电力系统自动化
電力繫統自動化
전력계통자동화
AUTOMATION OF ELECTRIC POWER SYSTEMS
2015年
16期
29-36
,共8页
气象测量场%风电爬坡%经验正交函数%区间数%非线性回归
氣象測量場%風電爬坡%經驗正交函數%區間數%非線性迴歸
기상측량장%풍전파파%경험정교함수%구간수%비선성회귀
meteorological measurement field%wind ramp%empirical orthogonal function (EOF)%interval number%nonlinear regression
随着大规模风电接入电网，风电爬坡事件的风险不断增大，提高爬坡时段风功率预测精度对电网安全经济运行具有重要作用。提出了一种基于气象测量场的爬坡时段区域风功率预测方法。考虑爬坡时段风速场的动态变化，利用经验正交函数分解，将风速资料阵分解成不同空间模态和主分量，通过多元非线性逐步回归方法建立风速场主分量和区域风功率间的映射关系。考虑风速预测误差，采用区间正交函数分解，将上述模型扩展为处理非确定性数据的预测方法。实际区域风功率预测结果表明，所提出的方法能够显著提高风电爬坡时段风功率预测的精度，对存在风速预测误差的情况具有较强的鲁棒性。
隨著大規模風電接入電網，風電爬坡事件的風險不斷增大，提高爬坡時段風功率預測精度對電網安全經濟運行具有重要作用。提齣瞭一種基于氣象測量場的爬坡時段區域風功率預測方法。攷慮爬坡時段風速場的動態變化，利用經驗正交函數分解，將風速資料陣分解成不同空間模態和主分量，通過多元非線性逐步迴歸方法建立風速場主分量和區域風功率間的映射關繫。攷慮風速預測誤差，採用區間正交函數分解，將上述模型擴展為處理非確定性數據的預測方法。實際區域風功率預測結果錶明，所提齣的方法能夠顯著提高風電爬坡時段風功率預測的精度，對存在風速預測誤差的情況具有較彊的魯棒性。
수착대규모풍전접입전망，풍전파파사건적풍험불단증대，제고파파시단풍공솔예측정도대전망안전경제운행구유중요작용。제출료일충기우기상측량장적파파시단구역풍공솔예측방법。고필파파시단풍속장적동태변화，이용경험정교함수분해，장풍속자료진분해성불동공간모태화주분량，통과다원비선성축보회귀방법건립풍속장주분량화구역풍공솔간적영사관계。고필풍속예측오차，채용구간정교함수분해，장상술모형확전위처리비학정성수거적예측방법。실제구역풍공솔예측결과표명，소제출적방법능구현저제고풍전파파시단풍공솔예측적정도，대존재풍속예측오차적정황구유교강적로봉성。
With large scale wind farms integrating into electric power systems,the risks caused by the wind power ramp continuously increases.Accurate wind power ramp forecasting is of great importance for the secure and economic operation of power systems.A regional wind power forecasting method during the ramp periods is proposed by using meteorological measurement field data.The wind speed matrix extracted from the measurement field is decomposed into various spatial modes and principal components through empirical orthogonal function(EOF)decomposition,which encodes regional wind speed field dynamics during ramp periods.The mapping between the decomposed principal components and regional wind power is constructed by using multiple variable nonlinear regression method.To address wind speed prediction error,the interval EOF decomposition is used,adapting the proposed forecasting method to the uncertainty of measurement data.The forecasting results from a region with multiple wind farms demonstrate that the proposed method significantly improves the precision of regional wind power forecasting during ramp periods,and shows strong robustness against wind speed prediction error.
