作　　者： (王鹤鸣）; (王灵矫）; (谭貌）; (夏伟）; (郭华）;

机构地区： 湘潭大学信息工程学院,湖南湘潭411105

出　　处： 《云南大学学报（自然科学版）》 2017年第5期760-767,共8页

摘　　要： 配电网的故障恢复是一项重要且复杂的任务,配电网能否可靠快速地从故障状态中恢复对配电网的安全性与可靠性有着极大的影响.由于故障恢复操作需要对系统数据以及网络结构进行分析再作出对应决策,这种分析过程复杂度较高,因此会对配电网的故障恢复速度造成极大影响.为解决这种问题提出了基于多目标优化回声状态网络的配电网故障恢复算法,通过ESN结合多目标优化算法NSGA-Ⅱ进行非监督学习,在不需要训练样本输出的情况下能实现配电网对故障恢复方案的学习,从而实现系统对故障信息作出快速响应.通过Matlab/Simulink平台的仿真实验结果证明了本算法能有效实现系统对故障信息的学习并对故障信息输入作出立即响应. The service restoration of distributed system is an important and complex task.The ability of serv-ice restoration has great effect on the reliability and safety of the distributed system.The restoration operation re-quires massive analysis for data from the system information, which can lead to high complexity and long operatingperiod.In order to solve this problem,a service restoration algorithm based on ESN and MOA is presented.This al-gorithm combines ESN and NSGA- II to accomplish the unsupervised learning for the information input whichhas no need for the output of the training sample.The experiment based on Matlab/Simulink has showed that thisalgorithm can make the system learn the restoration method effectively and make immediate response to the inputinformation.

关 键 词： 机器学习 神经网络 故障恢复 智能电网