journal of wind Engineering and industrial Aerodynamics 2008; 96(1): 78–82.
• El-Shanshoury GI, Ramadan A. Estimation of extreme value analysis of wind speed in the North-Western coast of Egypt. Arab J Nucl Sci Appl 2012; 45: 265–274.
• Nagatsuka H, Balakrishnan N. A method for estimating parameters and quantiles of the three-parameter inverse Gaussian distribution based on statistics invariant to unknown location. Journal of Statistical Computation and Simulation 2014; 84(11): 2361–2377.
• Alayat MM, Kassem Y, Çamur H. Assessment of wind energy potential as a power generation source: A case study of eight selected locations in Northern Cyprus. Energies 2018; 11(10): 2697.
• Lee D, Baldick R. Probabilistic wind power forecasting based on the laplace distribution and golden search. In: IEEE. ; 2016: 1–5.
• Wallner M. A half-normal distribution scheme for generating functions. European Journal of Combinatorics 2020; 87: 103138.
• Gómez YM, Vidal I. A generalization of the half-normal distribution. Applied Mathematics-A Journal of Chinese Universities 2016; 31(4): 409–424.
• Ayuketang Arreyndip N, Joseph E. Generalized extreme value distribution models for the assessment of seasonal wind energy potential of Debuncha, Cameroon. Journal of Renewable Energy 2016; 2016.
• Sarkar A, Deep S, Datta D, Vijaywargiya A, Roy R, Phanikanth V. Weibull and Generalized Extreme Value Distributions for Wind Speed Data Analysis of Some Locations in India. KSCE Journal of Civil Engineering 2019; 23(8): 3476–3492.
• Singh VP, Guo H. Parameter estimation for 3-parameter generalized Pareto distribution by the principle of maximum entropy (POME). Hydrological sciences journal 1995; 40(2): 165–181.
• D’Amico G, Petroni F, Prattico F. Wind speed prediction for wind farm applications by extreme value theory and copulas. Journal of Wind Engineering and Industrial Aerodynamics 2015; 145: 229–236.
• Zhang J. Wind power fluctuation characteristics of wind farms. In: Atlantis Press. ; 2015.
• Sohoni V, Gupta S, Nema R. A comparative analysis of wind speed probability distributions for wind power assessment of four sites. Turkish Journal of Electrical Engineering & Computer Sciences 2016; 24(6): 4724–4735.
• Wadi M, Kekezoglu B, Baysal M, Tur MR, Shobole A. Feasibility Study of Wind Energy Potential in Turkey: Case Study of Catalca District in Istanbul. In: IEEE. ; 2019: 1–6.
• Gul M, Tai N, Huang W, Nadeem MH, Yu M. Evaluation of Wind Energy Potential Using an Optimum Approach based on Maximum Distance Metric. Sustainability 2020; 12(5): 1999.
• Jung C, Schindler D. Global comparison of the goodness-of-fit of wind speed distributions. Energy Conversion and Management 2017; 133: 216–234.
• Saxena BK, Rao KVS. Comparison of Weibull parameters computation methods and analytical estimation of wind türbine capacity factor using polynomial power curve model: case study of a wind farm. Renewables: Wind, Water, and Solar 2015; 2(1): 1–11.
• Pobočíková I, Sedliačková Z, Michalková M. Application of four probability distributions for wind speed modeling. Procedia engineering 2017; 192: 713–718.
• Drobinski P, Coulais C, Jourdier B. Surface wind-speed statistics modelling: Alternatives to the Weibull distribution and performance evaluation. Boundary-Layer Meteorology 2015; 157(1): 97–123.
• Abolpour B, Abolpour B, Bakhshi H, Yaghobi M. An Appropriate Extreme Value Distribution for the Annual Extreme Gust Winds Speed. J Fundam Renewable Energy Appl 2017; 7(223): 2.
• Quan Y, Wang F, Gu M. A method for estimation of extreme values of wind pressure on buildings based on the generalized extreme-value theory. Mathematical Problems in Engineering 2014; 2014.
• Zhao X, Zhang Z, Cheng W, Zhang P. A new parameter estimator for the Generalized Pareto distribution under the peaks over threshold framework. Mathematics 2019; 7(5): 406.
• An Y, Pandey M. A comparison of methods of extreme wind speed estimation. Journal of Wind Engineering and Industrial Aerodynamics 2005; 93(7): 535–545.
• Xiao Y, Li Q, Li Z, Chow Y, Li G. Probability distributions of extreme wind speed and its occurrence interval. Engineering Structures 2006; 28(8): 1173–1181.
• Krishnamoorthy R, Udhayakumar K, Raju K, Elavarasan RM, Mihet-Popa L, others. An Assessment of Onshore and Offshore Wind Energy Potential in India Using Moth Flame Optimization. Energies 2020; 13(12): 1–41.
• Zhang L, Li Q, Guo Y, Yang Z, Zhang L. An investigation of wind direction and speed in a featured wind farm using joint probability distribution methods. Sustainability 2018; 10(12): 4338.
• Ahsanullah M, Alzaatreh A. Parameter estimation for the log-logistic distribution based on order statistics. REVSTAT Statistical Journal 2018; 16: 429–443.
• Lin L, Ang AH, Fan W, Xia D. A probability-based analysis of wind speed distribution and related structural response in southeast China. Structure and Infrastructure Engineering 2019; 15(1): 14–26.
• Markose S, Alentorn A. The generalized extreme value distribution, implied tail index, and option pricing. The Journal of Derivatives 2011; 18(3): 35–60.
• Kang S, Song J. Parameter and quantile estimation for the generalized Pareto distribution in peaks over threshold framework. Journal of the Korean Statistical Society 2017; 46: 487–501.
• Brabson B, Palutikof J. Tests of the generalized Pareto distribution for predicting extreme wind speeds. Journal of applied meteorology 2000; 39(9): 1627–1640.
• Holmes J, Moriarty W. Application of the generalized Pareto distribution to extreme value analysis in wind engineering. Journal of Wind Engineering and Industrial Aerodynamics 1999; 83(1-3): 1–10.
• Steinkohl C, Davis RA, Klüppelberg C. Extreme value analysis of multivariate high-frequency wind speed data. Journal of Statistical Theory and Practice 2013; 7(1): 73–94.
• Li K, Kang X, Liu L. Two-stage Optimal Sizing of Hybrid Energy Storage System for Wind Energy Integration in Microgrid. In: IEEE. ; 2020: 1–5.
• Lin W, Wen J, Cheng S, Lee WJ. An investigation on the active-power variations of wind farms. IEEE Transactions on Industry Applications 2012; 48(3): 1087–1094.
• Elmasry W, Akbulut A, Zaim AH. Empirical study on multiclass classification-based network intrusion detection. Computational Intelligence 2019; 35(4): 919–954.
• Elmasry W, Akbulut A, Zaim AH. Comparative evaluation of different classification techniques for masquerade attack detection. International Journal of Information and Computer Security 2020; 13(2): 187–209.
• Mostafaeipour A. Feasibility study of harnessing wind energy for turbine installation in province of Yazd in Iran. Renewable and Sustainable Energy Reviews 2010; 14(1): 93–111.
• Okorie ME, Inambao F, Chiguvare Z. Evaluation of wind shear coefficients, surface roughness and energy yields over inland locations in Namibia. Procedia Manufacturing 2017; 7: 630–638.
• Gualtieri G, Secci S. Wind shear coefficients, roughness length and energy yield over coastal locations in Southern Italy. Renewable Energy 2011; 36(3): 1081–1094.
• Lackner MA, Rogers AL, Manwell JF, McGowan JG. A new method for improved hub height mean wind speed estimates using short-term hub height data. Renewable Energy 2010; 35(10): 2340–2347.
• Laban ON, Maghanga CM, Joash K. Determination of the surface roughness parameter and wind shear exponent of Kisii Region from the on-site measurement of wind profiles. Journal of Energy 2019; 2019.
• Gualtieri G, Secci S. Comparing methods to calculate atmospheric stability-dependent wind speed profiles: A case study on coastal location. Renewable Energy 2011; 36(8): 2189–2204.
• Ağçay M, Attay F. Türkiye’nin Elektrik Enerjisi Arz Talep Dengesinin Tespiti, Üretim Projeksiyonuna Yönelik Rüzgar Elektrik Santrali Tasarımı RES’in Kurulum Maliyetlerinin ve Üretim Parametrelerinin Analizinin Matlab & Simulink İle Yazılan Programda Yapılması. Master’s thesis. Yıldız Teknik Üniversitesi, Elektronik Mühendisliği Bitirme Tezi. Istanbul, Turkey: 2007.
• Gul M, Tai N, Huang W, Nadeem MH, Yu M. Assessment of wind power potential and economic analysis at hyderabad in pakistan: powering to local communities using wind power. Sustainability 2019; 11(5): 1391.
• Willmott CJ, Matsuura K. Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate research 2005; 30(1): 79–82.
• Hyndman RJ, Koehler AB. Another look at measures of forecast accuracy. International journal of forecasting 2006; 22(4): 679–688.
• Papoulis A, Pillai SU. Probability, random variables, and stochastic processes. Tata McGraw-Hill Education. 2002.
• Soong TT. Fundamentals of probability and statistics for engineers. John Wiley & Sons. 2004.
• DeCarlo LT. On the meaning and use of kurtosis. Psychological methods 1997; 2(3): 292.
• Irwanto M, Gomesh N, Mamat M, Yusoff Y. Assessment of wind power generation potential in Perlis, Malaysia. Renewable and sustainable energy reviews 2014; 38: 296–308.
• Saeidi D, Mirhosseini M, Sedaghat A, Mostafaeipour A. Feasibility study of wind energy potential in two provinces of Iran: North and South Khorasan. Renewable and Sustainable Energy Reviews 2011; 15(8): 3558–3569.