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Load Margin Assessment of Power Systems Using Physics-Informed Neural Network with Optimized Parameters

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  • Murilo Eduardo Casteroba Bento

    (Department of Electrical Engineering, Federal University of Rio de Janeiro, Rio de Janeiro 21941909, Brazil)

Abstract

Challenges in the operation of power systems arise from several factors such as the interconnection of large power systems, integration of new energy sources and the increase in electrical energy demand. These challenges have required the development of fast and reliable tools for evaluating the operation of power systems. The load margin (LM) is an important index in evaluating the stability of power systems, but traditional methods for determining the LM consist of solving a set of differential-algebraic equations whose information may not always be available. Data-Driven techniques such as Artificial Neural Networks were developed to calculate and monitor LM, but may present unsatisfactory performance due to difficulty in generalization. Therefore, this article proposes a design method for Physics-Informed Neural Networks whose parameters will be tuned by bio-inspired algorithms in an optimization model. Physical knowledge regarding the operation of power systems is incorporated into the PINN training process. Case studies were carried out and discussed in the IEEE 68-bus system considering the N-1 criterion for disconnection of transmission lines. The PINN load margin results obtained by the proposed method showed lower error values for the Root Mean Square Error (RMSE), Mean Square Error (MSE) and Mean Absolute Percentage Error (MAPE) indices than the traditional training Levenberg-Marquard method.

Suggested Citation

  • Murilo Eduardo Casteroba Bento, 2024. "Load Margin Assessment of Power Systems Using Physics-Informed Neural Network with Optimized Parameters," Energies, MDPI, vol. 17(7), pages 1-20, March.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:7:p:1562-:d:1363386
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    References listed on IDEAS

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    1. Chinmayee Biswal & Binod Kumar Sahu & Manohar Mishra & Pravat Kumar Rout, 2023. "Real-Time Grid Monitoring and Protection: A Comprehensive Survey on the Advantages of Phasor Measurement Units," Energies, MDPI, vol. 16(10), pages 1-34, May.
    2. Gokhale, Gargya & Claessens, Bert & Develder, Chris, 2022. "Physics informed neural networks for control oriented thermal modeling of buildings," Applied Energy, Elsevier, vol. 314(C).
    3. Prottay M. Adhikari & Luigi Vanfretti & Hao Chang & Koushik Kar, 2023. "Real-Time Control of a Battery Energy Storage System Using a Reconfigurable Synchrophasor-Based Control System," Energies, MDPI, vol. 16(19), pages 1-20, September.
    4. Sepideh Radhoush & Trevor Vannoy & Kaveen Liyanage & Bradley M. Whitaker & Hashem Nehrir, 2023. "Distribution System State Estimation and False Data Injection Attack Detection with a Multi-Output Deep Neural Network," Energies, MDPI, vol. 16(5), pages 1-22, February.
    5. Hu, Guoqing & You, Fengqi, 2023. "An AI framework integrating physics-informed neural network with predictive control for energy-efficient food production in the built environment," Applied Energy, Elsevier, vol. 348(C).
    6. Smitha Joyce Pinto & Pierluigi Siano & Mimmo Parente, 2023. "Review of Cybersecurity Analysis in Smart Distribution Systems and Future Directions for Using Unsupervised Learning Methods for Cyber Detection," Energies, MDPI, vol. 16(4), pages 1-24, February.
    7. Onkemetse Tshenyego & Ravi Samikannu & Bokani Mtengi & Modisa Mosalaosi & Tshiamo Sigwele, 2023. "A Graph-Theoretic Approach for Optimal Phasor Measurement Units Placement Using Binary Firefly Algorithm," Energies, MDPI, vol. 16(18), pages 1-30, September.
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