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1. 发明申请

US20160125097A1 OPTIMIZING POWER FLOWS USING HARMONY SEARCH WITH MACHINE LEARNING 审中-公开
标题翻译：优化使用和谐搜索与机器学习的功率流
公开(公告)号：US20160125097A1
公开(公告)日：2016-05-05
申请号：US14933696
申请日：2015-11-05
申请人： NEC Laboratories America, Inc.
发明人： Yanyi He , Ratnesh Sharma
IPC分类号： G06F17/30 , G05B19/042 , G06N99/00
CPC分类号： G06F17/30979 , G05B19/042 , G05B2219/2639 , G06N99/005
摘要： Systems and methods for optimizing power flows using a harmony search, including decoupling phases in a multi-phase power generation system into individual phase agents in a multi-phase power flow model for separately controlling at least one of phase variables or parameters. One or more harmony segments from harmony memory are ranked and selected based on a utility value determined for each of the decoupled phases. A harmony search with gradient descent learning is performed to move the selected harmony segments to a better local neighborhood. A new utility value for each of the selected segments is determined based on historical performance, and the harmony memory is iteratively updated if one or more of the new utility values are higher than a utility value of a worst harmony segment stored in the harmony memory.
摘要翻译：用于使用和谐搜索优化功率流的系统和方法，包括在多相功率流模型中的多相发电系统中的去耦合相到单相相位试剂中，用于分别控制相位变量或参数中的至少一个。根据为每个去耦合阶段确定的效用值，对来自和谐记忆的一个或多个和声段进行排名和选择。执行具有梯度下降学习的和声搜索以将所选择的和声段移动到更好的本地邻域。基于历史性能确定每个所选段的新效用值，并且如果一个或多个新效用值高于和谐存储器中存储的最差和谐段的效用值，则迭代地更新和谐存储器。

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