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浏览/检索结果: 共3条，第1-3条 帮助 限定条件 专题：西安交通大学    第一署名单位    第一作者单位    通讯作者单位     已选(0)清除 条数/页：5101520253035404550556065707580859095100   排序方式：请选择作者升序作者降序题名升序题名降序发表日期升序发表日期降序提交时间升序提交时间降序 Lightning flash algorithm for solving non-convex combined emission economic dispatch with generator constraints 期刊论文 IET GENERATION TRANSMISSION & DISTRIBUTION, 2018, 卷号: 12, 页码: 104-116 作者:  Kheshti, Mostafa;  Kang, Xiaoning;  Li, Jiangtao;  Regulski, Pawel;  Terzija, Vladimir 收藏  |  浏览/下载：50/0  |  提交时间：2019/11/19 fossil-fuelled power plants  power generation protection  dual-objective CEED problem  power generation dispatch  cloud movements  lightning strikes  evolutionary computation  grounding  wind power penetration  dynamic dispatch problems  concave programming  environmental impacts  generator constraints  wind power plants  thunderstorm  global warming  lightning protection  nonconvex CEED  evolutionary lightning flash algorithm  earthing  nonconvex combined emission economic dispatch  multiple fuel options  gaseous pollutants emission  operation constraints  power generation economics   Spinning reserve width of generator unit: concept, properties and applications, part I: concept and properties 期刊论文 Zhongguo Dianji Gongcheng Xuebao/Proceedings of the Chinese Society of Electrical Engineering, 2016, 卷号: 36, 期号: [db:dc_citation_issue], 页码: 4808-4816 作者:  Zhai, Qiaozhu;  Tian, Jianfang;  Mao, Yashan 收藏  |  浏览/下载：3/0  |  提交时间：2019/12/02 Economic dispatch problems  Feasibility condition  Generator unit  Linear programming problem  Spinning reserves  Theoretical foundations  Unit Commitment  Up and down-spinning reserve   Multiple strategies based orthogonal design particle swarm optimizer for numerical optimization 期刊论文 COMPUTERS & OPERATIONS RESEARCH, 2015, 卷号: 60, 期号: [db:dc_citation_issue], 页码: 91-110 作者:  Qin, Quande;  Cheng, Shi;  Zhang, Qingyu;  Wei, Yiming;  Shi, Yuhui 收藏  |  浏览/下载：4/0  |  提交时间：2019/12/02 Particle swarm optimization  Orthogonal design  Opposition-based learning  Global optimization  Economic load dispatch problems  Learning strategy