An Improved Human-Inspired Algorithm for Distribution Network Stochastic Reconfiguration Using a Multi-Objective Intelligent Framework and Unscented Transformation

Min Zhu, Saber Arabi Nowdeh () and Aspassia Daskalopulu
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Min Zhu: College of Information Science and Technology, Zhejiang Shuren University, Hangzhou 310015, China
Saber Arabi Nowdeh: Institute of Research Sciences, Power and Energy Group, Johor Bahru 81310, Malaysia
Aspassia Daskalopulu: Department of Electrical and Computer Engineering, University of Thessaly, 38334 Volos, Greece

Mathematics, 2023, vol. 11, issue 17, 1-23

Abstract: In this paper, a stochastic multi-objective intelligent framework (MOIF) is performed for distribution network reconfiguration to minimize power losses, the number of voltage sags, the system’s average RMS fluctuation, the average system interruption frequency (ASIFI), the momentary average interruption frequency (MAIFI), and the system average interruption frequency (SAIFI) considering the network uncertainty. The unscented transformation (UT) approach is applied to model the demand uncertainty due to its being simple to implement and requiring no assumptions to simplify it. A human-inspired intelligent method named improved mountaineering team-based optimization (IMTBO) is used to find the decision variables defined as the network’s optimal configuration. The conventional MTBO is improved using a quasi-opposition-based learning strategy to overcome premature convergence and achieve the optimal solution. The simulation results showed that in single- and double-objective optimization some objectives are weakened compared to their base value, while the results of the MOIF indicate a fair compromise between different objectives, and all objectives are enhanced. The results of the MOIF based on the IMTBO clearly showed that the losses are reduced by 30.94%, the voltage sag numbers and average RMS fluctuation are reduced by 33.68% and 33.65%, and also ASIFI, MAIFI, and SAIFI are improved by 6.80%, 44.61%, and 0.73%, respectively. Also, the superior capability of the MOIF based on the IMTBO is confirmed compared to the conventional MTBO, particle swarm optimization, and the artificial electric field algorithm. Moreover, the results of the stochastic MOIF based on the UT showed the power loss increased by 7.62%, voltage sag and SARFI increased by 5.39% and 5.31%, and ASIFI, MAIFI, and SAIFI weakened by 2.28%, 6.61%, and 1.48%, respectively, compared to the deterministic MOIF model.

Keywords: multi-objective intelligent framework; reconfiguration; voltage sag; reliability; unscented transformation; improved mountaineering team-based optimization algorithm (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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