Box-Muller harmony search algorithm for optimal coordination of directional overcurrent relays in power system

Abstract

Directional overcurrent relays are used to protect interconnected power systems and looped distribution systems. The problem is modeled as a constrained nonlinear optimization problem in which the decision variables are the devices that control the act of isolation of faulty lines from the system without disturbing the healthy lines. Time dial setting (TDS) and plug setting (PS) are considered as optimal parameters. IEEE 3-bus model and IEEE 4-bus model are two models of this problem. This paper presents a new efficient and reliable approach based on a constrained harmony search algorithm (HSA) to solve the problems. The authors' proposed technique is called Box-Muller harmony search (BMHS) algorithm. BMHS has been tested by applying it to two models. The simulation result of this technique is compared with those reported in the literature. The outcome is very encouraging and proves that the new technique outperforms them in terms of reaching a more optimal solution and speed.

Optimization Techniques for Directional Overcurrent Relay Coordination: A Comprehensive Review

“…Objective function/ Description Aim [31], [60], [67], [68], [71], [75], [84], [90], [115], [121], [137],…”

“…Additionally, it includes the minimization of the total operating time of all primary relays for both near-end and far-end faults. This objective function takes into account the fact that faults can occur at different locations from the relay and aims involved in protecting the system [31], [60], [67], [68], [71], [75], [84], [90], [115], [121], [137], [180]- [184]. It also includes the minimization of the summation of the operating time of all primary and backup relays.…”

“…To ensure that no relay go out of bounds and defined constraints, a constraint handling technique is added to the objective function of the algorithm [110]. One common approach is the use of penalty methods [2], [3], [6], [14], [15], [18]- [20], [22], [23], [25], [32], [34], [41], [44]- [48], [53], [54], [57], [62], [63], [65], [66], [69], [70], [72], [75], [76], [78], [81], [83], [84], [86], [88], [91]- [94], [98], [100], [102], [103], [106], [110]- [112], [118], [122], [125], [128]- [132],…”

“…In [75], a new effective and reliable approach that employs a constrained HSA and integrates the Box-Muller harmony search (BMHS) algorithm was suggested to address the coordination problem. This technique was proposed to obtain optimal results.…”

“…Conventional and deterministic optimization, including linear programming (LP) such as simplex, dual simplex, two-phase simplex and Big-M methods [36], [37], [40], [138]- [140], non-linear programming (NLP) such as sequential quadratic programming (SQP) [30], interior point-based method [22], Random Search Technique (RST) [31], and gradient searchbased method [32], Mixed Integer Linear Programming (MILP) [21], Analytical method (AA) [33], [34], and Local Fit method [35]. Metaheuristic methods, including Genetic Algorithm (GA) [44]- [53], [119], Particle Swarm Optimization (PSO) [54]- [60], Rao-1 optimization algorithm [61], Honey bee algorithm [62], Artificial Bee Colony (ABC) [63]- [65], Differential Evolution (DE) [66]- [72], Harmony Search Algorithm (HSA) [73]- [75], Seeker Optimization Algorithm (SOA) [23], Biogeography-based optimization (BBO) [17], Firefly Algorithm (FA) [76]- [83], Teaching Learning Based Optimization (TLBO) [84], [85], Cuckoo Search Algorithm (CS) [86]- [88], Ant Colony Optimization (ACO) [89], Ant Lion Optimization (ALO) [90], Backtracking Search Algorithm (BSA) [91], [92], Modified This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and content may change prior to final publication.…”