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 future Substation Fault Monitoring.

This paper presents a fifth-generation (5G) wireless smart antenna for performing both power substation communication (in space domain beam-steering) and electrostatic discharge (in time domain Ultra-high Frequency "UHF" impulse) detection. The same smart antenna used to communicate with other wireless antennas in the switchyard, as well as with the control room, is utilized to cyclically gather data from power apparatus, busbars, and switches where electrostatic discharge (ESD) may occur. The ESD poses a major threat to electrical safety and lifetime of the apparatus as well as the stability of the power system. The same smart antenna on which beam rotation in space-domain is designed by implementing an artificial neural network (ANN) is also trained in time-domain to identify any of the received signals matching the ultra-high frequency band electrostatic discharge pulses that may be superimposed on the power frequency electric current. The proposed system of electrostatic discharge detection is tested for electrostatic pulses empirically simulated and represented in a trigonometric form for the training of the Perceptron Neural model. The working of the system is demonstrated for electrostatic discharge pulses with rising times of the order of one nanosecond. The artificial intelligence system driving the 5G smart antenna performs the dual roles of beam steering for 5G wireless communication (operating in the space domain) and for picking up any ESD generated UHF pulses from any one of the apparatus or nearby lightning leaders (operating in the time domain).

Power Substation is the most important unit in the power system, therefore, the monitoring process must be carried out effectively to detect the operation status of the equipment, and the maintenance is necessary for safe operation. Substation faulty such as Dielectric breakdowns, originating from the insulation degradation is still a major issue in the power system (1–3).  Many methods and techniques with intelligence approaches have been developed to provide a better way of fault detection in a substation. However, not many are willing to adopt those techniques by reasoning the high cost of installation and more sensors required to improve localization accuracy (4). Therefore, to reduce cost and increase the speed of detection, this paper presents a 2-element array antenna to perform as a sensor to detect and localize the electric discharges (ED) produced by abnormal radiated electromagnetic activities in substation based on the direction of arriving angle (DOA) received by elements in the array antenna. The radiation patterns obtained were then visualized using software of signal processing based on the normalized Array Factor (AFN). This sensor has shown its efficiency in eliminating the interferer signals at random DOA of  and maximizing the desired signal at DOA of 45Ëš; the identified angle direction from the substation power apparatus.  This sensor has the ability to be steered isotopically and terminate or maximize signals which differ by  or 0.0873 radians of DOAs, simultaneously. Having these abilities allowed this sensor to be a cohesive unit in detecting and localizing the abnormal radiated electromagnetic activities in substation based on the identified DOA thus, make it as a promising preventive approach for substation breakdown and improve the performance in Substation Fault Monitoring.