Data-Driven, Multi-Region Distributed State Estimation for Smart Grids
Data-Driven, Multi-Region Distributed State Estimation for Smart Grids
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DOI：
10.1109/isgteurope52324.2021.9639984
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发表时间：
2021-10
期刊：
影响因子：
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通讯作者：
Md. Jakir Hossain;M. Rahnamay-Naeini
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文献类型：
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作者：
Md. Jakir Hossain;M. Rahnamay-Naeini
智能电网的实时广域监控要求对电力系统数据进行低延迟的数据处理。为了实现低延迟需求并避免将大量时间敏感数据传输到中央处理单元所带来的巨大开销，分布式和本地处理数据是一种很有前途的方法，可以改进系统监控功能。在电力系统中，数据驱动的状态估计是得益于分布式数据处理和增强系统实时监控功能的一个例子。本文考虑了基于地理距离和电力系统各组成部分之间状态相关性的多区域分布式状态估计。研究了贝叶斯多元线性回归（BMLR）与自回归AR(p)相结合的多区域电力系统分布式状态估计方法。使用ieee118测试用例，在正常情况和部分不可观测场景下，评估了分布式数据驱动状态估计方法的性能和区域的作用。
Real-time wide-area monitoring of smart grids demands a low latency data processing of power system data. To enable the low latency requirements and to avoid the large overhead of communicating a large volume of time-sensitive data to central processing units, distributed and local processing of data is a promising approach that can improve system monitoring functions. Data-driven state estimation in power systems is an example of functions that can benefit from distributed processing of data and enhance the real-time monitoring of the system. In this paper, distributed state estimation is considered over multi-region, identified based on geographical distance and correlations among the state of the power system's components. Bayesian Multivariate Linear Regression (BMLR) combined with Auto-Regressive AR(p) process for distributed state estimation is considered over the multi-region power system. The performance of the distributed data-driven state estimation method and the role of regions are evaluated using the IEEE 118 test case under normal conditions as well as partially unobservable scenarios.