Optimal power flow using Teaching-Learning-Based Optimization technique

Bouchekara, Houssem R. E. H.; Abido, M. A.; Boucherma, M.

doi:10.1016/j.epsr.2014.03.032

Cited by 225 publications

(117 citation statements)

References 28 publications

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“…Remarkably, the average computational time of one iteration for this case is 1.83 s. The optimal adjustments of control variables and optimal values of cost minimization are tabulated in Table 6. For further validation, the results obtained with Jaya algorithm are compared with those of TLBO [28], GSA [34], BBO [34], PSO [35], DE [36], and GWO [36], as shown in Table 7. Jaya algorithm obviously obtained a more superior solution.…”

Section: Ieee 118-bus Networkmentioning

confidence: 99%

“…Teaching-Learning-Based Optimization (TLBO) [28] 129,682.844 NA Gravitational Search Algorithm (GSA) [34] 129,565 76.19 Biogeography-Based Optimisation (BBO) [34] 129,686 78.14 Particle Swarm Optimization(PSO) [35] 130,288.21 NA Differential Evolution (DE) [36] 129,582 79.41 Grey Wolf Optimizer (GWO) [36] 129,720 79.58…”

Section: Algorithm Fuel Cost ($/H) Real Power Losses (Mw)mentioning

confidence: 99%

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Optimal Power Flow Using the Jaya Algorithm

et al. 2016

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This paper presents application of a new effective metaheuristic optimization method namely, the Jaya algorithm to deal with different optimum power flow (OPF) problems. Unlike other population-based optimization methods, no algorithm-particular controlling parameters are required for this algorithm. In this work, three goal functions are considered for the OPF solution: generation cost minimization, real power loss reduction, and voltage stability improvement. In addition, the effect of distributed generation (DG) is incorporated into the OPF problem using a modified formulation. For best allocation of DG unit(s), a sensitivity-based procedure is introduced. Simulations are carried out on the modified IEEE 30-bus and IEEE 118-bus networks to determine the effectiveness of the Jaya algorithm. The single objective optimization cases are performed both with and without DG. For all considered cases, results demonstrate that Jaya algorithm can produce an optimum solution with rapid convergence. Statistical analysis is also carried out to check the reliability of the Jaya algorithm. The optimal solution obtained by the Jaya algorithm is compared with different stochastic algorithms, and demonstrably outperforms them in terms of solution optimality and solution feasibility, proving its effectiveness and potential. Notably, optimal placement of DGs results in even better solutions.

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Section: Ieee 118-bus Networkmentioning

confidence: 99%

Section: Algorithm Fuel Cost ($/H) Real Power Losses (Mw)mentioning

confidence: 99%

Optimal Power Flow Using the Jaya Algorithm

et al. 2016

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“…Conversely, feasibility preserving methods are highly time-consuming. To use a penalty function method, a penalty factor associated with each violated constraint is added to the objective function in order to penalize infeasible solutions [47]. Therefore, the optimum is found when all the constraints are respected and the objective function is minimized.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Reactive Power Dispatch Optimization with Voltage Profile Improvement Using an Efficient Hybrid Algorithm †

et al. 2018

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This paper presents an efficient approach for solving the optimal reactive power dispatch problem. It is a non-linear constrained optimization problem where two distinct objective functions are considered. The proposed approach is based on the hybridization of the particle swarm optimization method and the tabu-search technique. This hybrid approach is used to find control variable settings (i.e., generation bus voltages, transformer taps and shunt capacitor sizes) which minimize transmission active power losses and load bus voltage deviations. To validate the proposed hybrid method, the IEEE 30-bus system is considered for 12 and 19 control variables. The obtained results are compared with those obtained by particle swarm optimization and a tabu-search without hybridization and with other evolutionary algorithms reported in the literature.

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“…Transformers Connected in Parallel (TCP) with tap-changers are used for controlling power flows through voltage regulation in power grids and ensuring that the load voltage remains within an admissible range; therefore, most methods for coordinating TCP with taps are mainly designed to regulate voltage amplitude [6][7][8][9][10][11][12]. In [6,7], tap-changers of TCP are mainly used for reactive power control with the aim of minimizing power losses and therefore the construction features of transformers are neglected.…”

Section: Introductionmentioning

confidence: 99%

“…In [6,7], tap-changers of TCP are mainly used for reactive power control with the aim of minimizing power losses and therefore the construction features of transformers are neglected. In [8,9], TCP with tap-changers are used in the context of optimal power flow. In this case, the authors objective is to find the optimal tap positions with the aim of improving stability and minimizing generation fuel cost, respectively.…”

Section: Introductionmentioning

confidence: 99%

Power Loss Minimization for Transformers Connected in Parallel with Taps Based on Power Chargeability Balance

et al. 2018

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Abstract:In this paper, a model and solution approach for minimizing internal power losses in Transformers Connected in Parallel (TCP) with tap-changers is proposed. The model is based on power chargeability balance and seeks to keep the load voltage within an admissible range. For achieving this, tap positions are adjusted in such a way that all TCP are set in similar/same power chargeability. The main contribution of this paper is the inclusion of several construction features (rated voltage, rated power, voltage ratio, short-circuit impedance and tap steps) in the minimization of power losses in TCP that are not included in previous works. A Genetic Algorithm (GA) is used for solving the proposed model that is a system of nonlinear equations with discrete decision variables. The GA scans different sets for tap positions with the aim of balancing the power supplied by each transformer to the load. For this purpose, a fitness function is used for minimizing two conditions: The first condition consists on the mismatching between power chargeability for each transformer and a desired chargeability; and the second condition is the mismatching between the nominal load voltage and the load voltage obtained by changing the tap positions. The proposed method is generalized for any given number of TCP and was implemented for three TCP, demonstrating that the power losses are minimized and the load voltage remains within an admissible range.

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Optimal power flow using Teaching-Learning-Based Optimization technique

Cited by 225 publications

References 28 publications

Optimal Power Flow Using the Jaya Algorithm

Optimal Power Flow Using the Jaya Algorithm

Reactive Power Dispatch Optimization with Voltage Profile Improvement Using an Efficient Hybrid Algorithm †

Power Loss Minimization for Transformers Connected in Parallel with Taps Based on Power Chargeability Balance

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