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基于BP神经网络的城网供电可靠性预测方法 总被引:4,自引:5,他引:4
传统的供电可靠性评估方法是以准确的配电网结构和多年的元件可靠性历史数据为基础的,难以实现城市复杂配电网远期供电可靠性指标的预测。为此文章提出一种基于BP神经网络的城市电网供电可靠性预测方法,首先找出影响供电可靠性指标的几个主要特征量,包括最大负荷、架空线平均长度、线上平均分段开关台数、线上平均联络开关台数、线路平均配变台数和线路平均配变容量,将这些特征量的历史数据作为输入样本对人工神经网络进行训练,利用训练好的网络就可以预测规划目标年的城市电网供电可靠性指标。对某城市电网的应用结果表明该方法是有效的,所采用的BP神经网络具有较好的收敛性。通过对影响供电可靠性的相关因素进行灵敏度分析还可以获得对供电可靠性指标较敏感的相关特征量,供电企业可以据此制定提高可靠性的相关措施。 相似文献
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To obtain data for experiments and applications of HPR20PLUS on-line mass spectrometer in low pressure. The gas, standard gas, and breath gas of low pressure cabin were analyzed respectively in unman and manned experiments under different low pressure. The accuracy and stability of HPR20PLUS On-line Mass Spectrometer in unman experiments under different low pressure were conformed to the requirements of HPR20PLUS on-line mass spectrometer, and the changes of breath gas in manned experiments were accorded with the laws of human experiments . Performance tests of HPR20PLUS on-line mass spectrometer in low pressure is excellent , and HPR20 PLUS on-line mass spectrometer can be used for human experiments under low pressure . 相似文献