CAJ | 학술논문

模拟风速时间序列在含风电场电力系统的规划及评估等领域应用广泛。传统风速序列建模的一阶马尔可夫链模型无法保留时间序列的自相关特性，同时不能反映出实际风速随季节、天气等因素的变化情况。文中通过将一年分为12个时间段及将一日分为4个时段，在传统一阶马尔可夫链中引入了风速的季节特性和日特性；同时，考虑风速与降水量的关联，引入了风速的干湿特性。在此基础上，提出了风速时间序列模拟的改进一阶马尔可夫链模型。仿真结果表明，该改进模型不仅较好地保留了观测风速的自相关特性，而且提高了模拟风速序列的精度。
모의풍속시간서렬재함풍전장전력계통적규화급평고등영역응용엄범。전통풍속서렬건모적일계마이가부련모형무법보류시간서렬적자상관특성，동시불능반영출실제풍속수계절、천기등인소적변화정황。문중통과장일년분위12개시간단급장일일분위4개시단，재전통일계마이가부련중인입료풍속적계절특성화일특성；동시，고필풍속여강수량적관련，인입료풍속적간습특성。재차기출상，제출료풍속시간서렬모의적개진일계마이가부련모형。방진결과표명，해개진모형불부교호지보류료관측풍속적자상관특성，이차제고료모의풍속서렬적정도。
Synthetic wind speed time series are widely used in the planning and assessment of the wind power integrated system.The original first-order Markov chain approach is not able to retain the autocorrelation property of observed wind speed data.Meanwhile,it is impossible for it to reflect the variation of wind speed due to changes of season and weather.An advanced first-order Markov chain approach is then put forward,in which the seasonal characteristics and diurnal behavior of wind are modeled by dividing a year into 1 2 and a day into 4 equal parts,respectively.Also,the relationship between wind and precipitation is taken into account.A comparison between the advanced approach and the original one shows that this advanced first-order Markov chain approach can not only retain the autocorrelation property of observed wind speed data fairly well,but it has improved the accuracy of the wind series generated as well.