Next Article in Journal
Piezoelectric Sensor Signal Analysis after Interface Changes between the Sensor and the Structure under Monitoring
Previous Article in Journal
Fourier-Transform Infrared Microspectroscopy (FT-IR) Study on Caput and Cauda Mouse Spermatozoa
Open AccessProceedings

Identification of Electrical Faults in Underground Cables Using Machine Learning Algorithm

1
Department of Electrical and Electronics Engineering, B. S. Abdur Rahman Crescent Institute of Science and Technology, Chennai 600048, India
2
Department of Information Technology, B. S. Abdur Rahman Crescent Institute of Science and Technology, Chennai 600048, India
*
Author to whom correspondence should be addressed.
Presented at the 6th International Electronic Conference on Sensors and Applications, 15–30 November 2019; Available online: https://ecsa-6.sciforum.net/.
Proceedings 2020, 42(1), 20; https://doi.org/10.3390/ecsa-6-06714 (registering DOI)
Published: 22 January 2020
Transmission and distribution play a vital role in delivering electricity. The presence of any fault in these systems may stop the delivery of electricity, which may create a huge problem in today’s world. Hence, fault detection has become essential for delivering uninterrupted power supply. In this work, a portable and intelligent system is designed, and the fault detection on underground transmission lines is done using a developed hardware system. Also, the proposed system has a thermal camera which is an 8 × 8 array of infrared thermal sensors interfaced with a system-on-chip device, which collects the real-time thermal images when connected to the device. Further, the thermal camera returns an array of 64 individual infrared temperature readings of the transmission line and locates the point of damage that might occur due to the aging of conductor insulation, physical force, etc. Also, 200 images with thermal information from the different instances and directions are utilized to train the adapted machine learning algorithm. The python software is utilized to code the machine learning algorithm inside the system-on-chip device. The convolutional neural network-based machine learning algorithm is adopted and validated using various performance metrics such as accuracy, sensitivity, specificity, precision, negative predicted value, and F1_score. Results demonstrate that the proposed hardware is highly capable of locating faults in underground transmission lines.
Keywords: convolutional neural network; electrical faults; system on-chip; transmission line convolutional neural network; electrical faults; system on-chip; transmission line
MDPI and ACS Style

Alagumariappan, P.; Y, M.S.; A, S.; Fathima, I. Identification of Electrical Faults in Underground Cables Using Machine Learning Algorithm. Proceedings 2020, 42, 20.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop