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Power System Voltage Stability Margin Estimation Using Adaptive Neuro-Fuzzy Inference System Enhanced with Particle Swarm Optimization

Author

Listed:
  • Oludamilare Bode Adewuyi

    (Department of Electrical Engineering, University of Cape Town, Cape Town 7701, South Africa)

  • Komla A. Folly

    (Department of Electrical Engineering, University of Cape Town, Cape Town 7701, South Africa)

  • David T. O. Oyedokun

    (Department of Electrical Engineering, University of Cape Town, Cape Town 7701, South Africa)

  • Emmanuel Idowu Ogunwole

    (Department of Electrical, Electronic and Computer Engineering, Cape Peninsula University of Technology, Bellville Campus, Cape Town 7535, South Africa)

Abstract

In the current era of e-mobility and for the planning of sustainable grid infrastructures, developing new efficient tools for real-time grid performance monitoring is essential. Thus, this paper presents the prediction of the voltage stability margin (VSM) of power systems by the critical boundary index (CBI) approach using the machine learning technique. Prediction models are based on an adaptive neuro-fuzzy inference system (ANFIS) and its enhanced model with particle swarm optimization (PSO). Standalone ANFIS and PSO-ANFIS models are implemented using the fuzzy ‘c-means’ clustering method (FCM) to predict the expected values of CBI as a veritable tool for measuring the VSM of power systems under different loading conditions. Six vital power system parameters, including the transmission line and bus parameters, the power injection, and the system voltage derived from load flow analysis, are used as the ANFIS model implementation input. The performances of the two ANFIS models on the standard IEEE 30-bus and the Nigerian 28-bus systems are evaluated using error and regression analysis metrics. The performance metrics are the root mean square error ( R M S E ), mean absolute percentage error ( M A P E ), and Pearson correlation coefficient ( R ) analyses. For the IEEE 30-bus system, R M S E is estimated to be 0.5833 for standalone ANFIS and 0.1795 for PSO-ANFIS; M A P E is estimated to be 13.6002% for ANFIS and 5.5876% for PSO-ANFIS; and R is estimated to be 0.9518 and 0.9829 for ANFIS and PSO-ANFIS, respectively. For the NIGERIAN 28-bus system, the R M S E values for ANFIS and PSO-ANFIS are 5.5024 and 2.3247, respectively; M A P E is 19.9504% and 8.1705% for both ANFIS and PSO-ANFIS variants, respectively, and the R is estimated to be 0.9277 for ANFIS and 0.9519 for ANFIS-PSO, respectively. Thus, the PSO-ANFIS model shows a superior performance for both test cases, as indicated by the percentage reduction in prediction error, although at the cost of a higher simulation time.

Suggested Citation

  • Oludamilare Bode Adewuyi & Komla A. Folly & David T. O. Oyedokun & Emmanuel Idowu Ogunwole, 2022. "Power System Voltage Stability Margin Estimation Using Adaptive Neuro-Fuzzy Inference System Enhanced with Particle Swarm Optimization," Sustainability, MDPI, vol. 14(22), pages 1-17, November.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:22:p:15448-:d:979242
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    References listed on IDEAS

    as
    1. Mohammed Amroune & Ismail Musirin & Tarek Bouktir & Muhammad Murtadha Othman, 2017. "The Amalgamation of SVR and ANFIS Models with Synchronized Phasor Measurements for On-Line Voltage Stability Assessment," Energies, MDPI, vol. 10(11), pages 1-18, October.
    2. Li, Yang & Zhang, Meng & Chen, Chen, 2022. "A Deep-Learning intelligent system incorporating data augmentation for Short-Term voltage stability assessment of power systems," Applied Energy, Elsevier, vol. 308(C).
    3. Walter M. Villa-Acevedo & Jesús M. López-Lezama & Delia G. Colomé, 2020. "Voltage Stability Margin Index Estimation Using a Hybrid Kernel Extreme Learning Machine Approach," Energies, MDPI, vol. 13(4), pages 1-19, February.
    4. 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.
    5. Panapakidis, Ioannis P. & Dagoumas, Athanasios S., 2017. "Day-ahead natural gas demand forecasting based on the combination of wavelet transform and ANFIS/genetic algorithm/neural network model," Energy, Elsevier, vol. 118(C), pages 231-245.
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    7. Oludamilare Bode Adewuyi & Ayooluwa Peter Adeagbo & Isaiah Gbadegesin Adebayo & Harun Or Rashid Howlader & Yanxia Sun, 2021. "Modified Analytical Approach for PV-DGs Integration into a Radial Distribution Network Considering Loss Sensitivity and Voltage Stability," Energies, MDPI, vol. 14(22), pages 1-20, November.
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