Ahmad, SD, Azizi, S orcid.org/0000-0002-9274-1177, Mohammad, A et al. (1 more author) (2020) Linear LAV-based State Estimation Integrating Hybrid SCADA/PMU Measurements. IET Generation, Transmission and Distribution, 14 (8). pp. 1583-1590. ISSN 1751-8687
Abstract
The accuracy of power system state estimation (PSSE), its robustness against bad data and the speed of its algorithm are crucial to economic and secure system operation. On the other hand, observability and redundancy considerations mandate PSSE to take advantage of traditional supervisory control and data acquisition (SCADA) measurements along with available phasor measurement unit (PMU) measurements. This set of hybrid PMU/SCADA inputs has traditionally made the problem formulation non-linear, and hence time-consuming to solve due to the iterative process of solution. This study addresses the foregoing challenges by proposing a novel linear least-absolute-value (LAV) estimation, without the need for an initial guess of the system state. The linearity of the proposed PSSE formulation is guaranteed regardless of whether PMU-only, SCADA-only or hybrid SCADA/PMU measurements are utilised. This facilitates the fast and non-iterative solution of the LAV estimation of system state based on linear programming. The LAV estimator outperforms the weighted-least-squares estimator in dealing with erroneous measurements, by automatically rejecting bad data of any size. An extensive number of simulation studies carried out on test systems of different sizes confirm the superiorities of the proposed method in comparison with other existing PSSE methods.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © The Institution of Engineering and Technology 2020. This is an author produced version of a journal article accepted for publication in IET Generation, Transmission & Distribution. Uploaded in accordance with the publisher's self-archiving policy. |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Institute of Communication & Power Networks (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 23 Jan 2020 14:00 |
Last Modified: | 10 May 2020 11:37 |
Status: | Published |
Publisher: | Institution of Engineering and Technology |
Identification Number: | 10.1049/iet-gtd.2019.1850 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:155903 |