Improved genetic algorithm for multi‐objective reactive power dispatch problem

Devaraj, D.

doi:10.1002/etep.146

Cited by 94 publications

(53 citation statements)

References 13 publications

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“…The effectiveness of the proposed approach is demonstrated through IEEE-30 bus system. The test results show the proposed algorithm gives better results with less computational burden and is fairly consistent in reaching the near optimal solution [10].In recent years, the problem of voltage stability and voltage collapse has become a major concern in power system planning and operation. To enhance the voltage stability, voltage magnitudes alone will not be a reliable indicator of how far an operating point is from the collapse point [11].…”

mentioning

confidence: 73%

Section: Introductionmentioning

confidence: 78%

“…The validity of the proposed Genetic Algorithm technique is demonstrated on IEEE-30 bus system. 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 (28-27) -are with the tap setting transformers. The real power settings are taken from [1].…”

Section: Simulation Resultsmentioning

confidence: 99%

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Optimal Reactive Power Dispatch for Voltage Stability Enhancement Using Real Coded Genetic Algorithm

Jeyanthy¹,

Devaraj²

2010

IJCEE

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Abstract-This paper presents an algorithm for solving the multi-objective reactive power dispatch problem in a power system. Modal analysis of the system is used for static voltage stability assessment. Loss minimization and maximization of voltage stability margin are taken as the objectives. Generator terminal voltages, reactive power generation of the capacitor banks and tap changing transformer setting are taken as the optimization variables. An improved genetic algorithm which permits the control variables to be represented in their natural form is proposed to solve this combinatorial optimization problem. For effective genetic operation, crossover and mutation operators which can directly operate on floating point number and integers are used. The proposed method has been tested on IEEE 30 bus system and has resulted in loss which is less than the value reported earlier and is well suited for solving the mixed integer optimization problem.Index Terms-Modal analysis, optimal reactive power dispatch, loss minimization, genetic algorithm. I. INTRODUCTIONOptimal reactive power dispatch problem is one of the difficult optimization problems in power systems. The sources of the reactive power are the generators, synchronous condensers, capacitors, static compensators and tap changing transformers. The problem that has to be solved in a reactive power optimization is to determine the required reactive generation at various locations so as to optimize the objective function. Here the reactive power dispatch problem involves best utilization of the existing generator bus voltage magnitudes, transformer tap setting and the output of reactive power sources so as to minimize the loss and to enhance the voltage stability of the system. It involves a non linear optimization problem. Various mathematical techniques have been adopted to solve this optimal reactive power dispatch problem. These include the gradient method [1-2], Newton method [3] and linear programming [4][5][6][7].The gradient and Newton methods suffer from the difficulty in handling inequality constraints. To apply linear programming, the input-output function is to be expressed as a set of linear P. Aruna Jeyanthy is with the Electrical and Electronics Engineering Department, N.I. College of Engineering Kanyakumari District, India (corresponding author to provide phone: 91 9443174253. email: arunadarwin@yahoo.com) D.Devaraj is with the Electrical and Electronics Engineering Department, Kalasalingam University, Srivilliputhur, India.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]. A genetic algorithm is a stochastic search technique based on the mechanics of natural selection. In this paper, genetic algorithm is used to solve the voltage constrained reactive power dispatch problem. The proposed algorithm identifies the optimal values of generation bus voltage magnitudes, transformer tap setting and the output of the reactive p...

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confidence: 73%

Section: Introductionmentioning

confidence: 78%

Section: Simulation Resultsmentioning

confidence: 99%

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Optimal Reactive Power Dispatch for Voltage Stability Enhancement Using Real Coded Genetic Algorithm

Jeyanthy¹,

Devaraj²

2010

IJCEE

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“…The effectiveness of the proposed approach is demonstrated through IEEE-30 bus system. The test results show the proposed algorithm gives better results with less computational burden and is fairly consistent in reaching the near optimal solution [10]. In this paper, FPA is applied for achieving enhanced computational speed and improved convergence profile of ORPD problem on standard IEEE-30 bus power system.…”

Section: Introductionmentioning

confidence: 88%

Solution of Optimal Reactive Power Dispatch by Flower Pollination Algorithm

Vigneswaran¹,

Sugumar²,

Vijayaraj³

2017

International Journal of Innovative Research in Electrical, Ele

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This study presents an efficient and reliable evolutionary-based approach, termed as Flower Pollination Algorithm (FPA), to solve the optimal reactive power dispatch (ORPD) problem of power system. The performance of the proposed FPA is examined and tested, successfully, on standard IEEE-30 test power systems for the solution of ORPD problem in which control of bus voltages, tap position of transformers and reactive power sources are involved. The objective function considered is either minimisation of active power transmission loss or that of total voltage deviation or enhancement of voltage stability index. The results offered by the proposed FPA are compared with those offered by other evolutionary optimisation techniques surfaced in the recent state-of-the-art literature. Simulation results indicate that the proposed FPA yields superior solution over the other recently surfaced popular techniques in terms of effectiveness and convergence speed.

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“…Differential harmony search (DHS) [43] and adaptive PSO (APSO) [44] were proposed to solve OPF problems. Computational intelligent-based techniques, such as genetic algorithm (GA) [45], improved GA [46], real parameter GA [47], adaptive GA [48], evolutionary programming (EP) [49], particle swarm optimization(PSO) [50], hybrid PSO [51], bacterial foraging optimization(BFO) [52], differential evolution (DE) [53][54][55], seeker optimization algorithm (SOA) [56], gravitational search algorithm (GSA) [57], etc, have been applied for solving ORPD problem. These techniques have shown effectiveness in overcoming the disadvantages in traditional methods.…”

Section: Introductionmentioning

confidence: 99%

Transmission Loss allocation with Optimal Power Flow using Gravitational Search Algorithm

Rao¹,

Rao²,

Sivanagaraju³

2014

International Journal of Innovative Research in Electrical, Ele

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Abstract:In deregulated power systems, the transmission loss allocation plays a key role in planning and designing of the power system. In practice, these losses should be allocated to both generators and loads depending on the amount of contribution in the total power system losses. In this paper, a new methodology to optimally allocate the transmission losses to either generators or loads based on the power flow tracing methodology is presented. In this methodology, trace usage coefficients are formulated to allocate transmission losses. In real time, system operator tries to minimize the transmission losses to increase the security of the system. In this paper, for the sack of analysis, the formulated OPF problem with transmission losses as objective is solved while satisfying system constraints using gravitational search algorithm. The minimized transmission losses are then allocated to either generators or loads. The proposed methodology is tested on standard IEEE-30 bus and real time Indian-24 bus test systems with supporting numerical results.

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Improved genetic algorithm for multi‐objective reactive power dispatch problem

Cited by 94 publications

References 13 publications

Optimal Reactive Power Dispatch for Voltage Stability Enhancement Using Real Coded Genetic Algorithm

Optimal Reactive Power Dispatch for Voltage Stability Enhancement Using Real Coded Genetic Algorithm

Solution of Optimal Reactive Power Dispatch by Flower Pollination Algorithm

Transmission Loss allocation with Optimal Power Flow using Gravitational Search Algorithm

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