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

Journals

IJIEC Volumes

Volume 1 (17)
- Issue 1 (9)
- Issue 2 (8)

Volume 2 (68)
- Issue 1 (12)
- Issue 2 (20)
- Issue 3 (20)
- Issue 4 (16)

Volume 3 (76)
- Issue 1 (9)
- Issue 2 (15)
- Issue 3 (20)
- Issue 4 (12)
- Issue 5 (20)

Volume 4 (50)
- Issue 1 (14)
- Issue 2 (10)
- Issue 3 (12)
- Issue 4 (14)

Volume 5 (47)
- Issue 1 (13)
- Issue 2 (12)
- Issue 3 (12)
- Issue 4 (10)

Volume 6 (39)
- Issue 1 (7)
- Issue 2 (12)
- Issue 3 (10)
- Issue 4 (10)

Volume 7 (47)
- Issue 1 (10)
- Issue 2 (14)
- Issue 3 (10)
- Issue 4 (13)

Volume 8 (30)
- Issue 1 (9)
- Issue 2 (7)
- Issue 3 (8)
- Issue 4 (6)

Volume 9 (32)
- Issue 1 (9)
- Issue 2 (6)
- Issue 3 (7)
- Issue 4 (10)

Volume 10 (34)
- Issue 1 (8)
- Issue 2 (10)
- Issue 3 (8)
- Issue 4 (8)

Volume 11 (36)
- Issue 1 (9)
- Issue 2 (8)
- Issue 3 (9)
- Issue 4 (10)

Volume 12 (29)
- Issue 1 (9)
- Issue 2 (6)
- Issue 3 (8)
- Issue 4 (6)

Volume 13 (41)
- Issue 1 (10)
- Issue 2 (8)
- Issue 3 (10)
- Issue 4 (13)

Volume 14 (50)
- Issue 1 (11)
- Issue 2 (15)
- Issue 3 (9)
- Issue 4 (15)

Volume 15 (55)
- Issue 1 (19)
- Issue 2 (15)
- Issue 3 (12)
- Issue 4 (9)

Countries

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.

Refrences

Abdelaziz, A.R. (2000).Genetic algorithm based power transmission expansion planning. In: Proceedings of the 7th IEEE international conference on electronics, Circuits and systems, 2, 642–645.

Al-Saba, T. & El-Amin, I. (2002). The application of artificial intelligent tools to the transmission expansion problem. International Journal of Electric Power Systems Research (Elsevier), 62, 117-126.

Binato, S., Oliveira, G.C. & Arajo, J.L. (2001). A greedy randomized adaptive search procedure for transmission expansion planning. IEEE Transactions on Power Systems, 16 (2), 247–53.

Choi, J., Tran, T., El-Keib, A. A., Thomas, R., Oh, H.S., & Billinton, R. (2005). A Method for Transmission System Expansion Planning Considering Probabilistic Reliability Criteria. IEEE Transactions on Power Systems, 20, 1606-1615.

Christodoulou, C., & Georgiopoulos, M. (2001). Applications of Neural networks in Electromagnetics. Artech House Publishers.

De, I., Silva, J., Rider, M.J., Romero, R., & Murari, C.A. (2005). Transmission network expansion planning considering uncertainness in demand. In: Proceeding of IEEE power engineering society general meeting, 2, 1424-1429.

Eliassi, M., Seifi, H. & Haghifam, M.R. (2009). Multi-Objective Value-Based Reliability Transmission Planning Using Expected Interruption Cost Due to Transmission Constraint. Electric Power and Energy Conversion Systems (EPECS & apos; 09), International Conference on 10-12, 1-8.

Latorre, G., Cruz, R. D., Areiza, J. M., & Villegas A. (2003). Classification of publications and models in transmission expansion planning. IEEE Transactions PWRS, 18, 938-946.

MATLAB Neural Network Toolbox User’s Guide, The MathWorks, 1998.

Sadegheih, A. & Drake, P.R. (2008). System network planning expansion using mathematical programming, genetic algorithms and tabu search. International Journal of Energy Conversion and Management (Elsevier), 49, 1557–1566.

Shariati, H., Askarian Abyaneh, H. & Javidi, M. H. (2008). Transmission Expansion Planning Considering Security Cost under Market Environment. DRPT 2008 6-9 April 2008 Nanjing China, 1430-1435.

Shayeghi, H., Jalilzadeh, S., Mahdavi, M. & Haddadian, H. (2008). Studying influence of two effective parameters on network losses in transmission expansion planning using DCGA. International Journal of Energy Conversion and Management (Elsevier), 49, 3017–3024.

Silva, I.J., Rider, M. J. & Romero, R. (2005). Transmission Network Expansion Planning Considering Uncertainty in Demand. IEEE Transactions on Power Systems, 21, 1565-1573.

Vasebi, A., Fesanghary, M. & Bathaee, S.M.T. (2007). Combined heat and power economic dispatch by harmony search algorithm. International Journal of Electrical Power and Energy Systems (Elsevier), 29, 713–719.

Youseef, H. K. M. (2001). Dynamic transmission planning using a constrained genetic algorithm. International Journal of Electrical Power and Energy Systems (Elsevier), 23, 857-862.