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考虑风电机组故障电压穿越特性的连锁故障关键线路辨识OACSTPCD

Identification of Critical Lines for Cascading Failures Considering Fault Voltage Ride-through Characteristics of Wind Turbines

中文摘要英文摘要

新能源的接入提高了电网连锁故障发生的概率,给故障传播中关键线路的辨识增加了难度.为此,建立了考虑风电机组故障电压穿越特性和线路可靠性的连锁故障仿真模型,从线路失负荷严重程度、失负荷不均匀性和结构脆弱性3个角度建立了线路综合风险指标评价集.基于状态故障网络计及线路失负荷程度和线路失负荷不均匀性,分别定义了失负荷风险指标和不均匀风险指标;基于电气介数提出了考虑分布式新能源接入的脆弱结构指标;同时,考虑电网的网络结构和状态转移特性,采用模糊熵权法定义综合风险指标,以衡量线路开断的综合影响.通过IEEE 39节点和IEEE 118节点系统算例验证所提方法用于关键线路辨识的有效性,且针对关键线路的缓解措施能显著降低大停电风险.

The integration of renewable energy increases the likelihood of cascading failures,posing challenges in identifying critical lines during fault propagation.In light of this,a cascading failure simulation model that incorporates the fault voltage ride-through characteristics of wind turbines and line reliability is built.An indicator evaluation set of line comprehensive risk is also introduced,which encompasses line load loss severity and non-uniformity,and structural vulnerability perspectives.The security risk index and the uneven risk index are defined based on the state-failure-network,the degree of line load loss severity and non-uniformity.Based on the electrical betweenness,a fragile structure index accounting for distributed renewable energy access is proposed.Meanwhile,to measure the overall impact of line disconnection,a fuzzy entropy weight method is employed to define the comprehensive risk index,which considers the network structure and state transition characteristics of the power grid.Numerical examples involving the IEEE 39-bus and IEEE 118-bus systems validate the effectiveness of the proposed method in identifying critical lines.Additionally,mitigation measures for critical lines can significantly reduce the risk of blackouts.

徐箭;贺中豪;廖思阳;邹曜坤;孙元章;杨军

武汉大学电气与自动化学院,湖北省武汉市 430072

连锁故障新能源风电机组故障电压穿越电气介数模糊熵权法

cascading failurerenewable energywind turbinefault voltage ride-throughelectric betweennessfuzzy entropy weight method

《电力系统自动化》 2024 (002)

82-94 / 13

国家自然科学基金委员会-国家电网公司智能电网联合基金资助项目(U22B6006). This work is supported by National Natural Science Foundation of China-State Grid Joint Fund for Smart Grid(No.U22B6006).

10.7500/AEPS20230711001

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