Optimal Power Flow Using Tabu Search Algorithm

Abido, M. A.

doi:10.1080/15325000252888425

Cited by 370 publications

(155 citation statements)

References 24 publications

(17 reference statements)

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“…The simulation was done by taking a quadratic cost curve in case 1, a piecewise quadratic cost curve in case 2, and quadratic cost curve with valve point loading in case 3. The result of the PVHS algorithm is compared with NLP [8], EP [4], TS [5], PSO [9], IEP [6] and MDE [7]. The algorithm is coded on Intel Pentium IV 2.3 GHz processor and 2 GB RAM memory using Matlab 7.4 [12] programming language.…”

Section: Resultsmentioning

confidence: 99%

“…It also has higher sensitivity to initial solution, so it may trap into local optima. The difficulties in implementing OPF can be overcome by modern stochastic algorithms such as evolutionary programming (EP) [4], tabu search (TS) [5], improved evolutionary programming (IEP) [6], modified differential evolution (MDE) [7], particle swarm optimization (PSO) [9], genetic algorithm (GA) [10] and simulated annealing (SA) [11].…”

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confidence: 99%

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Population Variance Harmony Search Algorithm to Solve Optimal Power Flow with Non-Smooth Cost Function

Panigrahi

Pandi

Das

et al. 2010

Recent Advances in Harmony Search Algorithm

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Abstract. This chapter presents a novel Harmony Search (HS) algorithm used to solve security constrained optimal power flow (OPF) with various generator fuel cost characteristics. HS is a recently developed derivative-free, meta-heuristic optimization algorithm, which draws inspiration from the musical process of searching for a perfect state of harmony. This chapter analyses the evolution of the populationvariance over successive generations in HS and thereby draws some important attention regarding the explorative power of HS. This novel methodology of modified population variance harmony search algorithm (PVHS) easily takes care of solving optimal power flow problem even with non-smooth and piecewise cost functions. This PVHS algorithm was tested on the IEEE30 bus system with three different types of cost characteristics and compared with other reported results.

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Section: Resultsmentioning

confidence: 99%

mentioning

confidence: 99%

Population Variance Harmony Search Algorithm to Solve Optimal Power Flow with Non-Smooth Cost Function

Panigrahi

Pandi

Das

et al. 2010

Recent Advances in Harmony Search Algorithm

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“…It is clear that DGWO has stable and rapid convergence characteristic. [14] 800.5099 NA NA SOS [24] 801.5733 801.7251 801.8821 ABC [17] 800.6600 800.8715 801.8674 TS [22] 802.290 NA NA MDE [23] 802.376 802.382 802.404 IEP [15] 802.465 802.521 802.581 TS [15] 802.502 802.632 802.746 EP [16] 802.62 803.51 805.61 TS/SA [15] 802.788 803.032 803.291 EP [15] 802.907 803.232 803.474 ITS [15] 804.556 805.812 806.856 GA [9] 805.937 NA NA…”

Section: Case1: Opf Solution Without Considering the Valve Point Effectsmentioning

confidence: 99%

“…In addition, these methods may fall into local minima, hence new optimization algorithms have been proposed to avoid the shortcomings of these methods. From these methods; GA [8,9], MFO [10], DE [11,12], PSO [13], MSA [14], EP [15,16], ABC [17], GSA [18], BBO [19], SFLA [20], forced initialized differential evolution algorithm [21], TS [22], MDE [23], SOS [24], BSA [25] and TLBO [26], decentralized decision-making algorithm [27]. The thermal generation units have multiple valves to control the output generated power.…”

Section: Introductionmentioning

confidence: 99%

Solving Non-Smooth Optimal Power Flow Problems Using a Developed Grey Wolf Optimizer

et al. 2018

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Abstract:The optimal power flow (OPF) problem is a non-linear and non-smooth optimization problem. OPF problem is a complicated optimization problem, especially when considering the system constraints. This paper proposes a new enhanced version for the grey wolf optimization technique called Developed Grey Wolf Optimizer (DGWO) to solve the optimal power flow (OPF) problem by an efficient way. Although the GWO is an efficient technique, it may be prone to stagnate at local optima for some cases due to the insufficient diversity of wolves, hence the DGWO algorithm is proposed for improving the search capabilities of this optimizer. The DGWO is based on enhancing the exploration process by applying a random mutation to increase the diversity of population, while an exploitation process is enhanced by updating the position of populations in spiral path around the best solution. An adaptive operator is employed in DGWO to find a balance between the exploration and exploitation phases during the iterative process. The considered objective functions are quadratic fuel cost minimization, piecewise quadratic cost minimization, and quadratic fuel cost minimization considering the valve point effect. The DGWO is validated using the standard IEEE 30-bus test system. The obtained results showed the effectiveness and superiority of DGWO for solving the OPF problem compared with the other well-known meta-heuristic techniques.

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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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Optimal Power Flow Using Tabu Search Algorithm

Cited by 370 publications

References 24 publications

Population Variance Harmony Search Algorithm to Solve Optimal Power Flow with Non-Smooth Cost Function

Population Variance Harmony Search Algorithm to Solve Optimal Power Flow with Non-Smooth Cost Function

Solving Non-Smooth Optimal Power Flow Problems Using a Developed Grey Wolf Optimizer

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

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