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Journals / International Journal of Energy and Smart Grid / 2025 / Volume: 10 Issue: 2

DEEP LEARNING BASED PREDICTION OF POWER QUALITY DISTURBANCES IN DISTRIBUTION NETWORKS

Metin Akdeniz(BANDIRMA ONYEDİ EYLÜL ÜNİVERSİTESİ)0000-0002-5336-0128
İlyas Özer(BANDIRMA ONYEDI EYLUL UNIVERSITY)0000-0003-2112-5497
Serhat Berat Efe(BANDIRMA ONYEDİ EYLÜL ÜNİVERSİTESİ)0000-0001-6076-4166
Pages
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Publication Date
2025-11-30
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

These findings indicate that the proposed approach provides a reliable and practical solution for predicting harmonic distortions and can effectively support decision-making mechanisms aimed at enhancing power quality in distribution systems.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The main objective of this study is to predict harmonic distortions in a power distribution system using real-world active and reactive power data.
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Key Findings & Data:Unlike conventional methods, the GRU model demonstrates successful performance by shortening training duration and increasing prediction accuracy.
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Conclusion & Impact:These findings indicate that the proposed approach provides a reliable and practical solution for predicting harmonic distortions and can effectively support decision-making mechanisms aimed at enhancing power quality in distribution systems.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:electric power systems · deep learning · power quality

Abstract

The main objective of this study is to predict harmonic distortions in a power distribution system using real-world active and reactive power data. To achieve this, a Gated Recurrent Unit (GRU)-based artificial intelligence algorithm was employed, which is particularly effective in modeling the dynamic nature of time series. Unlike conventional methods, the GRU model demonstrates successful performance by shortening training duration and increasing prediction accuracy. The prediction results yielded promising error metrics, with mean absolute error (MAE) values of 0.5200, 0.5330, and 0.5771; mean absolute percentage error (MAPE) values of 7.52%, 7.55%, and 7.72%; and root mean square error (RMSE) values of 0.7014, 0.7231, and 0.7848 for the THD_I1, THD_I2, and THD_I3 indices, respectively. These findings indicate that the proposed approach provides a reliable and practical solution for predicting harmonic distortions and can effectively support decision-making mechanisms aimed at enhancing power quality in distribution systems.

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Cite This Article

Akdeniz, M., Özer, İ., Efe, S. B. (2025). DEEP LEARNING BASED PREDICTION OF POWER QUALITY DISTURBANCES IN DISTRIBUTION NETWORKS. International Journal of Energy and Smart Grid, Volume: 10 Issue: 2. https://doi.org/10.55088/ijesg.1757249

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