Dynamic and multi-objective reconfiguration of distribution network using a novel hybrid algorithm with parallel processing capability

The configuration of the networks leads to significant effect on the power quality factors like power loss, voltage profile, reliability, and networks resiliency. Due to intermittent nature of system parameters, the network configuration should be adjusted adaptively and dynamically; this process needs a fast and precise reconfiguration algorithm. The main challenge of existing algorithms is the efficient tradeoff between speed and accuracy of reconfiguration. Most of methods use wide-area searching algorithms. Hence, their response to dynamic deviations is low in the term of computational speed. In this regard, the reconfiguration methods that have been presented for dynamic purposes, have weak optimization structure and low accuracy. In this paper, a novel hybrid algorithm is proposed for dynamic and multi-objective reconfiguration of the distribution networks by using the parallel processing method and adaptive population approach. The combination of the exchange market algorithm (EMA) and wild goats algorithm (WGA) is implemented, in parallel pools, for enhancing the accuracy and speed of the reconfiguration simultaneously. The adaptive updating of the population size of parallel algorithms increases the convergence speed of the hybrid method and also offers a fast responding approach for dynamic reconfiguration of network. The objective functions intended for reconfiguration are active power loss and reliability indexes. The conducted research proposes an applicable architecture called as improved loop matrix for eliminating defects of the conventional loop matrix method which leads to ensuring the radial structure of network. The proposed method is tested on IEEE 15, 33, 69 and 85-bus standard test systems and the results are compared with literature and base mode of network. The analysis of the comparisons illuminates the superiority of the proposed method in terms of convergence speed, accuracy and processing time. (C) 2020 Elsevier B.V. All rights reserved.