A solution to the optimal power flow using genetic algorithm

This paper proposes an effective novel cuckoo search algorithm (ENCSA) in order to enhance the operation capacity of hydrothermal power systems, considering the constraints in the transmission network, and especially to overcome optimal power flow (OPF) problems. This proposed algorithm is developed on the basis of the conventional cuckoo search algorithm (CSA) by two modified techniques: the first is the self-adaptive technique for generating the second new solutions via discovery of alien eggs, and the second is the high-quality solutions based on a selection technique to keep the best solutions among all new and old solutions. These techniques are able to expand the search zone to overcome the local optimum trap and are able to improve the optimal solution quality and convergence speed as well. Therefore, the proposed method has significant impacts on the searching performances. The efficacy of the proposed method is investigated and verified using IEEE 30 and 118 buses systems via numerical simulation. The obtained results are compared with the conventional cuckoo search algorithm (CCSA) and the modified cuckoo search algorithm (MCSA). As a result, the proposed method can overcome the OPF of hydrothermal power systems better than the conventional ones in terms of the optimal solution quality, convergence speed, and high success rate.

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“…(25) and (26). -Run optimal power flow for the final M th optimal subintervals to obtain dependent variables.…”

Section: Start Select Control Parametersmentioning

confidence: 99%

A Novel Algorithm for Optimal Operation of Hydrothermal Power Systems under Considering the Constraints in Transmission Networks

et al. 2018

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“…The power flow calculation is based on the Matpower 6.0 toolbox in MATLAB R2014a. The performance of BFRL for RBED has been evaluated on IEEE RTS-79 system [43] compared with that of other algorithms, e.g., GA [23], QGA [24], ABC [25], PSO [26], BFO [27,28] and Q-learning [40]. For each algorithm, there are both feasible and infeasible solutions to the proposed RBED problem.…”

Section: Case Studiesmentioning

confidence: 99%

“…So far, an enormous variety of artificial intelligence (AI) algorithms, including genetic algorithm (GA) [23], quantum genetic algorithm (QGA) [24], artificial bee colony (ABC) [25], particle swarm optimization (PSO) [26] and bacteria foraging optimization (BFO) [27,28] have been successfully applied for an optimal power system operation due to their elegant merits of global convergence, model free feature, and applicability to discrete nonlinear problems. In particular, an optimization task can be tackled by variables, objective functions and the number of unsatisfied constraints.…”

Section: Introductionmentioning

confidence: 99%

Bacteria Foraging Reinforcement Learning for Risk-Based Economic Dispatch via Knowledge Transfer

et al. 2017

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This paper proposes a novel bacteria foraging reinforcement learning with knowledge transfer method for risk-based economic dispatch, in which the economic dispatch is integrated with risk assessment theory to represent the uncertainties of active power demand and contingencies during power system operations. Moreover, a multi-agent collaboration is employed to accelerate the convergence of knowledge matrix, which is decomposed into several lower dimension sub-matrices via a knowledge extension, thus the curse of dimension can be effectively avoided. Besides, the convergence rate of bacteria foraging reinforcement learning is increased dramatically through a knowledge transfer after obtaining the optimal knowledge matrices of source tasks in pre-learning. The performance of bacteria foraging reinforcement learning has been thoroughly evaluated on IEEE RTS-79 system. Simulation results demonstrate that it can outperform conventional artificial intelligence algorithms in terms of global convergence and convergence rate.

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“…Some examples algorithm metaheuristic widely used are: Genetic Algorithm (GA), Ant Colony Optimization (ACO), Evolutionary Programming (EP), Particle Swarm Optimization (PSO), Differential Evolution (DE), Tabu Search (TS), Biogeography based Optimization (BBO), Simulated Annealing (SA), etc. [6,7,8,9,10,11,12,13]. Do a comparison between the genetic algorithm with ant colony optimization algorithm to solve a scheduling problem subjects, genetic algorithm is an evolutionary methods that solve problems using a random way.…”

Section: Introductionmentioning

confidence: 99%

Comparison Performance of Genetic Algorithm and Ant Colony Optimization in Course Scheduling Optimizing

Ashari

Muslim

Alamsyah

2016

SJI

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Scheduling problems at the university is a complex type of scheduling problems. The scheduling process should be carried out at every turn of the semester's. The core of the problem of scheduling courses at the university is that the number of components that need to be considered in making the schedule, some of the components was made up of students, lecturers, time and a room with due regard to the limits and certain conditions so that no collision in the schedule such as mashed room, mashed lecturer and others. To resolve a scheduling problem most appropriate technique used is the technique of optimization. Optimization techniques can give the best results desired. Metaheuristic algorithm is an algorithm that has a lot of ways to solve the problems to the very limit the optimal solution. In this paper, we use a genetic algorithm and ant colony optimization algorithm is an algorithm metaheuristic to solve the problem of course scheduling. The two algorithm will be tested and compared to get performance is the best. The algorithm was tested using data schedule courses of the university in Semarang. From the experimental results we conclude that the genetic algorithm has better performance than the ant colony optimization algorithm in solving the case of course scheduling.

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A solution to the optimal power flow using genetic algorithm

Cited by 170 publications

References 2 publications

A Novel Algorithm for Optimal Operation of Hydrothermal Power Systems under Considering the Constraints in Transmission Networks

A Novel Algorithm for Optimal Operation of Hydrothermal Power Systems under Considering the Constraints in Transmission Networks

Bacteria Foraging Reinforcement Learning for Risk-Based Economic Dispatch via Knowledge Transfer

Comparison Performance of Genetic Algorithm and Ant Colony Optimization in Course Scheduling Optimizing

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