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Full-Text Articles in Engineering
"Var Planning In Distribution Systems Via Genetic Operators", Fabricio I. Salgado, Enrique A. López, Hugh Rudnick
"Var Planning In Distribution Systems Via Genetic Operators", Fabricio I. Salgado, Enrique A. López, Hugh Rudnick
Fabricio I. Salgado MSc.
A new solution for VAR planning in distribution systems is proposed. It is based on Genetic Algorithms (GAs). Allelic Alphabet and Hamming Distance concepts are efficiently exploited under real coding. The model’s robustness is demonstrated through a 69-node system. The effect of the genetic operators is quantified (mutation probability and mutation radius, crossover probability) regarding the convergence of the solution. The procedure allows obtaining a realistic solution for the VAR planning problem.
Artificial Neural Network Learning Using Improved Genetic Algorithms, D. K. Chaturvedi
Artificial Neural Network Learning Using Improved Genetic Algorithms, D. K. Chaturvedi
D. K. Chaturvedi Dr.
The feedforward back-propagation artificial neural networks (ANN) are widely used to control the various industrial process, for modelling, simulation of systems and forecasting. The backpropagation learning has various drawbacks such as slowness in learning, stuck in local minima, requies functional derivative of aggregation function and thresholding function to minimize error function. Various researchers have suggested a number of improvement in simple back-propagation learning algorithm developed by Widrow and Holf in 1956. In this paper, a program is developed for feedforward artificial neural network with genetic algorithm (GA) as the learning mechanism to overcome some of the disadvantages of back-propagation learning …
Load Forecasting Using Genetic Algorithms, D. K. Chaturvedi, R. K. Mishra, A. Agarwal
Load Forecasting Using Genetic Algorithms, D. K. Chaturvedi, R. K. Mishra, A. Agarwal
D. K. Chaturvedi Dr.
Genetic Algorithms (GAs) are gaining popularity in many engineering and scientific applications due to their enormous advantages such as adaptibility, ability to handle non-linear, ill defined and probabilistic problems. In this paper load forecasting problem on long term basis is formulated in the frame work of Genetic Algorithms. The results of GAs are compared with the central Electricity Authority (CEA) forecasted data to demonstrate the effectiveness of the proposed algorithms.