论文标题
基于贝叶斯山脊回归的模型，以预测HVDC网络中的故障位置
Bayesian Ridge Regression Based Model to Predict Fault Location in HVdc Network
论文作者
论文摘要
本文讨论了一种使用单端电流和电压测量值准确估算多末端高压直流电（HVDC）传输网络中故障位置的方法。后的电压和当前签名是多个因素的函数，因此在多末端网络上准确定位故障是具有挑战性的。我们讨论了一种基于数据驱动的贝叶斯回归方法，以准确预测断层位置。提出的算法对测量噪声，故障位置，电阻和电流限制电感的敏感性是在径向三端MTDC网络上进行的。该测试系统在电源系统计算机辅助设计（PSCAD）/电磁瞬变（包括DC（EMTDC））中设计。
This paper discusses a method for accurately estimating the fault location in multi-terminal High Voltage direct current (HVdc) transmission network using single ended current and voltage measurements. The post-fault voltage and current signatures are a function of multiple factors and thus accurately locating faults on a multi-terminal network is challenging. We discuss a novel data-driven Bayes Regression based method for accurately predicting fault locations. The sensitivity of the proposed algorithm to measurement noise, fault location, resistance and current limiting inductance are performed on a radial three-terminal MTdc network. The test system is designed in Power System Computer Aided Design (PSCAD)/Electromagnetic Transients including dc (EMTdc).
