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首页> 外文期刊>Asian Journal of Control >A DISTRIBUTED STATE ESTIMATION APPROACH TO CONDITION MONITORING OF NONLINEAR ELECTRIC POWER SYSTEMS
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A DISTRIBUTED STATE ESTIMATION APPROACH TO CONDITION MONITORING OF NONLINEAR ELECTRIC POWER SYSTEMS

机译：非线性电力系统状态监测的分布状态估计方法

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摘要

This paper analyzes distributed state estimation methods for condition monitoring of electric power transmission and distribution systems. When a fault occurs in such large-scale systems, it is usually difficult to detect it and to determine its exact position. Moreover, due to the cost of installation and maintenance of measurement devices and due to the excessive size of the electric power grid, the complete monitoring of the associated infrastructure is impractical. Therefore, to monitor the condition of the power grid, some form of estimation is required. As suitable approaches for distributed state estimation this paper proposes the extended information filter (EIF) and the unscented information filter (UIF). The Extended Information Filter is actually an implementation of distributed extended Kalman filtering while the unscented information filter is an implementation of distributed unscented Kalman filtering. With the use of the aforementioned filtering algorithms on processing units located at different parts of the power grid, one can produce local estimates of the system's state vector which in turn can be fused into an aggregate state estimation. The produced global state estimate enables continuous monitoring of the condition of the electric power system and early fault diagnosis if used by a suitable fault detection and isolation algorithm.
机译：本文分析了用于电力传输和配电系统状态监测的分布式状态估计方法。当在这样的大型系统中发生故障时，通常很难检测到故障并确定其确切位置。此外，由于安装和维护测量设备的成本以及由于电网的尺寸过大，对相关基础设施进行完全监视是不切实际的。因此，为了监视电网状况，需要某种形式的估计。作为分布式状态估计的合适方法，本文提出了扩展信息过滤器（EIF）和无味信息过滤器（UIF）。扩展信息过滤器实际上是分布式扩展卡尔曼滤波的实现，而无味信息过滤器是分布式无味卡尔曼滤波的实现。通过在位于电网不同部分的处理单元上使用上述过滤算法，可以生成系统状态向量的局部估计，而该局部估计又可以融合到聚合状态估计中。如果使用适当的故障检测和隔离算法，则产生的全局状态估计值可以连续监视电力系统的状况并进行早期故障诊断。

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