Improved Firefly Algorithm for the Optimal Coordination of Directional Overcurrent Relays

Abstract

In an electrical power network linear and non-linear models are used for directional overcurrent relay (DOCR) coordination issue by applying different heuristic techniques. Nature inspired algorithms (NIA) have found great interest in power system optimization issues. This paper proposes the recently developed meta-heuristic technique known as Firefly Algorithm (FA) that mimics the flashing behavior of fireflies. The implementation of the proposed algorithm has been utilized to solve the coordination of DOCR problems. The main aim of this paper is to find out the optimum values of the Time Dial Setting (TDS) to minimize the relay operating time. The modifications to original FA has been implemented in this paper to solve the DOCR coordination issues. Self-adaptive weight and experience-based learning strategy are added in the original FA, named as improved firefly algorithm (IFA). In IFA, a self-adaptive weight is presented to change the propensity of moving the best solution and ignoring the worst solution. In addition, an experiencebased learning system is created and utilized arbitrarily to keep up the populace-assorted variety and improve the exploration capacity. The IFA has been tested on IEEE 6 and 30-bus systems and tested on IEEE 9bus system for numerical DOCRs and the results had been compared with results of Whale optimization algorithm to validate the performance of IFA in case of numerical DOCR. The obtained results show that the IFA provides efficient and promising results compared to other meta-heuristic techniques mentioned in the literature. The IFA has been successfully implemented on MATLAB software programming. INDEX TERMS Improved firefly algorithm (IFA), directional overcurrent relay coordination (DOCR), time dial setting (TDS), power system protection.

Optimal Coordination of Standard and Non-Standard Direction Overcurrent Relays Using an Improved Moth-Flame Optimization

“…the performance of proposed IMFO is evaluated using three different networks (8-bus, 9-bus, and 15-bus). In each test network, the proposed IMFO is compared with other well-known optimization algorithms (EFO [19], DE [23], MEFO [19], PSO [23], MWCA [1], WCA [1], HS [12], SQA [24], GSA [24], GA-NLP [25], GSA-SQP [24], BBO-LP, GA [23], CSA [26], SFSA [17], SA [5], BSA [27], EBSA [28], GSO [29], ER-WCA [30], WOA [31], HWO [31], FA [32], IFA [32], DJAYA [34], and OJAYA [34]) to show its superiority in finding the optimal solution for the coordination problem of DOCRs. Both of MFO and IMFO are applied on the following two cases: -Case #1, the coordination problem of DOCRs with the standard DOCR characteristics.…”

An Optimized Adaptive Protection Scheme for Numerical and Directional Overcurrent Relay Coordination Using Harris Hawk Optimization

“…A different version of the differential algorithm was reported in [33] to solve the DOCR coordination problem to point out the superiority of modified differential evolution algorithms. Many other Nature-inspired algorithms like the grey wolf optimizer (GWO), teaching learning-based optimization (TLBO), biography-based optimization (BBO), back-tracking algorithm, the improved firefly (IFA) metaheuristic and modified electromagnetic field optimization (MEFO) were used for DOCR coordination in [34][35][36][37][38][39][40]. A modified teaching-based optimization algorithm was implemented in [41].…”

Kesinti Koşullarında Bulanık Mantık Tabanlı Röle Koordinasyon Senaryolarını Kullanarak Minimum Kesintili Elektrik Halka Şebekelerinin Beslenmesi

“…A modified adaptive teaching learning based optimization algorithm is developed in [11]. Genetic algorithm [12,17], particle swarm optimization [18], seeker optimization algorithm [19], firefly algorithm [20] and differential evolution [21], methods have been proposed to coordinate relay paramerters. As it is well known, the metaheuristic methods are iteration-based and take long time to converge.…”