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A method for determining the location and type of fault in transmission network using neural networks and power quality monitors

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In this paper, a procedure for determining the location of a fault on a power line using neural networks is proposed. Specifically, the procedure involves four stages (three of which employ neural networks): gathering voltage input data from power quality monitors via simulation, classifying the fault type, detecting the faulted line, and determining the fault position on the power line. The IEEE 39 bus test system was used to develop and test the mentioned model. Input voltages are obtained using DigSILENT PowerFactory software in which a set of three-phase and single-phase short circuits are simulated. For the next steps of the method, voltages from all buses are not used, but only voltages from optimally placed power quality monitors on 12 buses in the IEEE 39 bus test system. In the second step, the first neural network is employed in order to classify the fault type – single-phase or three-phase. In the third stage, another neural network is used to determine the faulted line and in the fourth stage, the last neural network is developed to determine the fault position on the faulted line.
Rocznik
Strony
art. no. 187166
Opis fizyczny
Bibliogr. 18 poz., rys., tab., wykr.
Twórcy
  • Croatian Armed Forces, Croatia
  • Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, Croatia., Croatia
  • Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, Croatia., Croatia
  • Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, Croatia., Croatia
Bibliografia
  • 1. Batarseh F, Yang R, editors. Data democracy: at the nexus of artificial intelligence, software development, and knowledge engineering. London, United Kingdom: Academic Press, an Imprint of Elsevier; 2020. 241 p. https://doi.org/10.1016/C2018-0-04003-7
  • 2. Chen Y, Li L, Li W, Guo Q, Du Z, Xu Z. Al computing systems: an application driven perspective. London: Morgan Kaufmann; 2024. 427 p.
  • 3. Dangeti P. Statistics for machine learning: techniques for exploring supervised, unsupervised, and reinforcement learning models with Python and R. Birmingham, UK: Packt Publishing; 2017. 426 p.
  • 4. Fahim SR, Sarker Y, Sarker SK, Sheikh MdRI, Das SK. Self attention convolutional neural network with time series imaging based feature extraction for transmission line fault detection and classification. Electr Power Syst Res. 2020 Oct 1;187:106437. doi:10.1016/j.epsr.2020.106437
  • 5. Gao F, Li B, Chen L, Shang Z, Wei X, He C. A softmax classifier for high-precision classification of ultrasonic similar signals. Ultrasonics. 2021 Apr;112:106344. doi:10.1016/j.ultras.2020.106344
  • 6. Han J, Miao S, Li Y, Yang W, Yin H. Fault Diagnosis of Power Systems Using Visualized Similarity Images and Improved Convolution Neural Networks. IEEE Syst J. 2022 Mar;16(1):185–96. doi:10.1109/JSYST.2021.3056536
  • 7. He K, Zhang X, Ren S, Sun J. Deep Residual Learning for Image Recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas, NV, USA: IEEE; 2016. p. 770–8. doi:10.1109/CVPR.2016.90
  • 8. Jiang L, Yi W. Power Grid Fault Diagnosis Method Based on VGG Network Line Graph Semantic Extraction. 2022;10(5). doi:SE22502172124
  • 9. Jindal SK, Banerjee S, Patra R, Paul A. 9 - Deep learning-based brain malignant neoplasm classification using MRI image segmentation assisted by bias field correction and histogram equalization. In: Chaki J, editor. Brain Tumor MRI Image Segmentation Using Deep Learning Techniques. Academic Press; 2022. p. 135–61. doi:10.1016/B978-0-323-91171-9.00008-9
  • 10. Ketkar N. Deep Learning with Python. Berkeley, CA: Apress; 2017. doi:10.1007/978-1-4842-2766-4
  • 11. Kiio MN, Wekesa CW, Kamau SI. Development of electrical power transmission system linear hybrid state estimator based on circuit analysis techniques. Heliyon. 2022 Oct;8(10):e11000. doi:10.1016/j.heliyon.2022.e11000
  • 12. Li W, Deka D, Chertkov M, Wang M. Real-Time Faulted Line Localization and PMU Placement in Power Systems Through Convolutional Neural Networks. IEEE Trans Power Syst. 2019 Nov;34(6):4640–51. doi:10.1109/TPWRS.2019.2917794
  • 13. Ogar VN, Hussain S, Gamage KAA. The use of artificial neural network for low latency of fault detection and localisation in transmission line. Heliyon. 2023 Feb;9(2):e13376. doi:10.1016/j.heliyon.2023.e13376
  • 14. Šipoš M, Klaić Z, Nyarko EK, Fekete K. Determining the Optimal Location and Number of Voltage Dip Monitoring Devices Using the Binary Bat Algorithm. Energies. 2021 Jan 5;14(1):1–13. doi:10.3390/en14010255
  • 15. Sipos M, Klaic Z, Nyarko EK, Fekete K, Primorac M, Sljivac D. Determining the Fault Position in the Power System. In: 2020 International Conference on Smart Systems and Technologies (SST). Osijek, Croatia: IEEE; 2020. p. 135–9. doi:10.1109/SST49455.2020.9264078
  • 16. Tokel HA, Halaseh RA, Alirezaei G, Mathar R. A new approach for machine learning-based fault detection and classification in power systems. In: 2018 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT). 2018. p. 1–5. doi:10.1109/ISGT.2018.8403343
  • 17. Vaish R, Dwivedi UD, Tewari S, Tripathi SM. Machine learning applications in power system fault diagnosis: Research advancements and perspectives. Eng Appl Artif Intell. 2021 Nov 1;106:104504. doi:10.1016/j.engappai.2021.104504
  • 18. Xing W, Zheng L, Bai J. Power Grid Fault Diagnosis Method Based on Improved Inception-ResNet Model Graph Semantic Extraction. Int J Sci Eng Res IJSER. 2020;9(8).
Typ dokumentu
Bibliografia
Identyfikator YADDA
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