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Journals / Politeknik Dergisi / 2026 / Cilt: 29 Sayı: 3

Convolutional Neural Network Based Real-Time Power Quality Disturbance Detection and Classification

Evrişimsel Sinir Ağı Tabanlı Gerçek Zamanlı Güç Kalitesi Bozulması Tespiti ve Sınıflandırması

Mehmet Gök(Kahramanmaraş İstiklal Üniversitesi)0000-0003-1656-5770
Muhammed Ali Koşan(Kahramanmaraş İstiklal Üniversitesi)0000-0002-1422-6006
Pages
1–14
Publication Date
2026-03-29
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AI Summary & Key Takeaways

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

The application is ported to Raspberry Pi 5 and Jetson Orin Nano single board computers to reveal the embedded system performance of the presented system.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Increased instalment of power electronic devices and alternative energy sources to grids leads to increase in power quality problems representing a threat to power-sensitive loads and electronically controlled equipment.
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Key Findings & Data:Accurate examination of anomalies in power quality in real-time is required to decide which preventive measure to take.
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Conclusion & Impact:The application is ported to Raspberry Pi 5 and Jetson Orin Nano single board computers to reveal the embedded system performance of the presented system.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Evrişimli sinir ağı · elektrik anomalisi · anomali tespiti

Abstract

Increased instalment of power electronic devices and alternative energy sources to grids leads to increase in power quality problems representing a threat to power-sensitive loads and electronically controlled equipment. Dealing with power quality problems is an important concern viewed from life-cycle of equipment and facilitating a stable manufacturing process in industrial plants. Accurate examination of anomalies in power quality in real-time is required to decide which preventive measure to take. There are numerous studies employing machine learning and optimization methods to detect and classify the power quality events in literature. However, only a few dwell on real-time performance of proposed approaches. This study presents a closed-loop approach based on one-dimensional convolutional neural network model to capture power quality events in time domain requiring no human intervention from training to inference. An application, accompanied by a data acquisition tool, is developed to demonstrate the performance of the proposed approach using the Tensorflow C++ library. The application is ported to Raspberry Pi 5 and Jetson Orin Nano single board computers to reveal the embedded system performance of the presented system.

Özet

Şebekelere güç elektroniği cihazlarının ve alternatif enerji kaynaklarının artan şekilde entegre edilmesi, güç kalitesi sorunlarında artışa yol açarak, güce duyarlı yükler ve elektronik olarak kontrol edilen ekipmanlar için bir tehdit oluşturmaktadır. Güç kalitesi sorunlarıyla başa çıkmak, ekipmanın yaşam döngüsü açısından ve endüstriyel tesislerde istikrarlı bir üretim sürecini kolaylaştırmak açısından önemli bir konudur. Hangi önleyici tedbirin alınacağına karar vermek için güç kalitesindeki anormalliklerin gerçek zamanlı olarak doğru bir şekilde incelenmesi gerekmektedir. Literatürde, güç kalitesi olaylarını tespit etmek ve sınıflandırmak için makine öğrenimi ve optimizasyon yöntemlerini kullanan çok sayıda çalışma bulunmaktadır. Bununla birlikte, önerilen yaklaşımların gerçek zamanlı performansına odaklanan çalışmaların sayısı azdır. Bu çalışma, eğitimden çıkarıma kadar insan müdahalesi gerektirmeyen, zaman alanında güç kalitesi olaylarını yakalamak için tek boyutlu evrimsel sinir ağı modeline dayalı kapalı döngü bir yaklaşım sunmaktadır. Önerilen yaklaşımın performansını göstermek için Tensorflow C++ kütüphanesi kullanılarak bir veri toplama aracıyla birlikte bir uygulama geliştirilmiştir. Uygulama, sunulan sistemin gömülü sistem performansını ortaya koymak için Raspberry Pi 5 ve Jetson Orin Nano tek kartlı bilgisayarlara taşınmıştır.

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Cite This Article

Gök, M., Koşan, M. A. (2026). Convolutional Neural Network Based Real-Time Power Quality Disturbance Detection and Classification. Politeknik Dergisi, Cilt: 29 Sayı: 3, 1–14. https://doi.org/10.2339/politeknik.1791727

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