Electric Discharges Localization for Substation Fault Monitoring Using Two Elements Sensor

摘要

A substation is an important unit in the electric power system. Thus, the monitoring process must be carried out effectively to detect the operation status of the equipment, and pre-fault threat detection is necessary for safe operation. Many methods and intelligent techniques have been developed to provide a better way of fault detection. However, power authorities unwilling to adopt those techniques due to the high cost of installation and more sensors required to improve localization accuracy. Therefore, to reduce cost and increase the speed of detection, this paper presents a 2-element array antenna acted like a sensor to detect and localize the electric discharges from abnormal radiated electromagnetic activities in the substation based on the direction of arriving angle (DOA) received by the array antenna. Software implemented signal processor was used to obtain the radiation patterns for different value of DOA relative to the normalized Array Factor (AFN). This 2-element Sensor was proven to eliminate the undesired signals (such as electromagnetic signals from outside the substation) and maximize the signals in the direction of the desired signal by detecting the DOA of abnormal radiation from power apparatus (such as power transformer or circuit breaker bushings) inside the substation. It was proven that this cohesive unit was able to perform the two tasks by simultaneously eliminating or maximizing signals with very small (such as 0.0873 radians) angle difference between external radiation and radiation from apparatus inside the substation. By performing these tasks, the 2-element Sensor was promisingly able to detect and localize the abnormal electrical activities such as Electric Corona and Electric Arcs discharges that may occur in any substation based on the identified DOA from the power apparatus within the substation as a preventative approach for substation breakdown and to improve the efficiency and the performance of fault detection technique in fu