基于反向传播神经网络的发电机进相能力建模研究Modeling of Generator Leading Phase Ability Based on Back Propagation Neural Network
王成亮;王宏华;徐钢;
摘要(Abstract):
提出一种基于反向传播神经网络(back propagationneural network,BPNN)的发电机进相能力建模新方法。该BPNN含2个隐层和1个输出层,以发电机有功和无功功率为输入,以发电机功角、电网电压为输出。以典型工况下的发电机进相运行试验结果作为训练样本和测试样本,建立了某600 MW发电机进相能力BPNN模型,从收敛精度最优出发,优化设计了模型的隐层数、神经元数、传递函数。建模实例表明,提出的建模方法精度高、泛化能力强,能有效克服传统分析方法的局限性,有推广应用价值。
关键词(KeyWords): 反向传播神经网络;发电机进相;泛化
基金项目(Foundation):
作者(Author): 王成亮;王宏华;徐钢;
Email:
DOI: 10.13335/j.1000-3673.pst.2011.11.027
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