摘要
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。
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 .
出处
《电测与仪表》
北大核心
2015年第18期68-73,89,共7页
Electrical Measurement & Instrumentation
基金
中央高校基本科研业务费专项资金资助(NO.13MS112)
关键词
可再生能源发电
微源模型
小波神经网络
功率预测
renewable energy power generation, micro source model, wavelet neural network, power prediction
