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Power System Nonlinear Modal Analysis Using Computationally Reduced Normal Form Method

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  • Nnaemeka Sunday Ugwuanyi

    (Arts et Métiers Institute of Technology, Université de Lille, Centrale Lille, HEI, EA 2697, L2EP—Laboratoire d’Electrotechnique et d’Electronique de Puissance, F-59000 Lille, France
    Electrical/Electronic Engineering department, AE- FUNAI, P.M.B. 1010, Abakaliki 480214, Nigeria)

  • Xavier Kestelyn

    (Arts et Métiers Institute of Technology, Université de Lille, Centrale Lille, HEI, EA 2697, L2EP—Laboratoire d’Electrotechnique et d’Electronique de Puissance, F-59000 Lille, France)

  • Bogdan Marinescu

    (Ecole Centrale de Nantes—Laboratoire des Sciences du Numérique de Nantes (LS2N), F-44000 Nantes, France)

  • Olivier Thomas

    (Arts et Metiers Institute of Technology—Laboratoire d’Ingenierie des Systèmes Physiques et Numériques (LISPEN), F-59000 Lille, France)

Abstract

Increasing nonlinearity in today’s grid challenges the conventional small-signal (modal) analysis (SSA) tools. For instance, the interactions among modes, which are not captured by SSA, may play significant roles in a stressed power system. Consequently, alternative nonlinear modal analysis tools, notably Normal Form (NF) and Modal Series (MS) methods are being explored. However, they are computation-intensive due to numerous polynomial coefficients required. This paper proposes a fast NF technique for power system modal interaction investigation, which uses characteristics of system modes to carefully select relevant terms to be considered in the analysis. The Coefficients related to these terms are selectively computed and the resulting approximate model is computationally reduced compared to the one in which all the coefficients are computed. This leads to a very rapid nonlinear modal analysis of the power systems. The reduced model is used to study interactions of modes in a two-area power system where the tested scenarios give same results as the full model, with about 70% reduction in computation time.

Suggested Citation

  • Nnaemeka Sunday Ugwuanyi & Xavier Kestelyn & Bogdan Marinescu & Olivier Thomas, 2020. "Power System Nonlinear Modal Analysis Using Computationally Reduced Normal Form Method," Energies, MDPI, vol. 13(5), pages 1-19, March.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:5:p:1249-:d:329850
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    References listed on IDEAS

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    1. Fuad Un-Noor & Sanjeevikumar Padmanaban & Lucian Mihet-Popa & Mohammad Nurunnabi Mollah & Eklas Hossain, 2017. "A Comprehensive Study of Key Electric Vehicle (EV) Components, Technologies, Challenges, Impacts, and Future Direction of Development," Energies, MDPI, vol. 10(8), pages 1-84, August.
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    Cited by:

    1. Luigi Fortuna & Arturo Buscarino, 2022. "Nonlinear Technologies in Advanced Power Systems: Analysis and Control," Energies, MDPI, vol. 15(14), pages 1-4, July.
    2. Natalia Bakhtadze & Igor Yadikin, 2020. "Discrete Predictive Models for Stability Analysis of Power Supply Systems," Mathematics, MDPI, vol. 8(11), pages 1-18, November.
    3. Natalia Bakhtadze & Igor Yadikin, 2021. "Analysis and Prediction of Electric Power System’s Stability Based on Virtual State Estimators," Mathematics, MDPI, vol. 9(24), pages 1-16, December.

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