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Momentum-based wavelet and double wavelet neural networks for power system applications

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Abstract

In order to minimize the power loss and to control the voltage in the power systems, the proposed momentum-based wavelet neural network and proposed momentum-based double wavelet neural network are proposed in this paper. The training data are obtained by using linear programming method by solving several abnormal conditions. The control variables considered are generator voltages and transformer taps, and the dependent variables are generator reactive powers and load bus voltages. The IEEE 14-bus system and IEEE 30-bus system are tested using the linear programming, Levenberg–Marquardt artificial neural network, proposed momentum-based wavelet neural network and proposed momentum-based double wavelet neural network to validate the effectiveness of the proposed MDWNN method. The trained neural networks are capable of controlling the voltage, and reactive power in power systems is proved by the results with the high level of precision and speed.

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Acknowledgments

The authors express their sincere thanks to the authorities of Anna University for providing necessary facilities to carry out this research work. The authors are grateful to University Grants Commission, New Delhi for providing the financial grant under Maulana Abul Kalam Azad Scheme to carry out this research work.

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Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, Rajalakshmi Engineering College, Chennai, Tamilnadu, India

    Subramaniam Nachimuthu Deepa & John Basha Rizwana

  2. 85/1, Ganesh Nagar First Street, G.N. Mill (Post), Coimbatore, Tamilnadu, 641 029, India

    John Basha Rizwana

Authors
  1. Subramaniam Nachimuthu Deepa
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  2. John Basha Rizwana
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Corresponding author

Correspondence to John Basha Rizwana.

Appendices

Appendix 1: Specification of IEEE 14-bus system

The maximum and minimum limits of control variables and dependent variables are specified in Tables 8 and 9.

Table 8 Control variable limits of IEEE 14-bus system
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Table 9 Dependent variable limits of IEEE 14-bus system
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Appendix 2: Specification of IEEE 30-bus system

The maximum and minimum limits of control variables and dependent variables are specified in Tables 10 and 11.

Table 10 Control variable limits of IEEE 30-bus system
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Table 11 Dependent variable limits of IEEE 30-bus system
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Deepa, S.N., Rizwana, J.B. Momentum-based wavelet and double wavelet neural networks for power system applications. Neural Comput & Applic 29, 495–511 (2018). https://doi.org/10.1007/s00521-016-2552-9

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  • DOI: https://doi.org/10.1007/s00521-016-2552-9

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