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A Robust Parallel Distributed State Estimation for Large Scale Distribution Systems
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DOI：10.1109/TPWRS.2023.3292552.png)
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Abstract
En 中文
Thegrowing need and interest in real-time monitoring of large distribution networks motivated by the rapid population of renewable sources, EVs and etc. demand a computationally efficient state estimation framework. This article presents an improved computational framework for implementing a robust state estimator using a multi-core processor. The main contribution of the article is the proposed computational framework alongwith two partitioning strategies which enable fast and robust state estimation for large scale radial and/or meshed distribution systems. Formulation of the proposed method and its implementation are described in detail. Performance of the estimator is tested by simulations first using a small 84-bus radial distribution system. Then the method's scalability is demonstrated by simulations on two very large scale distribution networks one configured radially and the othermeshed each containing over 12 500 buses.
Keywords:
Clustering
distributed estimation
network partitioning
parallel processing
state estimation
Journal
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7.2
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