为解决现有模型在风速拟合中灵活性差和精度低的问题,基于Topp-Leone分布族引入Weibull分布,构建一种性能更优的TLW（Topp-Leone-Weibull）分布模型。考虑到模型参数的增加会带来计算复杂度的提升,采用差分进化算法对模型参数进行优化以确保计算效果。同时,选取福建近海某区域8年间的风速数据进行仿真研究,通过均方根误差（RMSE）、平均绝对误差（MAE）、判定系数检验（R2）和卡方检验（X2）等指标对模型进行全面评估。结果表明,与传统Weibull分布及其他类型分布相比,TLW分布在拟合0~5 m/s低风速上凸段和15~20 m/s高风速下凹段存在不规则波动性时展现出更强的适应性,能有效捕捉风速变化趋势,显著提高拟合精度,更好地跟踪实际风速分布。
Abstract
In order to solve the problem of poor flexibility and low accuracy of wind speed probability distribution model in fitting wind speed, a better performance TLW（Topp-Leone-Weibull） distribution model is constructed based on the Topp-Leone distribution family and introducing Weibull. Considering that the increase of model parameters brings about the increase of computational complexity, differential evaluation algorithm is used to optimize the model parameters to ensure the computational effect. Meanwhile, the wind speed data of a region offshore of Fujian over 8 years are selected for the simulation study, and the model is comprehensively evaluated by root mean square error（RMSE）, mean absolute error（MAE）, coefficient of determination test（R²） and chi-square test （X²）. The results show that, compared with the traditional Weibull distribution and other types of distributions, the TLW distribution mode1 shows stronger adaptability in fitting the irregular fluctuations that exist in the upper convex section of low wind speeds of 0-5 m/s and the lower concave section of high wind speeds of 15-20 m/s, and it can effectively capture the trend of the wind speed changes, significantly improve the fitting accuracy, and better track the actual wind speed distribution.
关键词
风速 /
启发式算法 /
威布尔分布 /
Topp-Leone分布 /
风电资源评估 /
拟合
Key words
wind speed /
heuristic algorithms /
Weibull distribution /
Topp-Leone distribution /
wind power resource assessment /
fitting
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参考文献
[1] 吉会峰, 刘吉堂, 宋心刚, 等. 基于ERA5数据的江苏海域风能资源评估[J]. 太阳能学报, 2023, 44(1): 320-324.
JI H F, LIU J T, SONG X G, et al.Evaluation of wind energy resources in Jiangsu sea area based on ERA5 data[J]. Acta energiae solaris sinica, 2023, 44(1): 320-324.
[2] BOADU S, OTOO E.A comprehensive review on wind energy in Africa: challenges, benefits and recommendations[J]. Renewable and sustainable energy reviews, 2024, 191: 114035.
[3] GAO Q, BECHLENBERG A, JAYAWARDHANA B, et al.Techno-economic assessment of offshore wind and hybrid wind-wave farms with energy storage systems[J]. Renewable and sustainable energy reviews, 2024, 192: 114263.
[4] 王旭光, 张可, 李潇, 等. 基于变量相关注意力机制的短期风速预测[J]. 太阳能学报, 2023, 44(8): 467-476.
WANG X G, ZHANG K, LI X, et al.Short-term wind speed forecasting based on variable correlation attention mechanism[J]. Acta energiae solaris sinica, 2023, 44(8): 467-476.
[5] 陈绍南, 陈碧云, 韦化, 等. 不规则风速概率分布的混合半云建模方法[J]. 中国电机工程学报, 2015, 35(6): 1314-1321.
CHEN S N, CHEN B Y, WEI H, et al.Mixed half-cloud modeling method for irregular probability distribution of wind speed[J]. Proceedings of the CSEE, 2015, 35(6): 1314-1321.
[6] HE J Y, CHAN P W, LI Q S, et al.Assessment of urban wind energy resource in Hong Kong based on multi-instrument observations[J]. Renewable and sustainable energy reviews, 2024, 191: 114123.
[7] TIAM KAPEN P, JEUTHO GOUAJIO M, YEMÉLÉ D. Analysis and efficient comparison of ten numerical methods in estimating Weibull parameters for wind energy potential: application to the city of Bafoussam, Cameroon[J]. Renewable energy, 2020, 159: 1188-1198.
[8] LI Y, HUANG X, TEE K F, et al.Comparative study of onshore and offshore wind characteristics and wind energy potentials: a case study for southeast coastal region of China[J]. Sustainable energy technologies and assessments, 2020, 39: 100711.
[9] 周齐, 王海云, 王维庆. 基于峰型辨识的风速概率分布建模[J]. 太阳能学报, 2021, 42(8): 355-360.
ZHOU Q, WANG H Y, WANG W Q.Modeling of wind speed probability distribution based on peak pattern identification[J]. Acta energiae solaris sinica, 2021, 42(8): 355-360.
[10] 王文新, 陈可欣, 白杨, 等. 基于实测数据的呼和浩特近郊风速分布模型对比研究[J]. 太阳能学报, 2021, 42(9): 370-376.
WANG W X, CHEN K X, BAI Y, et al.Comparative study on wind speed distribution models of Hohhot suburb based on measured data[J]. Acta energiae solaris sinica, 2021, 42(9): 370-376.
[11] 黄武枫, 郑含博, 杜齐, 等. 基于Nakagami分布的风速概率分布拟合研究[J]. 电测与仪表, 2024, 61(2): 76-82.
HUANG W F, ZHENG H B, DU Q, et al.Study on fitting of wind speed probability distribution based on Nakagami distribution[J]. Electrical measurement & instrumentation, 2024, 61(2): 76-82.
[12] TSVETKOVA O, OUARDA T B M J. Use of the Halphen distribution family for mean wind speed estimation with application to Eastern Canada[J]. Energy conversion and management, 2023, 276: 116502.
[13] AHSAN-UL-HAQ M, CHOUDHARY S M, AL-MARSHADI A H, et al. A new generalization of Lindley distribution for modeling of wind speed data[J]. Energy reports, 2022, 8: 1-11.
[14] WANG C, ZHANG S H, XIAO L, et al.Wind speed forecasting based on multi-objective grey wolf optimisation algorithm, weighted information criterion, and wind energy conversion system: a case study in Eastern China[J]. Energy conversion and management, 2021, 243: 114402.
[15] KHALED KHAMEES A, ABDELAZIZ A Y, ALI Z M, et al.Mixture probability distribution functions using novel metaheuristic method in wind speed modeling[J]. Ain shams engineering journal, 2022, 13(3): 101613.
[16] WANG J Z, HUANG X J, LI Q W, et al.Comparison of seven methods for determining the optimal statistical distribution parameters: a case study of wind energy assessment in the large-scale wind farms of China[J]. Energy, 2018, 164: 432-448.
[17] PETROVIĆ A, ĐURIŠIĆ Ž. Genetic algorithm based optimized model for the selection of wind turbine for any site-specific wind conditions[J]. Energy, 2021, 236: 121476.
[18] ALRASHIDI M, RAHMAN S, PIPATTANASOMPORN M.Metaheuristic optimization algorithms to estimate statistical distribution parameters for characterizing wind speeds[J]. Renewable energy, 2020, 149: 664-681.
[19] XIONG G J, XIE X, YUAN Z X, et al.Differential evolution-based optimized hierarchical extreme learning machines for fault section diagnosis of large-scale power systems[J]. Expert systems with applications, 2023, 233: 120937.
[20] MUHAMMAD M, LIU L X, ABBA B, et al.A new extension of the topp: leone-family of models with applications to real data[J]. Annals of data science, 2023, 10(1): 225-250.
[21] AL-SHOMRANI A, ARIF O, SHAWKY A, et al.Topp-leone family of distributions: some properties and application[J]. Pakistan journal of statistics and operation research, 2016, 12(3): 443.
基金
国家自然科学基金（52367006）; 贵州省科技计划（黔科合基础-ZK[2022]一般121）
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