Successive-over-relaxation based recursive Bayesian approach for power system configuration identification

Ahmad, Fiaz and Rasool, Akhtar and Özsoy, Eşref Emre and Şabanoviç, Asif and Elitaş, Meltem (2017) Successive-over-relaxation based recursive Bayesian approach for power system configuration identification. COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering, 36 (4). ISSN 0332-1649

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Official URL: http://dx.doi.org/10.1108/COMPEL-10-2016-0462


Purpose To propose successive-over-relaxation (SOR) based recursive Bayesian approach (RBA) for the configuration identification of a power system. Moreover, to present 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 (MV) 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 later 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.

Item Type:Article
ID Code:32246
Deposited By:Meltem Elitaş
Deposited On:26 May 2017 15:06
Last Modified:26 May 2017 15:06

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