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An ASP-Based Approach for Phase Balancing in Power Electrical Systems

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Proceedings of the 21st EANN (Engineering Applications of Neural Networks) 2020 Conference (EANN 2020)

Abstract

Unbalanced electrical loads on feeders of power electrical systems can cause serious problems, including power losses, significantly lower power quality, damaging of electrical equipment, and tripping of protective devices. Nevertheless, the problem of balancing such systems—which essentially is equivalent to the problem of integer partitioning—has proven to be NP-complete. Against this background, in this article, an algorithm based on the powerful, declarative framework of Answer Set Programming (ASP) is provided, that efficiently attacks practical instances of the phase-balancing problem. To the best of our knowledge, this is the first attempt of approaching this significant engineering problem by means of the ASP paradigm. The whole study indicates that the examined problem is of great interest from an algorithmic viewpoint, as well as an engineering application that highlights ASP’s modelling methodology.

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Notes

  1. 1.

    The current of a linear electrical load (e.g., resistor, motor, capacitor) is, at any time, linearly proportional to its voltage.

  2. 2.

    HV and MV stand for High-Voltage and Medium-Voltage, respectively.

  3. 3.

    For a (non-zero) complex number z, arg(z) denotes the argument of z.

  4. 4.

    Recall that a multiset is a special type of set that allows for multiple instances for each of its elements.

  5. 5.

    For instance, the logic program \(\mathbf {P} = \big \{\ \texttt {a} ,\ \texttt {\{b\} :- a}\ \big \}\) has two answer sets; i.e., {a} and {a,b}.

  6. 6.

    https://potassco.org/clingo.

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Acknowledgements

The authors are grateful to the two anonymous reviewers for their valuable comments on this work.

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Correspondence to Theofanis Aravanis .

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Aravanis, T., Petratos, A., Douklia, G. (2020). An ASP-Based Approach for Phase Balancing in Power Electrical Systems. In: Iliadis, L., Angelov, P., Jayne, C., Pimenidis, E. (eds) Proceedings of the 21st EANN (Engineering Applications of Neural Networks) 2020 Conference. EANN 2020. Proceedings of the International Neural Networks Society, vol 2. Springer, Cham. https://doi.org/10.1007/978-3-030-48791-1_40

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  • DOI: https://doi.org/10.1007/978-3-030-48791-1_40

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-030-48791-1

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