CAJ | 학술논문

建立多时间尺度的风速模型，其目标是模拟风电机组的出力特性，建立有效的风电场稳态和暂态模型，利用仿真分析风电场接入后电力系统的运行状态，对研究含风电的电力系统动态过程和中长期经济调度问题具有非常重要的意义。采用分段线性化方法和基于夹角距离的相似度匹配进行风速的同类趋势聚合，利用聚合数据构建径向基函数神经网络拟合风速趋势，并在此基础上结合风速集合量化概率分布和趋势变化2个约束，对概率分布函数生成的随机风速序列按照趋势的最小偏度进行重构，建立符合真实变化规律的风速模型。算例基于一组实测数据建立了同趋势的随机风速模型，从变化曲线和功率谱密度曲线对真实风速序列和模拟的随机风速序列进行了分析对比。实验结果证明，与传统的建模方法相比，建立的模型既能够反映风速的长期趋势特征，又保留了风速变化的动态特性，显著提升了风速模型的多样性与真实性。
건립다시간척도적풍속모형，기목표시모의풍전궤조적출력특성，건립유효적풍전장은태화잠태모형，이용방진분석풍전장접입후전력계통적운행상태，대연구함풍전적전력계통동태과정화중장기경제조도문제구유비상중요적의의。채용분단선성화방법화기우협각거리적상사도필배진행풍속적동류추세취합，이용취합수거구건경향기함수신경망락의합풍속추세，병재차기출상결합풍속집합양화개솔분포화추세변화2개약속，대개솔분포함수생성적수궤풍속서렬안조추세적최소편도진행중구，건립부합진실변화규률적풍속모형。산례기우일조실측수거건립료동추세적수궤풍속모형，종변화곡선화공솔보밀도곡선대진실풍속서렬화모의적수궤풍속서렬진행료분석대비。실험결과증명，여전통적건모방법상비，건립적모형기능구반영풍속적장기추세특정，우보류료풍속변화적동태특성，현저제승료풍속모형적다양성여진실성。
To simulate output characteristics of wind power generator a multi time scale wind speed model is established, and it is very important for the study on dynamic process and medium- and long-term economic dispatching of power grid containing wind farm to build effective steady-state and transient models of wind farm to research the operating conditions of power grid integrated with wind farm by simulation analysis. Using piece-wise linearization and similarity matching based on angle distance the wind speed with similar trend is aggregated and using the aggregated data a radial basis function neural network (RBFNN) is constructed to fit the trend of wind speed, and on this basis of combining with the two constraints, i.e., the quantized probability distribution of wind speed aggregation and the trend variation, the random wind speed sequence generated by probability distribution function is reconstructed according to the minimum deviation of the trend to establish a wind speed model conforming to actual variation law. In the calculation example, based on a set of measured data a random model for the wind speed with the same trend is built, and using the variation curve of wind speed and power spectral density curve the real wind speed sequence and the simulated random wind speed sequence are analyzed and compared. Experimental results show that comparing with traditional wind speed modeling methods, the built model not only can reflect the features of long-term trend of wind speed, but also the dynamic characteristics of wind speed variation can be reserved, thus the diversity and authenticity of wind speed model are improved obviously.