A comparative study of metaheuristic optimization approaches for directional overcurrent relays coordination

Alam, Mahamad Nabab; Das, Biswarup; Pant, Vinay

doi:10.1016/j.epsr.2015.06.018

Cited by 207 publications

(125 citation statements)

References 20 publications

Supporting

Mentioning

122

Contrasting

Unclassified

Order By: Relevance

“…Among these heuristic‐based computing algorithms are genetic algorithm (GA), ant colony algorithm, various variants of differential evolution (DE) algorithm, firefly algorithm, teaching learning optimization algorithm, artificial bees colony, flower pollination algorithm (FPA), symbiotic organism search optimization, and many more, which have been attempted to solve the relay coordination problems under various network topologies. In the study of Alam et al, 5 meta‐heuristic optimization methods are considered to solve the optimal relay coordination with comprehensive comparisons of their performances. In which the meta‐heuristic optimization methods are GA, particle swarm optimization, DE, harmony search optimizer, and seeker algorithm are analyzed.…”

Section: Introductionmentioning

confidence: 99%

Optimized settings of directional overcurrent relays in meshed power networks using stochastic fractal search algorithm

El‐Fergany

Hasanien

2017

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

Summary This paper presents a novel application of the stochastic fractal search algorithm to solve the optimal relay coordination problem of meshed power networks. The optimization problem has 3 design variables, which are the time dial, the pickup current, and the tripping characteristic of each relay and subjects to set of coordination constraints. The objective function is adapted to minimize the total operating time of the primary and backup relays while maintaining the validity of coordination within acceptable limits. Three interconnected power systems with various topologies and fault scenarios are demonstrated. Among these 3 test cases, it is worthy emphasizing that the third power system is highly penetrated with 6 distributed generators, which tangle the optimization problems with numerous relay pair scenarios. The validity and the effectiveness of the proposed method are confirmed using the simulation results, in addition to necessary subsequent comparisons to other competing approaches. The simulated numerical results and the performance measures indicate that the stochastic fractal search algorithm is viable and able to generate competitive optimal settings for overcurrent relays and lie in an acceptable elapsed time.

show abstract

Section: Introductionmentioning

confidence: 99%

Optimized settings of directional overcurrent relays in meshed power networks using stochastic fractal search algorithm

El‐Fergany

Hasanien

2017

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

show abstract

“…The selected DG technology is a synchronous type, operating nominally at 0.9 lagging power factor. The detail information about the system is given in [18,31,33]. The CT ratio for each relay is considered to be 200:1.…”

Section: B Test System 2: Ieee 30-bus Networkmentioning

confidence: 99%

A Novel Approach for Optimum Coordination of Directional Overcurrent Relays Including Far-End Faults in Interconnected Power Systems

Rajput¹,

Pandya²

2016

IJET

View full text Add to dashboard Cite

Abstract- I. INTRODUCTIONThe primary protection in distribution and sub-transmission networks and backup protection in transmission networks are usually provided using DOCRs [1]. For reducing the chances of excessive power outages, only faulted section of the power system is isolated using primary relays as quickly as possible. As long as the primary relay fails to operate, the backup relays have to instigate to clear the fault after the prescribed time interval. This practice is called relay coordination. The proper coordination of primary and backup relays is essential to ensure the reliability of protection scheme, which is achieved by locating the optimal values of PS and TMS. In modern multi-loop and multi-source interconnected power systems, finding the optimal PS and TMS using analytical methods becomes very hard. Alternatively, it can be easily solved by optimization techniques [2].In the last few years, several optimization techniques are employed to solve the relay coordination problem. Among the conventional methods, Linear Programming (LP) technique gained good recognition to solve this problem, including simplex, two-phase simplex and dual simplex methods [3][4][5]. The LP methods involve assumptions in PS, allowing for operating time of each relay as a linear function of TMS. In [6,7], Sequential Quadratic Programming (SQP) has been used in order to optimize both TMS and PS. Afterwards, Artificial Intelligence (AI) techniques are studied more to solve the coordination problem such as GA The HSA is one of the metaheuristic optimization methods which is developed by Z.W. Geem [22]. It has the characteristics of fast convergence speed, easy in concept and simple in implementation with only a few parameters and mathematical requirements [22,23]. Same as other metaheuristic methods, the performance of the HSA experiences a serious problem of sensitive parameters setting. Hence, fine tuning of the parameters are required, which can help the HSA to maintain a balance between diversification and intensification and to explore the population in the evolution process [24]. To improve the performance of HSA, IHSA is presented in

show abstract

“…Furthermore, the experiences are accelerated by factors c 1 and c 2 , and random numbers r 1 and r 2 , generated between [0, 1], whereas the present movement is multiplied by an inertia factor w. More details about the basic conceptualization of PSO can be found in [26,27]. Several popular variants of PSO are classical PSO [28], time-varying acceleration coefficients PSO (T-PSO) [29], and constriction PSO (K-PSO) [30].…”

Section: Review Of Particle Swarm Optimizationmentioning

confidence: 99%

A novel perturbed particle swarm optimization-based support vector machine for fault diagnosis in power distribution systems

Thom¹,

Cho²,

Tuan³

2018

Turk J Elec Eng & Comp Sci

View full text Add to dashboard Cite

Abstract:In this paper, a novel perturbed particle swarm optimization (PPSO) algorithm is investigated to improve the performance of a support vector machine (SVM) for short-circuit fault diagnosis in power distribution systems.In the proposed PPSO algorithm, the velocity of each particle is perturbed whenever the particles strike into a local optimum, in order to achieve a higher quality solution to optimization problems. Furthermore, the concept of proposed perturbation is applied to three variants of PSO, and improved corresponding algorithms are named perturbed C-PSO (PC-PSO), perturbed T-PSO (PT-PSO), and perturbed K-PSO (PK-PSO). For the purpose of fault diagnosis, the timedomain reflectometry (TDR) method with pseudorandom binary sequence (PRBS) excitation is considered to generate the necessary fault simulation data set. The proposed approaches are tested on a typical two-lateral radial distribution network.

show abstract

A comparative study of metaheuristic optimization approaches for directional overcurrent relays coordination

Cited by 207 publications

References 20 publications

Optimized settings of directional overcurrent relays in meshed power networks using stochastic fractal search algorithm

Optimized settings of directional overcurrent relays in meshed power networks using stochastic fractal search algorithm

A Novel Approach for Optimum Coordination of Directional Overcurrent Relays Including Far-End Faults in Interconnected Power Systems

A novel perturbed particle swarm optimization-based support vector machine for fault diagnosis in power distribution systems

Contact Info

Product

Resources

About