The role of nuclear energy in the future of energy systems in Egypt

Elkhodary, Salem M.; Mahmoud, Hassan; Qotb, Saied A.; Sharouda, Emad El-Din

doi:10.1109/mepcon.2017.8301248

Cited by 1 publication

(1 citation statement)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…According to many researchers, the most commonly used form of energy is electrical energy, which is produced from various sources like coal, solar, nuclear etc. [1][2][3][4][5]. As it is possible to produce clean electrical energy using various renewable sources like wind mill, solar, hydro etc.…”

Section: Introductionmentioning

confidence: 99%

Identification of Electrical Faults in Underground Cables Using Machine Learning Algorithm

Alagumariappan

Sonya

et al. 2019

The 6th International Electronic Conference on Sensors and Applications

View full text Add to dashboard Cite

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.

show abstract