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Neural Network Based Approach for Steady-State Stability Assessment of Power Systems

Author

Listed:
  • Tayo Uthman Badrudeen

    (Center for Cyber Physical Food, Energy and Water Systems, University of Johannesburg, Johannesburg 2006, South Africa)

  • Nnamdi I. Nwulu

    (Center for Cyber Physical Food, Energy and Water Systems, University of Johannesburg, Johannesburg 2006, South Africa)

  • Saheed Lekan Gbadamosi

    (Center for Cyber Physical Food, Energy and Water Systems, University of Johannesburg, Johannesburg 2006, South Africa
    Department of Electrical Engineering, Vaal University of Technology, Vanderbijlpark 1900, South Africa)

Abstract

The quest for an intelligence compliance system to solve power stability problems in real-time with high predictive accuracy, and efficiency has led to the discovery of deep learning (DL) techniques. This paper investigates the potency of several artificial neural network (ANN) techniques in assessing the steady-state stability of a power system. The new voltage stability pointer (NVSP) was employed to parameterize and reduce the input data to the neural network algorithms to predict the proximity of power systems to voltage instability. In this study, we consider five neural network algorithms viz. feedforward neural network (FFNN), cascade-forward neural network (CFNN), layer recurrent neural network (LRNN), linear layer neural network (LLNN), and Elman neural network (ENN). The evaluation is based on the predictability and accuracy of these techniques for dynamic stability in power systems. The neural network algorithms were trained to mimic the NVSP dataset using a Levenberg-Marquardt (LM) model. Similarly, the performance analyses of the neural network techniques were deduced from the regression learner algorithm (RLA) using a root-mean-squared error (rmse) and response plot graph. The effectiveness of these NN algorithms was demonstrated on the IEEE 30-bus system and the Nigerian power system. The simulation results show that the FFNN and the CFNN possess a relatively better performance in terms of accuracy and efficiency for the considered power networks.

Suggested Citation

  • Tayo Uthman Badrudeen & Nnamdi I. Nwulu & Saheed Lekan Gbadamosi, 2023. "Neural Network Based Approach for Steady-State Stability Assessment of Power Systems," Sustainability, MDPI, vol. 15(2), pages 1-13, January.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:2:p:1667-:d:1036406
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    References listed on IDEAS

    as
    1. Shi, Zhongtuo & Yao, Wei & Zeng, Lingkang & Wen, Jianfeng & Fang, Jiakun & Ai, Xiaomeng & Wen, Jinyu, 2020. "Convolutional neural network-based power system transient stability assessment and instability mode prediction," Applied Energy, Elsevier, vol. 263(C).
    2. Lei Sun & Wenjun Yi & Dandan Yuan & Jun Guan, 2019. "Application of Elman Neural Network Based on Genetic Algorithm in Initial Alignment of SINS for Guided Projectile," Mathematical Problems in Engineering, Hindawi, vol. 2019, pages 1-9, April.
    3. Tayo Uthman Badrudeen & Funso Kehinde Ariyo & Saheed Lekan Gbadamosi & Nnamdi I. Nwulu, 2022. "A Novel Classification of the 330 kV Nigerian Power Network Using a New Voltage Stability Pointer," Energies, MDPI, vol. 15(19), pages 1-21, October.
    Full references (including those not matched with items on IDEAS)

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