A GA approach to compare ORPF objective functions including Secondary Voltage Regulation

Berizzi, Alberto; Bovo, Cristian; Merlo, Marco; Delfanti, Maurizio

doi:10.1016/j.epsr.2011.11.014

Cited by 83 publications

(66 citation statements)

References 18 publications

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“…The IEEE-30 bus system has 6 generator buses, 24 load buses and 41 transmission lines of which four branches are (6-9), (6-10) , (4)(5)(6)(7)(8)(9)(10)(11)(12) and Table 5 shows the proposed algorithm powerfully reduces the real power losses when compared to other given algorithms. The optimal values of the control variables along with the minimum loss obtained are given in Table 1.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…The reactive power support and voltage problems are internally related to each other.This paper formulates by combining both the real power loss minimization and maximization of Static Voltage Stability Margin (SVSM) as the objectives. Numerous Evolutionary algorithms have been already utilized to solve the reactive power flow problem [9][10][11][12][13][14][15][16][17][18][19][20]. This paper proposes Particle Distribution Algorithm (PDA) to solve reactive power dispatch problem.…”

Section: Introductionmentioning

confidence: 99%

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Particle Distribution Algorithm for Solving Optimal Reactive Power Dispatch Problem

Lenin¹

2017

IJARA

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In this paper, a novel perception of the particle swarm optimization (PSO) system called Particle Distribution Algorithm (PDA) is proposed to solve Reactive power problem. In that swarm individual particles are moved away, but discreetly characterize each particle by a field or by distribution & its behaviour absolutely dissimilar from the family of old PSO scheme. The projected PDA has been tested in standard IEEE 30 bus test system and simulation results show clearly the better performance of the proposed algorithm in reducing the real power loss.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Particle Distribution Algorithm for Solving Optimal Reactive Power Dispatch Problem

Lenin¹

2017

IJARA

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“…The problem of voltage stability plays a strategic role in power system planning and operation [8]. So many Evolutionary algorithms have been already proposed to solve the reactive power flow problem [9][10][11]. In [12,13], Hybrid differential evolution algorithm and Biogeography Based algorithm has been projected to solve the reactive power dispatch problem.…”

Section: Introductionmentioning

confidence: 99%

Modified Monkey Optimization Algorithm for Solving Optimal Reactive Power Dispatch Problem

Lenin

Reddy

Kalavathi

2015

IJEEI

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In this paper, a novel approach Modified Monkey optimization (MMO) IntroductionReactive power optimization plays a key role in optimal operation of power systems. Many numerical methods [1][2][3][4][5][6][7] have been applied to solve the optimal reactive power dispatch problem. The problem of voltage stability plays a strategic role in power system planning and operation [8]. So many Evolutionary algorithms have been already proposed to solve the reactive power flow problem [9][10][11]. In [12,13], Hybrid differential evolution algorithm and Biogeography Based algorithm has been projected to solve the reactive power dispatch problem. In [14,15], a fuzzy based technique and improved evolutionary programming has been applied to solve the optimal reactive power dispatch problem. In [16,17] nonlinear interior point method and pattern based algorithm has been used to solve the reactive power problem. In [18][19][20], various types of probabilistic algorithms utilized to solve optimal reactive power problem. This paper introduces a novel Modified Monkey optimization for solving optimal reactive power dispatch power problem. Monkey Optimization algorithm [21] is fresh entry in class of swarm intelligence. This Monkey Optimization algorithm is enthused by fission fusion social structure based on foraging behaviour of monkeys when searching for quality food source and for mating. Alike to any other population based optimization techniques, artificial bee colony (ABC) consists of a population of intrinsic solutions. The intrinsic solutions are food sources of honey bees. The fitness is decided in terms of the quality of the food source that is nectar amount. Artificial bee colony is comparatively a direct, quick and population based stochastic exploration technique in the field of nature inspired algorithms. Monkey Optimization algorithm is also alike to ABC in nature. There are two fundamental processes which drive the swarm to modernize in ABC: the deviation process, which empowers exploring different fields of the exploration space, and the selection process, which guarantees the exploitation of the preceding experience. However, it has been shown that the ABC may infrequently stop moving toward the global optimum even though the population has not meeting to a local optimum [22]. It can be observed that the solution search equation of ABC algorithm is good at exploration but poor at exploitation [23]. Therefore, to uphold the good equilibrium between exploration and exploitation behaviour of ABC, it is extremely expected to develop a local exploration method in the basic ABC to strengthen the exploration region. The proposed MMO algorithm has been evaluated in standard IEEE 30 bus test system & the simulation results show that our proposed approach outperforms all reported algorithms in minimization of real power loss.

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“…To apply linear programming, the input-output function is to be expressed as a set of linear functions which may lead to loss of accuracy. Recently global optimization techniques such as genetic algorithms have been proposed to solve the reactive power flow problem [8,9]. In recent years, the problem of voltage stability and voltage collapse has become a major concern in power system planning and operation.…”

Section: Introductionmentioning

confidence: 99%

Waning Of Real Power Loss By Modified Particle Swarm Optimization

Lenin¹

2017

IJARA

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This paper presents Modified Particle Swarm Optimization (MPSO) algorithm for solving reactive power problem. A nonlinear decreasing weight factor used to change the fundamental ways of Particle Swarm Optimization (PSO). To allow full play to the approximation capability of the function of Back Propagation neural network & overcome the main shortcomings of its liability to fall into local extreme value this study proposed a concept of modified PSO algorithm. Back Propagation network jointly to optimize the original weight, threshold value of network and incorporating the PSO algorithm into Back Propagation network to establish a Modified PSO-BP network system. Proposed method MPSO progresses convergence speed and the capability to search optimal value. In order to evaluate the proposed MPSO algorithm, it has been tested on IEEE 30 bus system and compared to other reported standard algorithms and simulation results show that (MPSO) is more efficient than other algorithms in reducing the real power loss & voltage profiles are within the limits. Lenin K, Researcher, Jawaharlal Nehru Technological University, Kukatpally, Hyderabad, 500 085, India, related. Hence, this paper formulates the reactive power dispatch as a multi-objective optimization problem with loss minimization and maximization of Static Voltage Stability Margin (SVSM) as the objectives. The Particle Swarm Optimization (PSO) [11][12][13][14][15] was based on the optimal algorithm of swarm intelligence and it guides optimal search through swarm intelligence producing by the corporation and competition among particles. The artificial Neural Network possesses the ability of processing database in parallel, organizing and learning by itself. It has made great achievements in many fields. Especially neural network technique has been used in reliability and few researchers apply the method [16][17][18][19][20] to predict failures. Theoretically, Network [21-32] may approach any continual nonlinear function. However, affected greatly by the sample, it may fall into local minimum value, thus it can't ensure that it converge the minimum value in overall situations. In our paper PSO algorithm is introduced into BP network optimization by using iterate algorithm of particle swarm instead of gradient correction algorithm of BP network. This method can shorten the time of training network and improve the convergence speed of BP algorithm. However, according to different system object, if basic particle swarm algorithm is modified, the performance of BP network is improved and its practicality is better. For balancing between the global search and the local search and improving the precision of the result, this study proposed the nonlinear strategies for decreasing inertia weight based on the idea of the existing linear decreasing inertia weight. The result of simulation shows that the optimizing methods speed the rapidity of convergence obviously and the precision of simulation increase greatly. The performance of Modified Particle Swarm Optimization (MPS...

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A GA approach to compare ORPF objective functions including Secondary Voltage Regulation

Cited by 83 publications

References 18 publications

Particle Distribution Algorithm for Solving Optimal Reactive Power Dispatch Problem

Particle Distribution Algorithm for Solving Optimal Reactive Power Dispatch Problem

Modified Monkey Optimization Algorithm for Solving Optimal Reactive Power Dispatch Problem

Waning Of Real Power Loss By Modified Particle Swarm Optimization

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