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考虑风电不确定性的电网状态检修策略 被引量:5

Condition-based Maintenance Scheduling for Power Transmission System Considering Wind Power Uncertainty
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摘要 设备状态检修背景下,随着风电等高比例新能源并网发电,电网检修计划决策中输变电设备个体与电网运行整体间的矛盾和冲突越来越难以平衡,使电网检修计划决策面临严峻挑战。对此,文中提出考虑风电不确定性的电网状态检修策略。首先,基于设备状态监测信息和状态评价技术,建立设备故障率及其与检修策略间关系的数学表达;其次,依据风险理论给出电网故障风险和检修风险的数学模型,并基于次时间尺度的安全约束机组组合模型评估其中的电网运行损失;最后,提出以电网故障风险和检修风险之和最小为目标的考虑风电不确定性的电网状态检修模型,并采用修改的IEEE-30节点系统算例验证所提模型的有效性。 With the high proportion of new energy such as wind power integration into power system and the increasing of equipment condition-based maintenance scheduling,the contradiction and conflict between the transmission equipment and the power transmission system is more difficult to balance and the decision of power transmission system maintenance scheduling is challenged.To meet this challenge,A condition-based maintenance scheduling of power transmission system is proposed considering the uncertainty of wind power.Firstly,the model of equipment failure rate and its relationship to maintenance schedule is established based on the equipment condition monitoring and evaluation technologies.Secondly,the expressions of the power system failure risk and maintenance risk are given according to the risk theory,in which the power system operating cost is evaluated based on the security constrained unit commitment.Finally,the model and method for the condition-based maintenance scheduling of power transmission system considering wind power uncertainty is presented with the goal of minimizing the sum of power system failure and maintenance risks,and a modified IEEE-30 test system is used to exhibit the effectiveness of the proposed approach.
作者 孙端航 李本新 SUN Duanhang;LI Benxin(Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology,Ministry of Education(Northeast Electric Power University),Jilin Jilin 132012)
出处 《东北电力大学学报》 2023年第4期65-73,共9页 Journal of Northeast Electric Power University
基金 吉林省科技发展计划项目(20210508031RQ)。
关键词 输电设备检修 状态检修 机组组合 鲁棒优化 电网风险 Transmission maintenance scheduling Condition-based maintenance scheduling Unit commitment Robust optimization Grid risk
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