Optimum Network Reconfiguration using Grey Wolf Optimizer

Abstract

Distribution system Reconfiguration is the process of changing the topology of the distribution network by opening and closing switches to satisfy a specific objective. It is a complex, combinatorial optimization problem involving a nonlinear objective function and constraints. Grey Wolf Optimizer (GWO) is a recently developed metaheuristic search algorithm inspired by the leadership hierarchy and hunting strategy of grey wolves in nature. The objective of this paper is to determine an optimal network reconfiguration that presents the minimum power losses, considering network constraints, and using GWO algorithm. The proposed algorithm was tested using some standard networks (33 bus, 69 bus, 84 bus and 118 bus), and the obtained results reveal the efficiency and effectiveness of the proposed approach.

Grey wolf optimizer based fuzzy-PI active queue management design for network congestion avoidance

“…α denotes the optimal solution while β represents the second-best solution and δ the third. [24] Other solutions are considered as ω (less valuable). The procedure in the GWO approach is completed in four stages, which include hunting, encompassing the prey, striking and searching the prey (exploration and exploitation stages) [20].…”

“…Many intelligent optimization algorithms are available for optimal tuning of the fuzzy controller, for example: Genetic Algorithms (GA) [17], and Particle Swarm Optimizer (PSO) [18]. The Grey Wolf Optimizer (GWO) [19] algorithm was created based on the observation of hunting hunting habits and social hierarchy of Grey wolfs [20]. Based on the inspiration from the social hierarchy of wolfs, the search population in the GWO is grouped into four, which are tagged alpha, delta, omega, and beta depending on the wolves' physical attributes.…”

Optimizing location and size of capacitors for power loss reduction in radial distribution networks

“…A dolphin algorithm was suggested in [14] for 16 and 33-buses systems but there has been comparison with previous methods. Distributed generators [15] as well as reconfiguration [16][17][18][19] are two solutions for reducing power loss of distribution. Both capacitor placement and reconfiguration were combined to reduce total loss [20].…”

Determining optimal location and size of capacitors in radial distribution networks using moth swarm algorithm

“…Dissimilar to the previous methods, teaching learning based optimization (TLBO) [13], hybrid method of chaotic search, opposition-based learning, DE and quantum mechanics (HCODEQ) [14], particle swarm optimization approaches (PSOs) [15] and flower pollination algorithm (FPA) [16] have solved such OCLSD problem by considering locations and size of capacitor as control variables of each solution. On the other hand, voltage enhancement can be reached by using wind turbines and photovoltaic systems [17,18], network reconfiguration [19][20][21], and distributed generators [22]. In this paper, MSA is applied to OCLSD problem.…”