Transmission network expansion planning based on hybridization model of neural networks and harmony search algorithm

Ameli, M.; Shivaie, M.; Moslehpour, S.

Growing Science » International Journal of Industrial Engineering Computations » Transmission network expansion planning based on hybridization model of neural networks and harmony search algorithm

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International Journal of Industrial Engineering Computations

ISSN 1923-2934 (Online) - ISSN 1923-2926 (Print) Quarterly Publication Volume 3 Issue 1 pp. 71-80 , 2012

Transmission network expansion planning based on hybridization model of neural networks and harmony search algorithm Pages 71-80 Download PDF

Authors: Mohammad Taghi Ameli, Mojtaba Shivaie, Saeid Moslehpour

DOI: 10.5267/j.ijiec.2011.08.018

Keywords: Artificial intelligence, Harmony search algorithm, Probabilistic neural networks, Transmission network expansion planning

Abstract: Transmission Network Expansion Planning (TNEP) is a basic part of power network planning that determines where, when and how many new transmission lines should be added to the network. So, the TNEP is an optimization problem in which the expansion purposes are optimized. Artificial Intelligence (AI) tools such as Genetic Algorithm (GA), Simulated Annealing (SA), Tabu Search (TS) and Artificial Neural Networks (ANNs) are methods used for solving the TNEP problem. Today, by using the hybridization models of AI tools, we can solve the TNEP problem for large-scale systems, which shows the effectiveness of utilizing such models. In this paper, a new approach to the hybridization model of Probabilistic Neural Networks (PNNs) and Harmony Search Algorithm (HSA) was used to solve the TNEP problem. Finally, by considering the uncertain role of the load based on a scenario technique, this proposed model was tested on the Garver’s 6-bus network.

How to cite this paper

 Ameli, M., Shivaie, M & Moslehpour, S. (2012). Transmission network expansion planning based on hybridization model of neural networks and harmony search algorithm.International Journal of Industrial Engineering Computations , 3(1), 71-80.

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