Low-cost and Efficient Fault Detection and Protection System for Distribution Transformer

Abstract

Distribution transformers are a vital component of electrical power transmission and distribution system. Frequent Monitoring transformers faults before it occurs can help prevent transformer faults which are expensive to repair and result in a loss of energy and services. The present method of the routine manual check of transformer parameters by the electricity board has proven to be less effective. This research aims to develop a low-cost protection system for the distribution transformer making it safer with improved reliability of service to the users. Therefore, this research work investigated transformer fault types and developed a microcontroller-based system for transformer fault detection and protection system using GSM (the Global System of Mobile Communication) technology for fault reporting. The developed prototype system was tested using voltage, current and temperature, which gave a threshold voltage higher than 220 volts to be overvoltage, a load higher than 200 watts to be overload and temperature greater than 39 degrees Celsius to be over temperature was measured. From the results, there was timely detection of transformer faults of the system, the transformer protection circuits were fully functional, and fault reporting was achieved using the GSM device. Overall, 99% accuracy was achieved. The system can thus be recommended for use by the Electricity Distribution Companies to protect distribution transformers for optimal performance, as the developed system makes the transformers more robust, and intelligent. Hence, a real-time distribution transformer fault monitoring and prevention system is achieved and the cost of transformer maintenance is reduced to an extent.

[Retracted] Multimodal Sensor Motion Intention Recognition Based on Three‐Dimensional Convolutional Neural Network Algorithm

“…The classifier, feature set, and window size were optimized for each stage. The experiment recruited 7 healthy subjects and a tibial amputation subject and collected 6 movement patterns (5 steady-state patterns, 1 in the passive mode), the motion signals of two inertial measurement units and a pressure sensor placed on the affected side were collected, the classifier was constructed by using discriminant analysis combined with secondary discriminant analysis, and the recognition rate reached 90% [ 16 ]. Relevant scholars use the acceleration sensor installed on the prosthesis receiving cavity to calculate the angle of the hip joint during the swing period of the prosthesis [ 17 , 18 ].…”