电测与仪表
電測與儀錶
전측여의표
Electrical Measurement & Instrumentation
2015年
18期
68-73,89
,共7页
可再生能源发电%微源模型%小波神经网络%功率预测
可再生能源髮電%微源模型%小波神經網絡%功率預測
가재생능원발전%미원모형%소파신경망락%공솔예측
renewable energy power generation%micro source model%wavelet neural network%power prediction
针对可再生能源发电具有功率周期变化与对环境敏感的双重性，提出将微源控制（ MSC ）用入分布式电网功率预测的小波神经网络模型学习算法。该算法在灵活处理功率局部与周期特性的基础上，结合环境因素对功率变化的影响，引入关联因子优化权重，得出最终预测结果。通过对实际微网系统的仿真测试，并与BP神经网络与GRNN模型进行比较，研究结果表明：MSC-WNN模型在三次测试中相对误差均在－1％～1％以内，说明了其具有较高预测精度和良好的鲁棒性能。
針對可再生能源髮電具有功率週期變化與對環境敏感的雙重性，提齣將微源控製（ MSC ）用入分佈式電網功率預測的小波神經網絡模型學習算法。該算法在靈活處理功率跼部與週期特性的基礎上，結閤環境因素對功率變化的影響，引入關聯因子優化權重，得齣最終預測結果。通過對實際微網繫統的倣真測試，併與BP神經網絡與GRNN模型進行比較，研究結果錶明：MSC-WNN模型在三次測試中相對誤差均在－1％～1％以內，說明瞭其具有較高預測精度和良好的魯棒性能。
침대가재생능원발전구유공솔주기변화여대배경민감적쌍중성，제출장미원공제（ MSC ）용입분포식전망공솔예측적소파신경망락모형학습산법。해산법재령활처리공솔국부여주기특성적기출상，결합배경인소대공솔변화적영향，인입관련인자우화권중，득출최종예측결과。통과대실제미망계통적방진측시，병여BP신경망락여GRNN모형진행비교，연구결과표명：MSC-WNN모형재삼차측시중상대오차균재－1％～1％이내，설명료기구유교고예측정도화량호적로봉성능。
Considering the duality of cyclical changes and environmentally sensitive in renewable energy power genera -tion, micro source control ( MSC) is used into the learning algorithm of wavelet neural network model to predict dis-tributed power grid .Based on the flexibility of dealing with the local and cycle nature of the power , and combined with the impact of environmental factors on the power change , the correlation factor is introduced into the algorithm to optimize the weight and the final prediction result is obtained .Through the actual network system simulation test , and compared with BP neural network and GRNN model , the results show that the relative errors of MSC -WNN model in three tests were within -1%to 1%, showing its high precision and good robust performance .
