Optimal coordination of directional overcurrent relays using NSGA-II

Moravej, Zahra; Adelnia, Farhad; Abbasi, Fazel

doi:10.1016/j.epsr.2014.09.010

Cited by 89 publications

(46 citation statements)

References 18 publications

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“…Evolutionary techniques have been used, such as the genetic algorithm (GA) [11][12][13][14][15], nondominated sorting genetic algorithm-II (NSGA-II) [16], particle swarm optimization (PSO) [17,18], seeker optimization algorithm (SOA) [19], ant colony algorithm [1], differential evolution (DE) algorithm [1], opposition based chaotic differential evolution (OCDE) algorithm [20], adaptive differential evolution (ADE) algorithm [21] and teaching learning-based optimization algorithm [22]. Hybrid techniques have also been used in [23,24], combining metaheuristics with deterministic solvers.…”

Section: Introductionmentioning

confidence: 99%

Melanoma risk prediction of a validated genetic signature compared to the AJCC online predictor

2015

Journal of the American Academy of Dermatology

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a b s t r a c tThis work presents a binary programming model for online coordination of directional overcurrent relay problems in interconnected power distribution and subtransmission systems. The proposed model considers discrete time dial and pickup current settings as in microprocessor-based relays. The coordination problem is solved for every topological change in the network and whose optimized settings are remotely adjusted on each relay through a SCADA system, enhancing the speed and sensitivity of the protection system. A pre-processing step is performed to ensure that the operation time of each relay be below the melting curve of the protected cable. Computational results for the binary programming model solved by CPLEX optimization package are compared to heuristic-based techniques. The simulation results indicates that our approach is suitable for online applications, since an optimal solution can be found in reduced times.

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Section: Introductionmentioning

confidence: 99%

Melanoma risk prediction of a validated genetic signature compared to the AJCC online predictor

2015

Journal of the American Academy of Dermatology

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“…NSGA-II, developed by Deb et al [31], is one of the representative evolutionary algorithms for multiobjective optimization problems to obtain Pareto solution set and has been modified and applied to different fields successfully [32,33]. However, several operators in the algorithm need to be designed elaborately when applied to a specific problem, such as encoding and decoding of the chromosome, crossover operator, and mutation operator.…”

Section: Solution Approachmentioning

confidence: 99%

The Location-Routing Problem with Full Truckloads in Low-Carbon Supply Chain Network Designing

Chen

Qiu

2018

Mathematical Problems in Engineering

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In recent years, low-carbon supply chain network design has been the focus of studies as the development of low-carbon economy. The location-routing problem with full truckloads (LRPFT) is investigated in this paper, which extends the existing studies on the LRP to full truckloads problem within the regional many-to-many raw material supply network. A mathematical model with dual objectives of minimizing total cost and environmental effects simultaneously is developed to determine the number and locations of facilities and optimize the flows among different kinds of nodes and routes of trucks as well. A novel multiobjective hybrid approach named NSGA-II-TS is proposed by combining a known multiobjective algorithm, NSGA-II, and a known heuristics, Tabu Search (TS). A chromosome presentation based on natural number and modified partially mapping crossover operator for the LRPFT are designed. Finally, the computational effectiveness of the hybrid approach is validated by the numerical results and a practical case study is applied to demonstrate the tradeoff between total cost and CO 2 emission in the LRPFT.

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“…Xnew kj,i = X kj,i + Diff_Mean j,i (8) where Xnew kj,i is the updated value of X kj,i . Substituting Eq.…”

Section: Teacher Phasementioning

confidence: 99%

“…Each relay in the power system must be coordinated with the other relays in the power system [1,2]. Several optimization techniques are proposed for optimum coordination of DOCRs [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17]. The optimum settings for TMS and PS are obtained using different algorithms proposed by the researchers.…”

Section: Introductionmentioning

confidence: 99%

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Optimum Coordination of Directional Overcurrent Relays Using Modified Adaptive Teaching Learning Based Optimization Algorithm

Kalage¹,

Ghawghawe²

2016

Intell Ind Syst

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Relay coordination problem is highly constrained optimization problem. Heuristic techniques are often used to solve optimization problem. These techniques have a drawback of converging to a non-optimum solution due to the wide range of design variables. On the other hand, initial solution becomes difficult to find with shorter range of design variables. This paper presents modified adaptive teaching learning based optimization algorithm to overcome this drawback of conventional heuristic techniques. The coordination problem is formulated as a constrained non-linear optimization problem to determine the optimum solution for the time multiplier setting (TMS) and plug setting (PS) of DOCRs. Initial solution for TMS is heuristically obtained with the commonly chosen widest range for TMS values. The upper bound of TMS range then substituted by the maximum TMS value in the first initial solution. The new upper limit is obviously lower than the earlier one. Next phase of optimization is carried out with the new range of TMS for the pre-determined iterations of teacher phase. Consequent to the completion of the teacher phase, new upper bound is obtained from the available solution and optimization is carried out for the pre-determined iterations of learner phase. This process is repeated to get the optimum solution. Fixed range for PS is used to obtain the selectivity. Such a strategy of iteratively updating the upper bound of TMS range shows remarkable improvement over the techniques which employ fixed TMS range. This algorithm is tested on different networks and has been found more effective. Four case studies

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Optimal coordination of directional overcurrent relays using NSGA-II

Cited by 89 publications

References 18 publications

Melanoma risk prediction of a validated genetic signature compared to the AJCC online predictor

Melanoma risk prediction of a validated genetic signature compared to the AJCC online predictor

The Location-Routing Problem with Full Truckloads in Low-Carbon Supply Chain Network Designing

Optimum Coordination of Directional Overcurrent Relays Using Modified Adaptive Teaching Learning Based Optimization Algorithm

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