Reactive power and voltage control of the nigerian grid system using micro-genetic algorithm

Bakare, G.A.; Venayagamoorthy, Ganesh K.; Aliyu, Usman

doi:10.1109/pes.2005.1489424

Cited by 34 publications

(12 citation statements)

References 11 publications

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“…The marker's distribution was estimated by means of a micro genetic algorithm (μGA) [1] where the chromosome consists of an X vector with "1" and "0" entries. The fitness value was measured as the PCA recognition performance obtained over a subset of test images considering the application of the X vector on these images and the training images.…”

Section: Estimation Of Markersmentioning

confidence: 99%

Noise-Removal Markers to Improve PCA-Based Face Recognition

Caballero‐Morales

2014

Lecture Notes in Computer Science

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Abstract. In this paper an approach based on insertion of "markers" is proposed to increase the performance of face recognition based on principal component analysis (PCA). The markers represent zero-valued pixels which are expected to remove information likely to affect classification (noisy pixels). The patterns of the markers was optimized with a genetic algorithm (GA) in contrast to other noise generation techniques. Experiments performed with a well known face database showed that the technique was able to achieve significant improvements on PCA particularly when data for training was small in comparison with the size of testing sets. This was also observed when the number of eigenfaces used for classification was small.

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Section: Estimation Of Markersmentioning

confidence: 99%

Noise-Removal Markers to Improve PCA-Based Face Recognition

Caballero‐Morales

2014

Lecture Notes in Computer Science

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“…The first idea is the use of a small population of particles, few as five or lesser; calling this algorithm the Small Population based PSO (SPPSO). This idea is synonymous to the Micro-GA (µGA) algorithm [18]. The second idea is the regeneration concept where new particles are randomly created every N iterations to replace all but the gbest particle in the swarm.…”

Section: Pso and Sppso Algorithmmentioning

confidence: 99%

Optimal Design of SVC Damping Controllers with Wide Area Measurements Using Small Population based PSO

Das

Jetti

Venayagamoorthy

2006

The 2006 IEEE International Joint Conference on Neural Network Proceedings

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“…Ref. [7] may be referred for further discussion on these techniques. Hence, it becomes essential to develop optimization techniques that are capable of overcoming these drawbacks.…”

Section: Introductionmentioning

confidence: 99%

Solution of reactive power dispatch of power systems by an opposition-based gravitational search algorithm

Shaw¹,

Mukherjee²,

Ghoshal³

2014

International Journal of Electrical Power & Energy Systems

195

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a b s t r a c tGravitational search algorithm (GSA) is based on law of gravity and the interaction between masses. In GSA, searcher agents are collection of masses and their interactions are based on Newtonian laws of gravity and motion. In this paper, to further improve the optimization performance of GSA, opposition-based learning is employed in opposition-based gravitational search algorithm (OGSA) for population initialization and also for generation jumping. In the present work, OGSA is applied for the solution of optimal reactive power dispatch (ORPD) of power systems. Traditionally, ORPD is defined as the minimization of active power transmission losses by controlling a number of control variables. ORPD is formulated as a non-linear constrained optimization problem with continuous and discrete variables. In this work, OGSA is used to find the settings of control variables such as generator voltages, tap positions of tap changing transformers and amount of reactive compensation to optimize certain objectives. The study is implemented on IEEE 30-, 57-and 118-bus test power systems with different objectives that reflect minimization of either active power loss or that of total voltage deviation or improvement of voltage stability index. The obtained results are compared to those yielded by the other evolutionary optimization techniques surfaced in the recent state-of-the-art literature including basic GSA. The results presented in this paper demonstrate the potential of the proposed approach and show its effectiveness and robustness for solving ORPD problems of power systems.

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Reactive power and voltage control of the nigerian grid system using micro-genetic algorithm

Cited by 34 publications

References 11 publications

Noise-Removal Markers to Improve PCA-Based Face Recognition

Noise-Removal Markers to Improve PCA-Based Face Recognition

Optimal Design of SVC Damping Controllers with Wide Area Measurements Using Small Population based PSO

Solution of reactive power dispatch of power systems by an opposition-based gravitational search algorithm

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