Performance Analysis of Artificial Intelligence Models for Classification of Transmission Line Losses

Abraham O. Amole (), Oluwagbemiga E. Ajiboye, Stephen Oladipo, Ignatius K. Okakwu, Ibrahim A. Giwa and Olamide O. Olusanya
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Abraham O. Amole: Department of Electrical, Electronics and Telecommunication Engineering, Bells University of Technology, Ota 112104, Ogun State, Nigeria
Oluwagbemiga E. Ajiboye: Department of Electrical, Electronics and Telecommunication Engineering, Bells University of Technology, Ota 112104, Ogun State, Nigeria
Stephen Oladipo: Department of Electrical and Electronics Engineering, University of Johannesburg, Johannesburg 2006, South Africa
Ignatius K. Okakwu: Department of Electrical and Electronics Engineering, Olabisi Onabanjo University, Ago-Iwoye 2001, Ogun State, Nigeria
Ibrahim A. Giwa: Department of Electrical, Electronics and Telecommunication Engineering, Bells University of Technology, Ota 112104, Ogun State, Nigeria
Olamide O. Olusanya: Department of Computer Engineering, Bells University of Technology, Ota 112104, Ogun State, Nigeria

Energies, 2025, vol. 18, issue 11, 1-29

Abstract: Conventional approaches to analyzing power losses in electrical transmission networks have largely emphasized generic power loss minimization through the integration of loss-reducing devices such as shunt capacitors. However, achieving optimal power loss minimization requires a more data-driven and intelligent approach that transcends traditional methods. This study presents a novel classification-based methodology for detecting and analyzing transmission line losses using real-world data from the Ikorodu–Sagamu 132 kV double-circuit line in Nigeria, selected for its dense concentration of high-voltage consumers. Twelve (12) transmission lines were examined, and the collected data were subjected to comprehensive preprocessing, feature engineering, and modeling. The classification capabilities of advanced deep learning models—Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (BiLSTM), and Gated Recurrent Unit (GRU)—were explored through six experimental scenarios: LSTM, LSTM with Attention Mechanism (LSTM-AM), BiLSTM, GRU, LSTM-BiLSTM, and LSTM-GRU. These models were implemented using the Python programming environment and evaluated using standard performance metrics, including accuracy, precision, recall, F1-score, support, and confusion matrices. Statistical analysis revealed significant variability in transmission losses, particularly in lines such as I1, Ps, Ogy, and ED, which exhibited high standard deviations. The LSTM-AM model achieved the highest classification accuracy of 83.84%, outperforming both standalone and hybrid models. In contrast, BiLSTM yielded the lowest performance. The findings demonstrate that while standalone models like GRU and LSTM are effective, the incorporation of attention mechanisms into LSTM architecture enhances classification accuracy. This study provides a compelling case for employing deep learning-based classification techniques in intelligent power loss classification across transmission networks. It also supports the realization of SDG 7 by aiming to provide access to reliable, affordable, and sustainable energy for all.

Keywords: losses; transmission; power; classification models; confusion matrix (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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