| 基于强化学习的电力通信网故障恢复方法 |
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| 引用本文： | 贾惠彬,盖永贺,李保罡,郑宏达. 基于强化学习的电力通信网故障恢复方法[J]. 中国电力, 2020, 53(6): 34-40. DOI: 10.11930/j.issn.1004-9649.201907078 |
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| 作者姓名： | 贾惠彬 盖永贺 李保罡 郑宏达 |
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| 作者单位： | 华北电力大学 电气与电子工程学院，河北 保定 071003 |
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| 基金项目： | 国家自然科学基金资助项目(61971190)；中央高校基本科研业务费专项资金资助项目(2017MS113) |
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| 摘 要： | 大规模的自然灾害或恶意攻击会引发智能电网中电力通信网的严重故障，如果不能及时恢复，将给电力系统的安全稳定运行带来极大的风险。为解决电力通信网络大规模故障后的恢复问题，在通信链路恢复资源有限的约束下，建立以失效业务数量恢复最大化为目标的电力通信网大规模故障后链路恢复模型。针对该模型，提出一种基于强化学习的启发式算法，该算法利用链路恢复资源和故障链路在失效业务中的重要度，设置奖惩函数和选择规则，并累积奖励最大值，得到最优链路恢复组合。实验结果表明，提出的电力通信网故障恢复方法可以在恢复资源有限的约束下，恢复较多的失效业务。
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| 关 键 词： | 智能电网 电力通信网 大规模故障 强化学习 故障恢复 |
| 收稿时间： | 2019-07-09 |
| 修稿时间： | 2019-08-26 |
Power Communication Network Recovery from Large-Scale Failures Based on Reinforcement Learning |
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| JIA Huibin,GAI Yonghe,LI Baogang,ZHENG Hongda. Power Communication Network Recovery from Large-Scale Failures Based on Reinforcement Learning[J]. Electric Power, 2020, 53(6): 34-40. DOI: 10.11930/j.issn.1004-9649.201907078 |
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| Authors: | JIA Huibin GAI Yonghe LI Baogang ZHENG Hongda |
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| Affiliation: | School of Electrical & Electronic Engineering, North China Electric Power University, Baoding 071003, China |
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| Abstract: | Natural disasters or malicious attacks may cause large-scale failures of power communication networks in smart grid systems, which will impose big risks on the security and stability of the power system operation unless the communication network is recovered immediately. In order to solve the recovery problem of power communication network after large-scale failure, under the constraints of limited link recovery resources, a link recovery model for large-scale failures in power communication networks is established with the objective to maximize the recovery amount of failed services. Regarding this model, a heuristic algorithm based on reinforcement learning is proposed, in which the link recovery resources and the degree of importance of the damaged link in the failed service are taken into account to set the reward and penalty functions as well as the selection rules. Then the optimal link recovery combination is obtained through the accumulation of the maximum reward values. The simulation results show that the power communication network failure recovery algorithm proposed in this paper can restore quite considerable failed services quickly with limited resources. |
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| Keywords: | smart grid power communication network large-scale fault reinforcement learning fault recover |
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