计及输电线路舞动预测的电网短期风险评估

为了准备评估覆冰舞动环境下电网的短期运行风险,保障输电线路的正常运维,提出了一种计及输电线路舞动预测的电网短期风险评估方法。定量地抓住决定事故风险程度的两个因素:故障可能性指标和后果严重度指标,从整体层面把握舞动对电网短期运行风险的影响。其中可能性指标通过BP神经网络预测舞动概率值和通过频次法划分的舞动分区图综合计算得到,后果严重度指标根据输电线路故障断开后的支路过载和母线电压越限指标计算得到。以河南电网的历史舞动数据进行算例分析,结果表明所提方法能有效评估电网的短期运行风险评估,舞动预警和电网风险评估结果可以为电网调度和线路运维人员提供决策支撑,及早调整运行方式或者进行防舞作业,降低舞动对电网安全运行的影响。

Short-term risk assessment of power grid considering galloping prediction of transmission lines

In order to assess the short term operational risk of the power grid under the ice-covered galloping environment and ensure the normal operation and maintenance of transmission lines, a short-term risk assessment method of power grid considering galloping prediction of transmission lines is proposed. It seizes two factors that determine the fault risk degree:the fault likelihood index and the system consequence severity index, which can grasp the impact of galloping on the short term operation of the power grid from an overall perspective. The fault likelihood index is comprehensively determined by the galloping prediction probability calculated through a BP neural network and the historical galloping level of a region which is divided by the frequency method. The system consequence severity index is calculated according to the conductor overload degree and bus voltage out-of-limit degree after power flow transferring. Taking the galloping data of Henan power grid as a case study, the results indicate that the proposed method can effectively evaluate the short term operational risk of power grid. The galloping early warning and risk assessment results can provide decision-making support for power grid dispatchers and transmission line operation staffs, help them adjust operation mode of power grid and (or) implement anti-galloping assignment of transmission lines in advance, to reduce the impact of transmission line galloping on the security operation of power grid. This work is supported by National Key R&D Program of China (No. 2016YFB0900600) and Major and Basic Project of Foresight Science and Technology of State Grid Corporation of China (No. SG20141187).