Optimal placement of SVC for minimizing power loss and improving voltage profile using GA

Dixit, Shishir; Srivastava, Laxmi; Agnihotri, Ganga

doi:10.1109/icicict.2014.6781264

Cited by 16 publications

(14 citation statements)

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“…GA is also a global search method which search for numerous solutions without knowing solution properties in advance. Real coded genetic algorithm (RCGA) is very similar to binary GA [27,[30][31][32][33]. The difference between binary coded GA (BCGA) and RCGA is that variables in RCGA are represented by floating-point numbers instead of bits of zero and ones.…”

Section: Best Compromise Solutionmentioning

confidence: 99%

“…Use of floating-point numbers for representation of the problem parameters in RCGA has overcome such difficulties specially when dealing with continuous search space. The structure of RCGA basically includes three main steps: selection, crossover (recombination) and mutation [27,30].…”

Section: Best Compromise Solutionmentioning

confidence: 99%

“…In this paper, Roulette wheel selection technique has been used [27,30]. By selecting probability of crossover P C near to 1, crossover has been applied between the best selected individuals (i.e.…”

Section: Best Compromise Solutionmentioning

confidence: 99%

“…parents) to create a new individuals. The intermediate recombination has been also applied in this paper [27].…”

Section: Best Compromise Solutionmentioning

confidence: 99%

“…The BB-BC demonstrates superior performance and convergence characteristics in compare of PSO [26]. In [27] for enhancing voltage profile and minimizing power loss, voltage deviation and size of SVC, the optimal location and size of SVC has been obtained using GA and critical contingency. For improving voltage stability of the power system the optimal location and size of TCSC has been find out using PSO and PSO-TVAC.…”

Section: Introductionmentioning

confidence: 99%

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Optimal Placement of SVC using NSGA-II

Dixit¹,

Srivastava²,

Agnihotri³

2016

IJGDC

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Improving voltage stability, reducing real power loss (PL) and voltage deviation (VD) are the most important tasks in the operation of electrical power systems. Voltage instability and voltage collapse are the severe problems which may take place because of deficit reactive power at load buses due to increased loading or contingencies. In this paper, the problem of obtaining optimal location and size of SVC is formulated as true multi-objective optimization problem for simultaneous minimization of the two objectives namely real power losses and load bus voltage deviation. The two algorithms real coded genetic algorithm (RCGA) and non-dominated sorting genetic algorithm-II (NSGA-II) with a feature of adoptive crowding distance have been used for solving nonlinear constrained multi-objective optimization problem. Both the algorithms have been used for obtaining optimal location and sizing of SVC. Voltage security of the power system has also been analyzed separately for all placement of SVC to ensure secure operation of the system. The proposed approaches have been implemented on IEEE 30-bus test system. The simulation results of the two algorithms have been compared for solution quality, computational complexity and computational time. It has been found that NSGA-II presents better performance in solving multi-objective optimization problem and also in obtaining a diverse set of solutions which converge near the true Pareto-optimal front. The simulation results of NSGA-II have also been presented to exhibit the capabilities of the algorithm to generate well-distributed Pareto-optimal front.

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Section: Best Compromise Solutionmentioning

confidence: 99%

Section: Best Compromise Solutionmentioning

confidence: 99%

Section: Best Compromise Solutionmentioning

confidence: 99%

“…parents) to create a new individuals. The intermediate recombination has been also applied in this paper [27].…”

Section: Best Compromise Solutionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%