Optimal Coordination of Overcurrent Relay Using Crow Search Algorithm

Optimization Techniques for Directional Overcurrent Relay Coordination: A Comprehensive Review

“…The coordination problem of DOCRs is stated as an optimization problem that is to be minimized [110] I provide different ways for optimizing the coordination of DOCRs, depending on the specific criteria that need to be considered. One of the objective functions mentioned in Table I is presented in [3], [6], [19], [29], [33], [46], [48], [52], [69], [73], [74], [77], [78], [81], [88], [91], [93], [94], [96], [101], [102], [105]- [108], [110], [114], [117], [120], [123], [124], [128], [129], [138], [140], [142], [155]- [170], which aims to minimize the overall operating time of all relays in the system, while taking into account the CTI requirement between the backup and primary relays. However, this objective function only minimizes the operating time of the primary relay, resulting in a longer discrimination time between the primary and backup relays.…”

“…To minimize the summation of all relays operating time considering the contribution of the required relay TM and CSM settings, as well as the mismatch of the grading margins [3], [6], [19], [29], [33], [46], [48], [52], [69], [73], [74], [77], [78], [81], [88], [91], [93], [94], [96], [101], [102], [105]- [108], [110], [114], [117], [120], [123], [124], [128], [129], [138], [140], [142], [155]- [170] min 𝑍 = 𝛼 𝑖 ሺ𝑇𝑀𝑆ሻ 𝑖 𝑚 𝑖=1 = 𝑡 𝑜𝑝 𝑚 𝑖=1 𝛼 = 𝜆 ሺ𝑃𝑆𝑀ሻ 𝛾 − 1 Where TMS is the time multiplier setting, 𝑡 𝑜𝑝 is the primary relay operational time, m is representing the number of relays in the power system and 𝜆 and γ are tripping curve constants defined by ANSI/IEEE and IEC standards.…”

“…Recently, various metaheuristic optimization algorithms have been utilized to obtain solutions to electrical engineering problems, such as the optimum settings of DOCRs coordination [81], [126], [150]. Metaheuristic methods have attracted a lot of interest for their ability to optimize DOCR settings [23], [62], [84], [96], [124], [133], [172]. They populate several solutions to start the optimization process rather of starting with just one, as in conventional ones.…”

“…The algorithm is based on the intelligent foraging behavior of crows, where crows hide and retrieve food in a group while communicating with each other [233]. Authors in [96] successfully applied the CSA algorithm for the optimal relay coordination problem of DOCRs for different power system networks and the results were compared with FA [76], CFA [81], GA [46], CGA [48], SM [232], and RTO [94] techniques and found that CSA outperformed these techniques in terms of solution quality, achieving minimized objective function values. The faster convergence rate of CSA also indicates its potential to reduce the computational time required for optimization.…”

“…Electromagnetic Field Optimization (MEFO) [93], Rooted Tree Optimisation (RTO) [94], [95], Crow Search Algorithm (CSA) [96], Flower Pollination Algorithm (FPA) [18], [19], Gravitational Search Algorithm (GSA) [97]- [99], Water Cycle Algorithm (WCA) [100]- [105], Grey Wolf Optimizer (GWO) [14], [106]- [109], Harris Hawk Optimization (HHO) [27], [110], Group Search Optimization (GSO) [111], Imperialist Competitive Algorithm (ICA) [112], [113], Political Optimization (PO) [114], Symbiotic Organism Search Technique (SOS) [115], Whale Optimization Algorithm (WOA) [116], Sine Cosine Algorithm (SCA) [26], Discrete and Continuous Hyper-Sphere Search (DC-HSS) [2], Bonobo Algorithm (BO) [117], JAYA [6], [15], [118], and Improved Invasive Weed Optimization algorithm (IIWO) [130]. Hybrid methods, including GA-LP [131], GA-NLP [141], GA-EHA [132], PSO-LP [133], NM-PSO [135]- [137], PSO-TVAC [120], PSO-DE [121], PSO-GA [122], PSO-LSA [123], [142], PSO-SA [124], ABC-LP [125], BBO-LP [17], FA-LP…”