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Static ATC Estimation Using Fully Complex-Valued Radial Basis Function Neural Network

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Artificial Intelligence and Evolutionary Algorithms in Engineering Systems

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 325))

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Abstract

In the deregulated power systems, the available transfer capability (ATC) should be known for secure and reliable operation. ATC mainly depends on load for a particular transaction. Due to complex nature of load, it is better if the ATC estimator is able to handle this complex nature. This paper presents fully complex-valued radial basis function (FC-RBF) neural network approach for ATC estimation for bilateral transaction under normal condition. The training patterns for neural network are generated using differential evolution algorithm (DEA). The important feature of the proposed method is the use of input reduction techniques to improve the performance of the developed network. Differential evolution feature selection (DEFS) technique is proposed to reduce the complexity and training time of neural network. The proposed method is tested on IEEE 118 bus system, and results are compared with DEA and repeated power flow (RPF) results. The test results show that the proposed method reduces the training time and it is suitable for online application.

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Authors and Affiliations

  1. Kalasalingam University, Srivilliputhur, Tamil Nadu, India

    M. Karuppasamypandiyan

  2. Vellammal College of Engineering and Technology, Madurai, Tamil Nadu, India

    R. Narmatha Banu & P. M. Manobalaa

Authors
  1. M. Karuppasamypandiyan
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  2. R. Narmatha Banu
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  3. P. M. Manobalaa
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Corresponding author

Correspondence to M. Karuppasamypandiyan .

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Editors and Affiliations

  1. Electrical & Electronics Engineering, Noorul Islam Centre for Higher Education, Kumaracoil, Tamil Nadu, India

    L Padma Suresh

  2. Electrical and Electronics Engineering, SRM Engineering College, Kattankulathur, Tamil Nadu, India

    Subhransu Sekhar Dash

  3. Electrical Engineering, IIT Delhi, New Delhi, Delhi, India

    Bijaya Ketan Panigrahi

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Karuppasamypandiyan, M., Banu, R.N., Manobalaa, P.M. (2015). Static ATC Estimation Using Fully Complex-Valued Radial Basis Function Neural Network. In: Suresh, L., Dash, S., Panigrahi, B. (eds) Artificial Intelligence and Evolutionary Algorithms in Engineering Systems. Advances in Intelligent Systems and Computing, vol 325. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2135-7_80

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  • DOI: https://doi.org/10.1007/978-81-322-2135-7_80

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  • Publisher Name: Springer, New Delhi

  • Print ISBN: 978-81-322-2134-0

  • Online ISBN: 978-81-322-2135-7

  • eBook Packages: EngineeringEngineering (R0)

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