基于故障高频电压稀疏量测的直流配电网双极短路故障定位

直流配电网分支密集,支持数据高速通信的测点相对较少,同时故障特性受配电网中电力电子设备控制策略的影响较大,导致传统配电网的故障定位方法难以适用于直流配电网。基于此,文中提出一种适用于直流配电网的双极短路故障定位新算法。基于暂态高频电流回路构建了不受控制策略影响的模块化多电平换流器和DC/DC换流器高频阻抗等值模型,然后,利用小波变换提取稀疏测点处的暂态高频电压形成节点高频电压方程。最后,将节点高频电压方程与基于贝叶斯算法的压缩感知理论相结合,求解节点高频电流稀疏向量,实现基于稀疏量测的准确故障定位。所提算法对测点数量要求低,同时不受换流器控制策略的影响,无需数据严格同步测量,理论上不受过渡电阻和负荷变化的影响。在PSCAD中搭建32节点直流配电网的仿真模型,测试数据验证了所提算法的定位效果。

Bipolar Short-circuit Fault Location for DC Distribution Network Based on Sparse Measurement of Fault High-frequency Voltage

DC distribution networks have many intensive branches and few measuring points that support data high-speed communication. Meanwhile, the fault characteristics are greatly affected by the control strategy of power electronic equipment, which makes the fault location method of the traditional distribution network difficult to be applied to DC distribution network. Based on this, the paper proposes a new bipolar short-circuit fault location algorithm for DC distribution networks. According to the transient high-frequency current loop, the high-frequency impedance equivalent models of modular multilevel converter and DC/DC converter not affected by the control strategy are constructed. Then, the wavelet transform is used to extract the fault high-frequency voltage of the sparse measurement point, which is used to form the node high-frequency voltage equation. Finally, combining the equation with the Bayesian algorithm based compressed sensing theory, the node high-frequency current sparse vector is solved and fault location is achieved accurately based on sparse voltage measurement. The proposed algorithm has low requirements on the number of measuring points and is not affected by the control strategy of converters. It does not need strict data synchronization measurement and is not theoretically affected by the transition resistance. The simulation model of the 32-node DC distribution network is built in PSCAD and the location effectiveness of the proposed algorithm is verified by the test data.