论文标题
高阻抗故障检测通过准静态状态估计：参数误差建模方法
High Impedance Fault Detection Through Quasi-Static State Estimation: A Parameter Error Modeling Approach
论文作者
论文摘要
本文介绍了一个模型，用于使用参数误差建模检测高阻抗故障（HIF）和两步性加权最小二乘状态估计（SE）过程。提出的方案利用相量测量单元和合成测量值来识别每阶段功率流量和注射测量结果，这些测量表明通过$χ^2 $假设测试的参数误差应用于组成的测量误差（CME）。尽管通常分析了电流和电压波形，以用于高阻抗故障检测，但已经固有的SE过程固有的广阔区域功率流量和注射测量值也显示出对现实世界高阻抗故障检测应用的希望。检测后的误差分布共享测量函数误差差异在验证的参数误差诊断中观察到，并且可以应用于HIF识别。此外，与HIF相关的此错误扩展将与测量误差清楚地辨别。案例研究是在Simulink中的33个公共汽车分布系统上进行的。
This paper presents a model for detecting high-impedance faults (HIFs) using parameter error modeling and a two-step per-phase weighted least squares state estimation (SE) process. The proposed scheme leverages the use of phasor measurement units and synthetic measurements to identify per-phase power flow and injection measurements which indicate a parameter error through $χ^2$ Hypothesis Testing applied to the composed measurement error (CME). Although current and voltage waveforms are commonly analyzed for high-impedance fault detection, wide-area power flow and injection measurements, which are already inherent to the SE process, also show promise for real-world high-impedance fault detection applications. The error distributions after detection share the measurement function error spread observed in proven parameter error diagnostics and can be applied to HIF identification. Further, this error spread related to the HIF will be clearly discerned from measurement error. Case studies are performed on the 33-Bus Distribution System in Simulink.
