IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i22p15448-d979242.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/22/15448/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/22/15448/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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).
    2. 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.
    3. 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.
    4. Songkai Liu & Ruoyuan Shi & Yuehua Huang & Xin Li & Zhenhua Li & Lingyun Wang & Dan Mao & Lihuang Liu & Siyang Liao & Menglin Zhang & Guanghui Yan & Lian Liu, 2021. "A Data-Driven and Data-Based Framework for Online Voltage Stability Assessment Using Partial Mutual Information and Iterated Random Forest," Energies, MDPI, vol. 14(3), pages 1-16, January.
    5. 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.
    6. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Rong-Ceng Leou, 2022. "A Substation-Based Optimal Photovoltaic Generation System Placement Considering Multiple Evaluation Indices," Energies, MDPI, vol. 15(15), pages 1-18, August.
    2. Li, Yang & Wang, Bin & Yang, Zhen & Li, Jiazheng & Chen, Chen, 2022. "Hierarchical stochastic scheduling of multi-community integrated energy systems in uncertain environments via Stackelberg game," Applied Energy, Elsevier, vol. 308(C).
    3. Gubbala Venkata Naga Lakshmi & Askani Jaya Laxmi & Venkataramana Veeramsetty & Surender Reddy Salkuti, 2022. "Optimal Placement of Distributed Generation Based on Power Quality Improvement Using Self-Adaptive Lévy Flight Jaya Algorithm," Clean Technol., MDPI, vol. 4(4), pages 1-13, November.
    4. Songkai Liu & Dan Mao & Tianliang Xue & Fei Tang & Xin Li & Lihuang Liu & Ruoyuan Shi & Siyang Liao & Menglin Zhang, 2021. "A Data-Driven Approach for Online Inter-Area Oscillatory Stability Assessment of Power Systems Based on Random Bits Forest Considering Feature Redundancy," Energies, MDPI, vol. 14(6), pages 1-20, March.
    5. Manuel Dario Jaramillo & Diego Francisco Carrión & Jorge Paul Muñoz, 2023. "A Novel Methodology for Strengthening Stability in Electrical Power Systems by Considering Fast Voltage Stability Index under N − 1 Scenarios," Energies, MDPI, vol. 16(8), pages 1-23, April.
    6. Mohamed Els. S. Abdelwareth & Dedet Candra Riawan & Chow Chompoo-inwai, 2023. "Optimum Generated Power for a Hybrid DG/PV/Battery Radial Network Using Meta-Heuristic Algorithms Based DG Allocation," Sustainability, MDPI, vol. 15(13), pages 1-25, July.
    7. Xiaosheng Peng & Kai Cheng & Jianxun Lang & Zuowei Zhang & Tao Cai & Shanxu Duan, 2021. "Short-Term Wind Power Prediction for Wind Farm Clusters Based on SFFS Feature Selection and BLSTM Deep Learning," Energies, MDPI, vol. 14(7), pages 1-18, March.
    8. Habib Ur Rehman & Arif Hussain & Waseem Haider & Sayyed Ahmad Ali & Syed Ali Abbas Kazmi & Muhammad Huzaifa, 2023. "Optimal Planning of Solar Photovoltaic (PV) and Wind-Based DGs for Achieving Techno-Economic Objectives across Various Load Models," Energies, MDPI, vol. 16(5), pages 1-38, March.
    9. Ifedayo Oladeji & Ramon Zamora & Tek Tjing Lie, 2021. "An Online Security Prediction and Control Framework for Modern Power Grids," Energies, MDPI, vol. 14(20), pages 1-27, October.
    10. Yuko Hirase & Yuki Ohara & Naoya Matsuura & Takeaki Yamazaki, 2021. "Dynamics Analysis Using Koopman Mode Decomposition of a Microgrid Including Virtual Synchronous Generator-Based Inverters," Energies, MDPI, vol. 14(15), pages 1-20, July.
    11. Xie, Haonan & Jiang, Meihui & Zhang, Dongdong & Goh, Hui Hwang & Ahmad, Tanveer & Liu, Hui & Liu, Tianhao & Wang, Shuyao & Wu, Thomas, 2023. "IntelliSense technology in the new power systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 177(C).
    12. Mahmoud Aref & Vladislav Oboskalov & Adel El-Shahat & Almoataz Y. Abdelaziz, 2023. "Modified Analytical Technique for Multi-Objective Optimal Placement of High-Level Renewable Energy Penetration Connected to Egyptian Power System," Mathematics, MDPI, vol. 11(4), pages 1-31, February.
    13. Minal S. Salunke & Ramesh S. Karnik & Angadi B. Raju & Vinayak N. Gaitonde, 2024. "Analysis of Transmission System Stability with Distribution Generation Supplying Induction Motor Loads," Mathematics, MDPI, vol. 12(1), pages 1-29, January.
    14. Chao Yu & Yang Zhou & Xiaolong Cui, 2023. "A Client-Cloud-Chain Data Annotation System of Internet of Things for Semi-Supervised Missing Data," Mathematics, MDPI, vol. 11(21), pages 1-18, November.
    15. Zixia Yuan & Guojiang Xiong & Xiaofan Fu, 2022. "Artificial Neural Network for Fault Diagnosis of Solar Photovoltaic Systems: A Survey," Energies, MDPI, vol. 15(22), pages 1-18, November.
    16. Shi, Zhongtuo & Yao, Wei & Zhao, Yifan & Ai, Xiaomeng & Wen, Jinyu & Cheng, Shijie, 2024. "Two-stage weakly supervised learning to mitigate label noise for intelligent identification of power system dominant instability mode," Applied Energy, Elsevier, vol. 359(C).
    17. Alok Nath Yadav & Kirti Pal, 2022. "Demand side maximum loading limit ranking based on composite index using ANN," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(3), pages 1419-1429, June.
    18. Li, Yang & Wang, Ruinong & Li, Yuanzheng & Zhang, Meng & Long, Chao, 2023. "Wind power forecasting considering data privacy protection: A federated deep reinforcement learning approach," Applied Energy, Elsevier, vol. 329(C).
    19. Li, Yang & Han, Meng & Shahidehpour, Mohammad & Li, Jiazheng & Long, Chao, 2023. "Data-driven distributionally robust scheduling of community integrated energy systems with uncertain renewable generations considering integrated demand response," Applied Energy, Elsevier, vol. 335(C).
    20. Tianhao Song & Xiaoqing Han & Baifu Zhang, 2021. "Multi-Time-Scale Optimal Scheduling in Active Distribution Network with Voltage Stability Constraints," Energies, MDPI, vol. 14(21), pages 1-20, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:14:y:2022:i:22:p:15448-:d:979242. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.