Power distribution systems (PDS) in urban areas suffer from different types of problems. One such major problem is accidental or scheduled interruption. In electrical networks, effects of interruptions are usually quantified using a set of reliability indices, namely the System Average Interruption Frequency Index and the System Average Interruption Duration Index. Installation cost (fixed cost) and cost due to temporary and/or permanent faults during interruptions (variable cost) are also major issues to be considered while achieving a cost efficient, fault-tolerant PDS. Formalization of an optimization problem that jointly minimizes the afore-mentioned reliability indices as well as the cost of a PDS by optimal allocation of different protective devices and switches has always been a challenging task. This paper presents a hybrid single as well as joint-objective function optimization technique to minimize different reliability indices (mixed integer minimization problems), as well as the operational cost of a PDS in urban areas. In the proposed technique, two well-known meta-heuristic search techniques, namely genetic algorithms (GA) and ant colony optimization (ACO), have been hybridized after modifying different participating operators. The effectiveness of the proposed algorithm is examined, and each PDS is tested in a different environment of constrained optimization. In addition, the presented simulation results are compared with existing approaches that solve this problem. The simulation results show the superiority of the proposed hybrid GA–ACO model, as compared to other established heuristic approaches.
Power distribution system Hybrid algorithms Constrained optimization Genetic algorithm Ant colony optimization Reliability indices
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We would like to thank all anonymous reviewers for their valuable comments and suggestions, which have enriched our paper significantly. Authors would also express their gratitude toward Prof. Asoke Kumar Bhunia for his valuable suggestions.
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Conflict of interest
The authors declare that they have no conflict of interest.
This article does not contain any studies with human participants or animals performed by any of the authors.
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