Ugur Can Yilmaz PhD Proposal Review

Title:
Robust and Computationally Efficient Three-Phase State Estimation for  Large-Scaled Unbalanced Power Systems

Date:
10/27/2023

Time:
4:00:00 PM

Committee Members:
Prof. Ali Abur (Advisor)
Prof. Mahshid Amirabadi
Dr. Bilgehan Donmez

Abstract:
Power system monitoring is crucial for ensuring the stability and reliability of electrical grids, allowing for the timely detection of anomalies and potential faults, thus facilitating proactive maintenance and preventing widespread outages. The growing need and interest in real-time monitoring of large distribution networks motivated by the rapid population of renewable sources, EVs etc. demand an accurate, robust and computationally efficient three-phase state estimation framework which is capable of handling unbalanced operation in asymmetrical structured networks.

The challenge common to distribution system state estimation are twofold: (1) while the phase angle of any of the bus voltages can be chosen as the angular reference in the state estimation formulation of positive sequence networks, the same approach does not readily extend to three-phase distribution network state estimation problem and (2) the scalability of the estimation in terms of computational performance for very large-scaled networks is not trivial without compromising the robustness of the estimator. In this study, a novel state estimation formulation will be presented which facilitates correct solution irrespective of the existence of buses with perfectly balanced voltages.  The new formulation is general. It yields accurate results in any three-phase power system irrespective of its operating conditions (balanced or highly unbalanced), configuration (isolated microgrid, connected to transmission system, etc.) and whether or not it contains any synchronous generators. Additionally, this research introduces innovative methodologies aimed at enhancing the efficacy of the state estimator for application in distribution networks. It is ensured that the computational efficiency of the estimator remains viable while the robustness is guaranteed, even when dealing with highly extensive power networks using a parallel distributed state estimation framework. At the core of the proposed approach are two partitioning algorithms that enable efficient parallelisation of computations both for radial and meshed networks. All the methodologies have been formulated, implemented, and their effectiveness confirmed through preliminary results employing straightforward test scenarios.