电力系统自动化
電力繫統自動化
전력계통자동화
AUTOMATION OF ELECTRIC POWER SYSTEMS
2012年
11期
19-22,37
,共5页
风力发电%风功率预测%神经网络%反馈时延神经网络%时间序列
風力髮電%風功率預測%神經網絡%反饋時延神經網絡%時間序列
풍력발전%풍공솔예측%신경망락%반궤시연신경망락%시간서렬
wind power%wind power forecast%neural network%recurrent time-delay neural network%time series
随着风电的大规模发展，准确预测风电场输出功率对于风电场的选址、大规模并网及运行具有重要的作用。文中提出了局部反馈时延神经网络和全局反馈时延神经网络2种动态神经网络预测模型，以适应风功率的时间序列特性，并与静态神经网络预测模型进行了比较。以国内北方某风电场的风功率预测为例，结合气象预报数据进行提前24h的风电输出功率预测，仿真结果表明，动态神经网络在预测具有时间序列特性的风功率时效果优于静态神经网络。
隨著風電的大規模髮展，準確預測風電場輸齣功率對于風電場的選阯、大規模併網及運行具有重要的作用。文中提齣瞭跼部反饋時延神經網絡和全跼反饋時延神經網絡2種動態神經網絡預測模型，以適應風功率的時間序列特性，併與靜態神經網絡預測模型進行瞭比較。以國內北方某風電場的風功率預測為例，結閤氣象預報數據進行提前24h的風電輸齣功率預測，倣真結果錶明，動態神經網絡在預測具有時間序列特性的風功率時效果優于靜態神經網絡。
수착풍전적대규모발전，준학예측풍전장수출공솔대우풍전장적선지、대규모병망급운행구유중요적작용。문중제출료국부반궤시연신경망락화전국반궤시연신경망락2충동태신경망락예측모형，이괄응풍공솔적시간서렬특성，병여정태신경망락예측모형진행료비교。이국내북방모풍전장적풍공솔예측위례，결합기상예보수거진행제전24h적풍전수출공솔예측，방진결과표명，동태신경망락재예측구유시간서렬특성적풍공솔시효과우우정태신경망락。
The precision of wind power forecast is very important in the selection of wind farm site, and in the integration and operation of power system with increasing penetration of wind power. Compared with static neural networks, two dynamic neural network models, locally recurrent time-delay neural network model and globally recurrent time-delay neural network model, are proposed for the forecasting of a wind farm output in order to simulate the time-series characteristic of the generation series. To demonstrate the effectiveness, the models are applied and tested on a wind farm located in the north of China. Base on numerical meteorological predictions, hourly forecasts up to 24 hours ahead are produced for the wind farm. Simulation results demonstrate that the dynamic neural network models outperform the static ones in the forecast of wind power with time-series characteristic.
