基于PMU数据的500kv实际TLS阴天电晕损耗在线研究

A. Diachenko, Y. Kononov, O. Rybasova, A.V. Petrov
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引用次数: 2

摘要

相量测量单元(PMUs)和广域监测系统(WAMS)无疑是电力系统中的重要技术[1]。其中一种实现是使用来自发送端和接收端的电流和电压相量矢量进行在线参数识别[2]。输电线路(TL)参数在线更新可以帮助解决不同的任务,如状态估计、潮流分析和Q/V优化。更新后的TL参数可以为故障定位提供数据。使用FACTS器件提供了沿TL自动控制工作电压的可能性[3]。Q/V优化不仅需要更新有源电阻,还需要更新电晕损耗(CL)值以及CL与工作电压(OV)的依赖关系。众所周知,瞬时CL值在很大程度上取决于TL沿线的天气条件(风速、空气湿度、环境温度、降水等)和线路沿线的电压分布。由于沿TL商业安装气象站的挑战,基于PMU测量的TL参数识别是最可行和有效的解决方案。有一些文献将CL和OV关系考虑为由于pi电路的二次函数[2],[4]。这些论文建议用TL电导来定义CL。已经证明CL和OV之间的真实依赖关系构成4-8阶多项式[5]。鉴于此,天气越好,观测到的阶数就越高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Online Corona Losses Investigation During Overcast In Real 500 KV TLS Based On PMU Data
Phasor measurement units (PMUs) and wide area monitoring systems (WAMS) are undoubtedly the important technologies in power systems [1]. One of their implementations is online parameter identification using current and voltage phasor vectors from sending and receiving ends [2]. Transmission line (TL) parameters online updating could help to solve different tasks, such as state estimation, power flow analysis, and Q/V optimization. The updated TL parameters could provide the data to identify the fault localization. Using FACTS devices gives a possibility to automatic control the operating voltage along the TL [3]. For the task of Q/V optimization not only active resistance updating is required but also renewing the corona losses (CL) value and the dependence between CL and operating voltage (OV). It is well known that instant CL value depends heavily on the weather conditions (wind velocity, air humidity, ambient temperature, precipitation, etc.) along the TL and on the voltage profile along the line. Due to the challenge in commercially installing weather stations along the TL, TL parameters identification based on PMU measurements is the most viable and effective solution. There are some papers considering CL and OV relation as a quadratic function due to a pi-circuit [2], [4]. These papers suggest defining CL from TL conductance. It has been established that real dependence between CL and OV constitutes the 4-8 order polynomial [5]. Given that, the better the weather is, the higher the order is observed.
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