Successive-over-relaxation based recursive Bayesian approach for power system configuration identification. Issue 4 (3rd July 2017)

• Record Type: Journal Article
• Authors: Ahmad, Fiaz; Rasool, Akhtar; Ozsoy, Esref Emre; Sabanoviç, Asif; Elitas, Meltem
• Is Part Of: Compel. Volume 36:Issue 4(2017)
• Page Start: 1043
• Page End: 1058
• Publication Date: 2017-07-03
• Subjects: Distributed energy resources -- Distribution system state estimation (DSSE) -- Power system configuration identification -- Recursive Bayesian based topology identification -- State estimation (SE); Electrical engineering -- Data Processing -- Periodicals; Electrical engineering -- Mathematics -- Periodicals; Electrical engineering -- Periodicals; Electronics -- Data Processing -- Periodicals; Electronics -- Mathematics -- Periodicals; 621.3
• DOI: 10.1108/COMPEL-10-2016-0462
• Languages: English
• ISSNs: 0332-1649

Abstract

Abstract : Purpose: The purpose of this paper is to propose successive-over-relaxation (SOR) based recursive Bayesian approach (RBA) for the configuration identification of a Power System. Moreover, to present a comparison between the proposed method and existing RBA approaches regarding convergence speed and robustness. Design/methodology/approach: Swift power network configuration identification is important for adopting the smart grid features like power system automation. In this work, a new SOR- based numerical approach is adopted to increase the convergence speed of the existing RBA algorithm and at the same time maintaining robustness against noise. Existing RBA and SOR-RBA are tested on IEEE 6 bus, IEEE 14 bus networks and 48 bus Danish Medium Voltage distribution network in the MATLAB R2014b environment and a comparative analysis is presented. Findings: The comparison of existing RBA and proposed SOR-RBA is performed, which reveals that the latter has good convergence speed compared to the former RBA algorithms. Moreover, it is robust against bad data and noise. Originality value: Existing RBA techniques have slow convergence and are also prone to measurement noise. Their convergence speed is effected by noisy measurements. In this paper, an attempt has been made to enhance convergence speed of the new identification algorithm while keeping its numerical stability and robustness during noisy measurement conditions. This work is novel and has drastic improvement in the convergence speed and robustness of the former RBA algorithms.