Oppositional Jaya Algorithm With Distance-Adaptive Coefficient in Solving Directional Over Current Relays Coordination Problem

Abstract

The model of directional over current relays (DOCRs) coordination is considered as an optimization problem. It is generally formulated as linear programming (LP), non-linear programming (NLP) and mixed integer non-linear programming (MINLP), according to the nature of the design variables. For each kind of formulation, the main goal is to minimize the summation of operating times of primary relays, by setting optimal values for decision variables as time dial setting (TDS) and pickup current setting (IP) or plug setting (PS). In this paper, we proposed an oppositional Jaya (OJaya) algorithm with distance-adaptive coefficient (DAC), to effectively solve the DOCRs coordination problem. Firstly, by oppositional learning (OL), the searching space of Jaya is expanded and the diversity of its population is strengthened; secondly, by DAC, the population's trends of running towards the best position and escaping from the worst position is accelerated. The performance of OJaya is evaluated by 3-bus, 8-bus, 9-bus and 15-bus testing systems, in aspects of convergence rate, objective function value, robustness and computation efficiency. The results indicate the effectiveness and superiority of OJaya in solving DOCRs coordination problems compared with standard Jaya.

The Comparison of Lately Proposed Harris Hawks Optimization and Jaya Optimization in Solving Directional Overcurrent Relays Coordination Problem

“…Biogeography-based optimization (BBO) along with a new hybrid BBO with linear programming (BBO-LP) was used in [14]. Furthermore, Jaya algorithm [15], seeker optimization algorithm (SOA), simulated annealing-based symbiotic organism search (SASOS), and improved group search optimization (IGSO) were used in [16][17][18].…”

A new iterative linear programming approach to find optimal protective relay settings

“…While for the 3 bus system the NILP converged in three iterations, the 8 bus system results converged in six iterations and, for the 30 bus system the NILP convergence is obtained in 14 The results of NILP are compared with a few most recent works in the literature, and the comparison results are tabulated in Table 9. For the purpose of a fair comparison, the IEEE 15 bus system 12,31,32 results have also been included, and the sum of primary relay operating times (OF [sec] = Σ t i ) is considered. It can be observed from Table 9 that the NILP is indeed superior as compared with QCQP, MILP formulations, heuristic, and hybrid heuristic formulations presented in the literature.…”

“…In Reference 27, a transient stability index is included as a constraint in the OcR‐CP model including DG, and the OcR‐CP is solved by using ant lion optimizer (ALO). Several other heuristic optimization based techniques like adaptive modified firefly algorithm (AFA), 28 Gravitational Search Algorithm (GSA), 29,30 oppositional Jaya algorithm (OJaya), 31 modified water cycle algorithm (MWCA) 32 have also been suggested in the literature. Though these works have more or less provided better optimal solutions as compared with respective past literature, each of these methods are non‐traditional and, being random search based techniques, they do not guarantee optimal solution for each and every trail run.…”

“…Heuristic optimization techniques, 17‐31 sometimes do not fetch the global optimum and do not converge to feasible solutions because there is no specific algorithm exist that can achieve the best solution for a particular optimization problem. This disadvantage can be overcome by applying hybrid optimization techniques which combine both heuristic and conventional optimization methods.…”

Development and Implementation of a Novel Optimization Algorithm for Reliable and Economic Grid-Independent Hybrid Power System

“…Opposition-based learning (OBL) [30] has been widely used to improve many optimization techniques such as quasi-oppositional teaching-learning (QOTLBO) [31,32], Quasi-oppositional swine influenza model-based optimization with quarantine (QOSIMBO-Q) [33] and Oppositional Jaya Algorithm [34]. In the OBL, improvements can be achieved by using the candidate solution and its opposite at the same time.…”