Version 1
: Received: 28 April 2023 / Approved: 29 April 2023 / Online: 29 April 2023 (04:11:19 CEST)
How to cite:
Sulaimon, W.; Habiballah, I. Recent Advances in Distribution System State Estimation Algorithms: from Model-based to Data-driven Approach. Preprints2023, 2023041203. https://doi.org/10.20944/preprints202304.1203.v1
Sulaimon, W.; Habiballah, I. Recent Advances in Distribution System State Estimation Algorithms: from Model-based to Data-driven Approach. Preprints 2023, 2023041203. https://doi.org/10.20944/preprints202304.1203.v1
Sulaimon, W.; Habiballah, I. Recent Advances in Distribution System State Estimation Algorithms: from Model-based to Data-driven Approach. Preprints2023, 2023041203. https://doi.org/10.20944/preprints202304.1203.v1
APA Style
Sulaimon, W., & Habiballah, I. (2023). Recent Advances in Distribution System State Estimation Algorithms: from Model-based to Data-driven Approach. Preprints. https://doi.org/10.20944/preprints202304.1203.v1
Chicago/Turabian Style
Sulaimon, W. and Ibrahim Habiballah. 2023 "Recent Advances in Distribution System State Estimation Algorithms: from Model-based to Data-driven Approach" Preprints. https://doi.org/10.20944/preprints202304.1203.v1
Abstract
This article presents a review of the distribution system state estimation (DSSE) algorithms from the model-based approach to the recent data-driven methodologies. The insufficient measurements which result in low observability have motivated the need to shift from the conventional algorithm to data-driven approaches that can successfully estimate states despite the DSSE challenges. This article discusses the nonlinearity in the DSSE problem formulation and how different model-based methods have been proposed to mitigate the problems of robustness, ill-conditioning, and complexity. Moreover, approximate DSSE to obviate nonlinearity were discussed: complex linearization, small angle approximation, convexification and compressed sensing. Furthermore, probabilistic DSSE methods were also discussed in the need to quantify the uncertainty associated with the state estimation results. Also, data-driven methods applicable to DSSE, pseudo measurement generations, and topology identification were also discussed using machine and deep learning methods. Lastly, a recent approach that employs a hybrid of model-based and data-driven methods using matrix and tensor completion is surfacing because they work in a low observable condition of the network and can estimate the states satisfactorily. With this review, researchers can look further into developing and improving on the model-based data-driven methods less susceptible to the barriers in conventional DSSE algorithms.
Keywords
distribution system state estimation; topology identification; pseudo-measurement; complex linearization; matrix completion; tensor completion.
Subject
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.