Optimizing Firefly Algorithm for Directional Overcurrent Relay Coordination: A case study on the Impact of Parameter Settings

This paper investigates the application of the Firefly Algorithm for solving the coordination problem in the IEEE 3-bus network. It analyzes the impact of key parameters, including the number of generations, population size, absorption coefficient (γ), and randomization parameter (α), on the algorithms performance. Through extensive experimentation, the study demonstrates the impact on solution quality, feasibility, computational requirements, and efficiency. Results indicate that increasing the number of generations improves solution quality, but benefits diminish beyond a certain point. Feasibility improves with higher generations, but a balance between solution quality and feasibility becomes apparent at very high generations. Objective function evaluations and computation time increase linearly with generations. Larger population sizes yield better solution quality and feasibility, but a balance is observed at very high population sizes. Objective function evaluations and computation time scale proportionally with population size. The randomization parameter has a modest influence on performance, with no significant changes observed. However, extreme values impact solution quality, feasibility, and computation time. The absorption coefficient significantly affects convergence and solution quality. Lower values expedite convergence but may lead to suboptimal solutions, while higher values enhance exploration at the cost of increased computational effort. This study provides a comprehensive understanding of parameter selection and optimization in the Firefly Algorithm for solving the coordination problem of the IEEE 3-bus network, offering valuable guidance for future research in enhancing performance through parameter refinement and adaptive techniques

Optimal Coordination of Directional Overcurrent Relays Using Hybrid Firefly–Genetic Algorithm

© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/The application of directional overcurrent relays (DOCRs) plays an important role in protecting power systems and ensuring their safe, reliable, and efficient operation. However, coordinating DOCRs involves solving a highly constrained and nonlinear optimization problem. The primary objective of optimization is to minimize the total operating time of DOCRs by determining the optimal values for decision variables such as the time multiplier setting (TMS) and plug setting (PS). This article presents an efficient hybrid optimization algorithm that combines the modified firefly algorithm and genetic algorithm to achieve improved solutions. First, this study modifies the firefly algorithm to obtain a global solution by updating the firefly’s brightness and to prevent the distance between the individual fireflies from being too far. Additionally, the randomized movements are controlled to produce a high convergence rate. Second, the optimization problem is solved using the genetic algorithm. Finally, the solution obtained from the modified firefly algorithm is used as the initial population for the genetic algorithm. The proposed algorithms have been tested on the IEEE 3-bus, 8-bus, 9-bus and 15-bus networks. The results indicate the effectiveness and superiority of the proposed algorithms in minimizing the total operating time of DOCRs compared with other optimization methods presented in the literature.Peer reviewe

Application of Equilibrium Optimizer Algorithm for Solving Linear and non Linear Coordination of Directional Overcurrent Relays

The safety and reliability of an electrical network depend on the performance of the protections utilized. Therefore, the optimal coordination of the pro- tective devices plays an essential role. In this paper, a new algorithm, Equilibrium Optimizer (EO), which is based on the physical equation of the mass balance, is implemented in the problem of the Optimal Coor- dination of Directional Overcurrent Relays (DOCRs). Moreover, the proposed method uses Linear Program- ming (LP), Nonlinear Programming (NLP) and Mixed- Integer Nonlinear Programming (MINLP) in order to optimize the Time Dial Setting (TDS), as well as the Plug Setting (PS), satisfying all possible constraints. Additionally, the performance of EO is evaluated using several benchmarks with different topologies. The results demonstrated the applicability and efficacy of the proposed approach. A comparison with other stud- ies reported in specialized literature is provided to demonstrate the benefits of the proposed approach

An Adaptive Protection Scheme Based on a Modified Heap-Based Optimizer for Distance and Directional Overcurrent Relays Coordination in Distribution Systems

This paper proposes an adaptive protection scheme (APS) based on the original heap-based optimization (HBO) and a modified HBO (MHBO). APS is used to solve protection relays coordination problems that include directional overcurrent relays (DOCRs) as well as the distance relay’s second zone times. The complexity of the coordination problem increases with the impact of distributed generators (DGs) switching (ON/OFF). Topological changes in grid configuration frequently occur in distributing networks, equipped with DGs, causing changes in the values and direction of short circuit currents. This issue becomes a challenge for protection systems to avoid relays miscoordination and save a network’s reliability. In the proposed MHBO, the Original HBO is modified by three points, population are divided into subgroups, then they are unified into one group gradually, those subgroups are exchanging some search agents between themselves, these search agents are called travelling agents, and the last one is about, upgrading an internal equation in the original algorithm. For validating the proposed relays coordination, the IEEE 8-bus test system, and the IEEE 14-bus distribution network are selected as case studies. The obtained simulated results of the proposed algorithm show better performance compared with those obtained by the previous algorithms. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.Funding: This work was funded by the Deanship of Scientific Research at Jouf University under grant No (DSR-2021-02-0306)

Particle Swarm Optimization Solution for Power System Operation Problems

Application of particle swarm optimization (PSO) algorithm on power system operation is studied in this chapter. Relay protection coordination in distribution networks and economic dispatch of generators in the grid are defined as two of power system-related optimization problems where they are solved using PSO. Two case study systems are conducted. The first case study system investigates applicability of PSO on providing proper overcurrent relay settings in the grid, while in the second case study system, the economic dispatch of a 15-unit system is solved where PSO successfully provides the optimum power output of generators with minimum fuel costs to satisfy the load demands and operation constraints. The simulation results in comparison with other methods show the effectiveness of PSO against other algorithms with higher quality of solution and less fuel costs on the same test system

Computational Intelligence Application in Electrical Engineering

The Special Issue "Computational Intelligence Application in Electrical Engineering" deals with the application of computational intelligence techniques in various areas of electrical engineering. The topics of computational intelligence applications in smart power grid optimization, power distribution system protection, and electrical machine design and control optimization are presented in the Special Issue. The co-simulation approach to metaheuristic optimization methods and simulation tools for a power system analysis are also presented. The main computational intelligence techniques, evolutionary optimization, fuzzy inference system, and an artificial neural network are used in the research presented in the Special Issue. The articles published in this issue present the recent trends in computational intelligence applications in the areas of electrical engineering

State-of-the-Art Renewable Energy in Korea

Nowadays, renewable energy plays an important role in our daily lives. This Special Issue addresses the current trend in the use of renewable energy in South Korea. The first aspect is a renewable-based power system, where both main and ancillary supplies are sourced from renewable energies; the second aspect is a distribution network for renewable energy; and the last aspect is a nanogrid network technology. Renewable energy requires many innovations over existing power infrastructure and regulation. These articles show the changing trend in various sectors in Korea