An Improved Crow Search Algorithm Applied to the Phase Swapping Problem in Asymmetric Distribution Systems

Abstract

This paper discusses the power loss minimization problem in asymmetric distribution systems (ADS) based on phase swapping. This problem is presented using a mixed-integer nonlinear programming model, which is resolved by applying a master–slave methodology. The master stage consists of an improved version of the crow search algorithm. This stage is based on the generation of candidate solutions using a normal Gaussian probability distribution. The master stage is responsible for providing the connection settings for the system loads using integer coding. The slave stage uses a power flow for ADSs based on the three-phase version of the iterative sweep method, which is used to determine the network power losses for each load connection supplied by the master stage. Numerical results on the 8-, 25-, and 37-node test systems show the efficiency of the proposed approach when compared to the classical version of the crow search algorithm, the Chu and Beasley genetic algorithm, and the vortex search algorithm. All simulations were obtained using MATLAB and validated in the DigSILENT power system analysis software.

Application of the Crow Search Algorithm to the Problem of the Parametric Estimation in Transformers Considering Voltage and Current Measures

“…In this research, the crow search algorithm is selected as the combinatorial optimization methodology to solve the studied problem based on the following facts: (i) The CSA is a metaheuristic optimization method from the family of bio-inspired algorithms that presents a balanced performance between the exploration and exploitation of the solution space, using memories to maintain and present the most promissory solutions of the solution space, as well as generating new solutions that allow to explore nonvisited solution regions; (ii) the effectiveness and robustness of the CSA to solve nonlinear nonconvex optimization problems has recently been demonstrated in similar optimization problems such as parametric estimation in photovoltaic modules [22], parametric estimation in induction motors [23], optimal phase-swapping in electrically unbalanced distribution networks [24], and segmentation of magnetic resonance images [25], among others; (iii) In the current literature, there is no evidence of the application of the CSA to the problem addressed in this research, which is an opportunity of research to which this paper tries to contribute.…”

Application of the Hurricane-Based Optimization Algorithm to the Phase-Balancing Problem in Three-Phase Asymmetric Networks

“…An in-depth study has been carried out to determine the feasibility of this proposed method as an optimal solution to phase balancing in distribution systems, detailing a master-slave optimization, which works as follows: Master phase: The arrangement system to be evaluated with its respective set of connections is determined [3]. Slave phase: Employing the three-phase iterative sweep method, it is a solution to the power flow and, specifically, the power losses in the system under study is determined [6], corresponding to the set of connections described in the master phase. HOA is applied to determine the number of possible connections and their location in space, thus obtaining the best connection set selection for the studied system.…”

“…This performance causes undesired scenarios [5] such as an increase in current in certain phases of the system, the appearance of current flow in the neutral conductor, overvoltages in the less loaded stages, increased power losses, economic penalties for the network operators, low economic efficiency in the electrical distribution system, inadequate operation of protections, and deterioration of the quality indexes in the distribution system [6].…”

Approximated Mixed-Integer Convex Model for Phase Balancing in Three-Phase Electric Networks

“…The optimal phase balancing problem in three-phase unbalanced distribution networks is a classical and widely studied optimization problem in electrical distribution systems [5]. The complete optimization model of the grid involves a general mixed-integer nonlinear programming (MINLP) model due to (i) the presence of binary variables associated with the load connection in the model that can be modeled with a binary matrix with 3 × 3 dimensions [1] and (ii) the intrinsic nonlinearity of the power balance constraints in their three-phase form, i.e., the products among voltages and trigonometric functions [12,13]. However, to address the problem of phase balancing in three-phase unbalanced networks, here, we propose a simplified mixed-integer convex model that would allow for the redistribution of all the loads by considering the ideal voltages for all the buses of the network as well as Kirchhoff's first law in all the nodes of the network.…”