Fault type and fault location detection in transmission lines with 6-convolutional layered CNN

• Autorzy: Gümüs Bilal , Kılıç Heybet , Haydaroglu Cem , Butakın Ulvi Yusuf

Identyfikatory

DOI

10.24425/bpasts.2024.151047

• Języki publikacji: EN

Abstrakty

EN

In this article, we propose a data-driven method for short-circuit fault detection in transmission lines that exploits the capabilities of convolutional neural networks (CNNs). CNNs, a class of deep feedforward neural networks, can autonomously detect different features from data, eliminating the need for manual intervention. To mitigate the effects of noise and increase network robustness, we present a CNN architecture with six convolutional layers. The study uses a single busbar power system model developed with the PSCAD simulation program to evaluate the performance of the proposed method. The proposed CNN method is also compared with machine learning methods such as LSTM, SVM and ELM. Our results show a high success rate of 98.4% across all fault impedances, confirming the effectiveness of the proposed CNN methods in accurately detecting short-circuit faults based on current and voltage measurements.

Słowa kluczowe

EN

PSCAD; CNN; convolutional neural networks; fault detection; 6-convolutional layered CNN; style

PL

PSCAD; CNN; wykrywanie błędów; styl; sieć neuronowa splotowa; 6-splotowa sieć neuronowa

• Wydawca: Polska Akademia Nauk, Wydział IV Nauk Technicznych
• Czasopismo: Bulletin of the Polish Academy of Sciences. Technical Sciences
• Rocznik: 2024
• Tom: Vol. 72, nr 5
• Strony: art. no. e151047
• Opis fizyczny: Bibliogr. 35 poz., rys., tab.

Twórcy

autor

Gümüs Bilal

• Electrical and Electronics Engineering Department, Faculty of Engineering, Dicle University, Diyarbakır 21280, Turkey

• https://orcid.org/0000-0003-4665-5339

autor

Kılıç Heybet

• Electric Power and Energy Department, Dicle University, Diyarbakır 21280, Turkey

• https://orcid.org/0000-0002-6119-0886

autor

Haydaroglu Cem

• Electrical and Electronics Engineering Department, Faculty of Engineering, Dicle University, Diyarbakır 21280, Turkey

• https://orcid.org/0000-0003-0830-5530

autor

Butakın Ulvi Yusuf

• SCADA System and DMS Manager, Dicle Electricity Distribution Inc., Diyarbakır, Turkey

• https://orcid.org/0009-0007-4931-5985

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