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Performance Analysis for Predictive Voltage Stability Monitoring Using Enhanced Adaptive Neuro-Fuzzy Expert System

Author

Listed:
  • Oludamilare Bode Adewuyi

    (Centre for Intelligence Systems and Emerging Technologies, Department of Electrical, Electronic and Computer Engineering, Cape Peninsula University of Technology, Bellville 7535, South Africa)

  • Senthil Krishnamurthy

    (Centre for Intelligence Systems and Emerging Technologies, Department of Electrical, Electronic and Computer Engineering, Cape Peninsula University of Technology, Bellville 7535, South Africa)

Abstract

Intelligent voltage stability monitoring remains an essential feature of modern research into secure operations of power system networks. This research developed an adaptive neuro-fuzzy expert system (ANFIS)-based predictive model to validate the viability of two contemporary voltage stability indices (VSIs) for intelligent voltage stability monitoring, especially at intricate loading and operation points close to voltage collapse. The Novel Line Stability Index (NLSI) and Critical Boundary Index are VSIs deployed extensively for steady-state voltage stability analysis, and thus, they are selected for the predictive model implementation. Six essential power system operational parameters with data values calculated at varying real and reactive loading levels are input features for ANFIS model implementation. The model’s performance is evaluated using reliable statistical error performance analysis in percentages ( M A P E and R R M S E p ) and regression analysis based on Pearson’s correlation coefficient ( R ). The IEEE 14-bus and IEEE 118-bus test systems were used to evaluate the prediction model over various network sizes and complexities and at varying clustering radii. The percentage error analysis reveals that the ANFIS predictive model performed well with both VSIs, with CBI performing comparatively better based on the comparative values of M A P E , R R M S E p , and R at multiple simulation runs and clustering radii. Remarkably, CBI showed credible potential as a reliable voltage stability indicator that can be adopted for real-time monitoring, particularly at loading levels near the point of voltage instability.

Suggested Citation

  • Oludamilare Bode Adewuyi & Senthil Krishnamurthy, 2024. "Performance Analysis for Predictive Voltage Stability Monitoring Using Enhanced Adaptive Neuro-Fuzzy Expert System," Mathematics, MDPI, vol. 12(19), pages 1-16, September.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:19:p:3008-:d:1486740
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    References listed on IDEAS

    as
    1. Mir Sayed Shah Danish & Tomonobu Senjyu & Sayed Mir Shah Danish & Najib Rahman Sabory & Narayanan K & Paras Mandal, 2019. "A Recap of Voltage Stability Indices in the Past Three Decades," Energies, MDPI, vol. 12(8), pages 1-18, April.
    2. Abidhan Bardhan & Raushan Kumar Singh & Sufyan Ghani & Gerasimos Konstantakatos & Panagiotis G. Asteris, 2023. "Modelling Soil Compaction Parameters Using an Enhanced Hybrid Intelligence Paradigm of ANFIS and Improved Grey Wolf Optimiser," Mathematics, MDPI, vol. 11(14), pages 1-23, July.
    3. Nikolina Ljepava & Aleksandar Jovanović & Aleksandar Aleksić, 2023. "Industrial Application of the ANFIS Algorithm—Customer Satisfaction Assessment in the Dairy Industry," Mathematics, MDPI, vol. 11(19), pages 1-22, October.
    4. Abdelhady Ramadan & Salah Kamel & I. Hamdan & Ahmed M. Agwa, 2022. "A Novel Intelligent ANFIS for the Dynamic Model of Photovoltaic Systems," Mathematics, MDPI, vol. 10(8), pages 1-14, April.
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