Prediction of Equipment Failure Rates in Power Distribution Networks based on Machine-learning Method

Majlesi Journal of Electrical Engineering

مقاله 10، دوره 17، شماره 3، آذر 2023، صفحه 77-82   

نوع مقاله: Reseach Article

شناسه دیجیتال (DOI): 10.30486/mjee.2023.1994835.1238

نویسندگان

Rita Jamasheva^{* 1}؛ Noor Hanoon Haroon²؛ Ahmed Read Al-Tameemi³؛ Israa Alhani⁴؛ Ali Murad Khudadad⁵؛ Bahira Abdulrazzaq Mohammed⁶؛ Ali H. O. Al Mansor⁷؛ Mustafa Asaad Hussein⁸

¹Department of Automation and Robotics, Almaty Technological University, Almaty, Kazakhstan

²Information and Communication Technology Research Group, Scientific Research Center, Al-Ayen University, Thi-Qar, Iraq.

³Department of Medical Laboratory Technics, Al-Nisour University College, Baghdad, Iraq.

⁴Department of Medical Laboratory Technics, Mazaya University College, Dhi Qar, Iraq

⁵Department of Medical Laboratory Technics, Al-Esraa University College, Baghdad, Iraq

⁶Department of Medical Engineering, Al-Hadi University College, Baghdad 10011 Iraq.

⁷Department of Medical Laboratory Technics, Al-Zahrawi University College, Karbala, Iraq

⁸National University of Science and Technology, Dhi Qar, Iraq

چکیده

This paper explores the application of a machine learning approach to predict equipment failure rates in power distribution networks, motivated by the significant impact of power outages on citizens' daily lives and the economy. In this research, data on equipment failure rates and maintenance records were collected from power distribution networks in Baghdad, Iraq. The collected data underwent preprocessing, and features were extracted to train Adaptive Neuro-Fuzzy Inference System (ANFIS) and Periodic Autoregressive Moving Average (PARMA) time series models. To initiate the project, information regarding blackouts that occurred between January 2018 and December 2021 was retrieved from the database. The RMSE index results for the PARMA time series and ANFIS model are 3.518 and 2.264, respectively, demonstrating the superior performance of the ANFIS model in predicting equipment failure rates and its potential for future predictions. This study highlights the ANFIS model's capacity to anticipate equipment failure rates, potentially enhancing maintenance efficiency and reducing power outages in Baghdad. The error mean square was employed to evaluate the proposed models' error rate.

کلیدواژه‌ها

Failure Rates؛ Machine Learning؛ Adaptive Neuro-Fuzzy Inference System model؛ Periodic Autoregressive Moving Average

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