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Grid Distribution Fault Occurrence and Remedial Measures Prediction/Forecasting through Different Deep Learning Neural Networks by Using Real Time Data from Tabuk City Power Grid

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  • Fahad M. Almasoudi

    (Department of Electrical Engineering, Faculty of Engineering, University of Tabuk, Tabuk 47913, Saudi Arabia)

Abstract

Modern societies need a constant and stable electrical supply. After relying primarily on formal mathematical modeling from operations research, control theory, and numerical analysis, power systems analysis has changed its attention toward AI prediction/forecasting tools. AI techniques have helped fix power system issues in generation, transmission, distribution, scheduling and forecasting, etc. These strategies may assist today’s large power systems which have added more interconnections to meet growing load demands. They make it simple for them to do difficult duties. Identification of problems and problem management have always necessitated the use of labor. These operations are made more sophisticated and data-intensive due to the variety and growth of the networks involved. In light of all of this, the automation of network administration is absolutely necessary. AI has the potential to improve the problem-solving and deductive reasoning approaches used in fault management. This study implements a variety of artificial intelligence and deep learning approaches in order to foresee and predict the corrective measures that will be conducted in response to faults that occur inside the power distribution network of the Grid station in Tabuk city with regard to users. The Tabuk grid station is the source of the data that was gathered for this purpose; it includes a list of defects categorization, actions and remedies that were implemented to overcome these faults, as well as the number of regular and VIP users from 2017 to 2022. Deep learning, the most advanced method of learning used by artificial intelligence, is continuing to make significant strides in a variety of domain areas, including prediction. This study found that the main predictors of remedial measures against the fault occurring in the power systems are the number of customers affected and the actual cause of the fault. Consequently, the deep learning regression model, i.e., Gated Recurrent Unit (GRU), achieved the best performance among the three, which yielded an accuracy of 92.13%, mean absolute error (MAE) loss of 0.37%, and root mean square error (RMSE) loss of 0.39% while the simple RNN model’s performance is not up to the mark with an accuracy of 89.21%, mean absolute error (MAE) loss of 0.45% and root mean square error (RMSE) loss of 0.34%. Significance of the research is to provide the maximum benefit to the customers and the company by using different AI techniques.

Suggested Citation

  • Fahad M. Almasoudi, 2023. "Grid Distribution Fault Occurrence and Remedial Measures Prediction/Forecasting through Different Deep Learning Neural Networks by Using Real Time Data from Tabuk City Power Grid," Energies, MDPI, vol. 16(3), pages 1-20, January.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:3:p:1026-:d:1038684
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    References listed on IDEAS

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    1. Zhao Luo & Zhiyuan Zhang & Xu Yan & Jinghui Qin & Zhendong Zhu & Hao Wang & Zeyong Gao, 2020. "Dissolved Gas Analysis of Insulating Oil in Electric Power Transformers: A Case Study Using SDAE-LSTM," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-10, November.
    2. Wani, Shufali Ashraf & Rana, Ankur Singh & Sohail, Shiraz & Rahman, Obaidur & Parveen, Shaheen & Khan, Shakeb A., 2021. "Advances in DGA based condition monitoring of transformers: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    3. Bing Zeng & Jiang Guo & Wenqiang Zhu & Zhihuai Xiao & Fang Yuan & Sixu Huang, 2019. "A Transformer Fault Diagnosis Model Based On Hybrid Grey Wolf Optimizer and LS-SVM," Energies, MDPI, vol. 12(21), pages 1-18, November.
    4. Yiyi Zhang & Yuxuan Wang & Xianhao Fan & Wei Zhang & Ran Zhuo & Jian Hao & Zhen Shi, 2020. "An Integrated Model for Transformer Fault Diagnosis to Improve Sample Classification near Decision Boundary of Support Vector Machine," Energies, MDPI, vol. 13(24), pages 1-15, December.
    5. Naser Hossein Motlagh & Mahsa Mohammadrezaei & Julian Hunt & Behnam Zakeri, 2020. "Internet of Things (IoT) and the Energy Sector," Energies, MDPI, vol. 13(2), pages 1-27, January.
    6. Enwen Li & Linong Wang & Bin Song & Siliang Jian, 2018. "Improved Fuzzy C-Means Clustering for Transformer Fault Diagnosis Using Dissolved Gas Analysis Data," Energies, MDPI, vol. 11(9), pages 1-17, September.
    7. Yan Wang & Liguo Zhang, 2017. "A Combined Fault Diagnosis Method for Power Transformer in Big Data Environment," Mathematical Problems in Engineering, Hindawi, vol. 2017, pages 1-6, May.
    8. Chun Yan & Meixuan Li & Wei Liu, 2019. "Transformer Fault Diagnosis Based on BP-Adaboost and PNN Series Connection," Mathematical Problems in Engineering, Hindawi, vol. 2019, pages 1-10, July.
    9. Jiake Fang & Hanbo Zheng & Jiefeng Liu & Junhui Zhao & Yiyi Zhang & Ke Wang, 2018. "A Transformer Fault Diagnosis Model Using an Optimal Hybrid Dissolved Gas Analysis Features Subset with Improved Social Group Optimization-Support Vector Machine Classifier," Energies, MDPI, vol. 11(8), pages 1-18, July.
    10. Yichen Zhou & Xiaohui Yang & Lingyu Tao & Li Yang, 2021. "Transformer Fault Diagnosis Model Based on Improved Gray Wolf Optimizer and Probabilistic Neural Network," Energies, MDPI, vol. 14(11), pages 1-21, May.
    11. Wan Chen & Baolian Liu & Muhammad Shahzad Nazir & Ahmed N. Abdalla & Mohamed A. Mohamed & Zujun Ding & Muhammad Shoaib Bhutta & Mehr Gul, 2022. "An Energy Storage Assessment: Using Frequency Modulation Approach to Capture Optimal Coordination," Sustainability, MDPI, vol. 14(14), pages 1-15, July.
    12. Mohammad Shoaib Shahriar & Ibrahim Omar Habiballah & Huthaifa Hussein, 2018. "Optimization of Phasor Measurement Unit (PMU) Placement in Supervisory Control and Data Acquisition (SCADA)-Based Power System for Better State-Estimation Performance," Energies, MDPI, vol. 11(3), pages 1-15, March.
    13. Youcef Benmahamed & Omar Kherif & Madjid Teguar & Ahmed Boubakeur & Sherif S. M. Ghoneim, 2021. "Accuracy Improvement of Transformer Faults Diagnostic Based on DGA Data Using SVM-BA Classifier," Energies, MDPI, vol. 14(10), pages 1-17, May.
    14. Hazlee Azil Illias & Wee Zhao Liang, 2018. "Identification of transformer fault based on dissolved gas analysis using hybrid support vector machine-modified evolutionary particle swarm optimisation," PLOS ONE, Public Library of Science, vol. 13(1), pages 1-15, January.
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    1. Fahad M. Almasoudi, 2023. "Enhancing Power Grid Resilience through Real-Time Fault Detection and Remediation Using Advanced Hybrid Machine Learning Models," Sustainability, MDPI, vol. 15(10), pages 1-21, May.

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