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Static VAR compensator using recurrent neural network

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Abstract

In this paper, an internal model control recurrent neural network method is used to control the switching of thyristor-controlled reactor in a static VAR compensator (SVC) system for regulating the voltage. The novel controller scheme contains several feedback loops instead of only a feed-forward loop as in the conventional recurrent neural network (RNN). In the proposed controller model, the RNN identifier creates a sample of the connected system and its output generates a part of inputs for the RNN controller which then sends the control signal to the SVC system. Three types of non-linear conditions are chosen to test the operational capability of the new control system to perform the voltage regulation satisfying the IEEE Std 519-1992. The test cases contain a three-phase fault power system, opening of one of the transmission lines in a double line transmission system and sudden changes in the load demand. Results show that the proposed control model is capable of regulating the voltage of the system in a desired range.

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Acknowledgments

The group would like to thank Universiti Teknologi Malaysia under Flagship Grant No: QK130000.2423.00G20 for their support and funding this work.

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Authors and Affiliations

  1. Centre for Artificial Intelligence and Robotics, Universiti Teknologi Malaysia international campus, 54100 , Kuala Lumpur, Malaysia

    R. Rahmani, M. F. Othman, A. A. Shojaei & R. Yusof

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  1. R. Rahmani
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  2. M. F. Othman
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  3. A. A. Shojaei
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  4. R. Yusof
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Correspondence to M. F. Othman.

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Rahmani, R., Othman, M.F., Shojaei, A.A. et al. Static VAR compensator using recurrent neural network. Electr Eng 96, 109–119 (2014). https://doi.org/10.1007/s00202-013-0287-5

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