Optimal real and reactive power control using linear programming

Mangoli, Maurice K.W; Lee, Kwang Y.; Park, Young Moon

doi:10.1016/0378-7796(93)90063-k

Cited by 157 publications

(98 citation statements)

References 8 publications

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“…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%

“…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.…”

Section: Introductionmentioning

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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Section: Simulation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Jeyanthy¹,

Devaraj²

2010

IJCEE

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“…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 functions which may lead to loss of accuracy.…”

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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“…Many numerical and heuristic methods such as quadratic programming [7], linear programming [8], the genetic algorithm [9], particle swarm optimization [10], and the artificial bee colony (ABC) algorithm [11] have been applied to solve the optimal reactive power flow (ORPF) problem of purely AC systems so far. As seen from the results reported in the literature, heuristic methods are superior to numerical methods [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

A new approach for optimal reactive power flow of MTDC systems using the ABC algorithm

Kılıç¹,

Ayan²

2017

Turk J Elec Eng & Comp Sci

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This paper presents a new approach to optimize reactive power flow of multiterminal high voltage direct current (HVDC) systems. Successful application of two-terminal DC systems worldwide makes the use of multiterminal direct current (MTDC) systems more attractive. Due to the economic and technical advantages of HVDC technology, MTDC systems have been used extensively in recent years. In this study, the artificial bee colony (ABC) algorithm is used for solution of the optimal reactive power flow problem of MTDC systems. In opposition to the current-balancing method used in the literature, this study represents a new approach for DC system power flow calculations. The proposed approach is tested on a sample IEEE MTDC test system. The results by the proposed approach are compared with those reported in the literature. Thus, the applicability and the efficiency of this approach used together with the ABC algorithm are shown.

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Optimal real and reactive power control using linear programming

Cited by 157 publications

References 8 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

Waning Of Real Power Loss By Modified Particle Swarm Optimization

A new approach for optimal reactive power flow of MTDC systems using the ABC algorithm

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