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An optimal deep belief with buffalo optimization algorithm for fault detection and power loss in grid-connected system

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Abstract

The recent increase in photovoltaic (PV) power generation and its extensive use worldwide has led to the development of complex distributed generation systems, which has caused an increase in PV faults. These defects lead to considerable power losses, significantly impacting the reliability and performance of the PV system. Several approaches have been implemented, but an accurate solution has not been found. Therefore, an optimal Deep Belief with Buffalo Optimization (DB-BO) algorithm is applied in the grid-connected system for detecting faults and regulating its classes. Moreover, principal component analysis is used to analyze the power loss issues, and linear discriminant analysis is utilized to mitigate the voltage deviation issues. MATLAB or Simulink is used as the implementation process, and simulation outcomes are compared with recent conventional models. It has been revealed that the developed DB-BO algorithm has reduced the power loss to 3.4 mW. Also, total harmonic distortion (THD) is improved compared to the existing security methods. Thus, the efficiency of the model that was built has been proven by getting the best results in accuracy, total harmonic distortion (THD), and power loss. The computation time of the proposed model (0.238 s) is compared with metaheuristic algorithms such as CSE (0.315629 s) and GWA (3.636 s).

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All datasets are available at https://www.kaggle.com/datasets.

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Funding

This research work was funded by Institutional Fund Projects under grant no. (IFPIP: 1165-135-1443). The authors gratefully acknowledge the technical and financial support provided by the Ministry of Education and King Abdulaziz University, DSR, Jeddah, Saudi Arabia.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, King Abdulaziz University, 21589, Jeddah, Kingdom of Saudi Arabia

    Md. Mottahir Alam & Navin Kasim

  2. Advance Power Electronics Research Lab, Department of Electrical Engineering, Jamia Millia Islamia, New Delhi, 110025, India

    Ahteshamul Haque

  3. Department of Physics, College of Science, Jazan University, P.O. Box. 114, 45142, Jazan, Kingdom of Saudi Arabia

    Jabir Hakami

  4. Computer Science Department, FCIT, King Abdulazi University, 21589, Jeddah, Kingdom of Saudi Arabia

    Asif Irshad Khan

  5. Aerospace Engineering Department, King Abdulaziz University, 21589, Jeddah, Kingdom of Saudi Arabia

    Amjad Ali Pasha

  6. Civil Engineering Department, College of Engineering, King Khalid University, 61421, Abha, Asir, Kingdom of Saudi Arabia

    Saiful Islam

  7. Department of Civil Engineering, Galgotia College of Engineering, Knowledge Park I, Greater Noida, Uttar Pradesh, 201310, India

    Mohammad Amir Khan

  8. Tecnologico de Monterrey, Escuela de Ingeniería y Ciencias, Puebla, Mexico

    Mostafa Hajiaghaei-Keshteli

  9. Interdisciplinary Research Center for Renewable Energy and Power Systems (IRC-REPS), Research Institute, King Fahd University of Petroleum & Minerals, 31261, Dhahran, Kingdom of Saudi Arabia

    Sasan Zahmatkesh & Kashif Irshad

Authors
  1. Md. Mottahir Alam
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  2. Ahteshamul Haque
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  3. Jabir Hakami
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  5. Amjad Ali Pasha
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  6. Navin Kasim
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  7. Saiful Islam
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  8. Mohammad Amir Khan
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  10. Mostafa Hajiaghaei-Keshteli
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Corresponding authors

Correspondence to Md. Mottahir Alam, Mohammad Amir Khan or Mostafa Hajiaghaei-Keshteli.

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Alam, M.M., Haque, A., Hakami, J. et al. An optimal deep belief with buffalo optimization algorithm for fault detection and power loss in grid-connected system. Soft Comput 28, 2577–2591 (2024). https://doi.org/10.1007/s00500-023-08558-2

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