CAJ | 학술논문

风电场真实的历史运行数据是准确研究风电功率预测、出力特性及对系统影响的基础。针对目前限风情况下风电场历史运行数据存在的不良数据的实际问题，提取了不良数据的时序特性，采用分段判定方法进行识别；基于可用的历史数据，一方面利用风电场出力之间的延时相关性还原重构缺失数据，另一方面利用风电自身出力数据，采用基于自回归滑动平均(ARMA)模型的双向权重比重构方法重构完整的时间序列。以中国某风电基地实际风电场历史运行数据为算例，采用所提出的方法可以有效识别并重构缺失数据。文中比较了两种重构方法的重构效果，并给出了各自的适应性。
풍전장진실적역사운행수거시준학연구풍전공솔예측、출력특성급대계통영향적기출。침대목전한풍정황하풍전장역사운행수거존재적불량수거적실제문제，제취료불량수거적시서특성，채용분단판정방법진행식별；기우가용적역사수거，일방면이용풍전장출력지간적연시상관성환원중구결실수거，령일방면이용풍전자신출력수거，채용기우자회귀활동평균(ARMA)모형적쌍향권중비중구방법중구완정적시간서렬。이중국모풍전기지실제풍전장역사운행수거위산례，채용소제출적방법가이유효식별병중구결실수거。문중비교료량충중구방법적중구효과，병급출료각자적괄응성。
Wind farm”s real historical operating data is the basis of accurate prediction for wind power,output characteristics and the influence on the power system.Due to the fact that some historical operating data are mixed with missing and bad data, especially curtailed wind power data,this paper aims to extract the timing characteristics of curtailed wind power data,analyze them with segmental identification method and reconstruct fault data by two methods based on available historical data.One uses delayed correlation between wind farms”output to reconstruct data and the other adopts bidirectional time series weight ratio of auto-regressive moving average(ARMA)model to reconstruct time series by the wind farm output data itself.Finally, the real wind farm historical operating data in a China wind power base is studied as an example,which shows that proposed methods can identify and reconstruct the missing data effectively.