基于贝叶斯岭回归的高压直流输电网故障定位预测模型
Bayesian Ridge Regression Based Model to Predict Fault Location in HVdc Network
摘要：
本文讨论了一种利用单端电流和电压测量准确估计多端高压直流输电网故障位置的方法。故障后的电压和电流特征是多种因素的函数，因此在多端网络上准确定位故障是具有挑战性的。我们讨论了一种新的基于数据驱动的贝叶斯回归方法，用于准确预测故障位置。在一个径向三端MTdc网络上进行了该算法对测量噪声、故障定位、电阻和限流电感的灵敏度分析。该测试系统是在电力系统计算机辅助设计（PSCAD）/电磁暂态包括直流（EMTdc）。
英文摘要：
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).
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