Learning the geometry of short-circuit faults in power systems for real-time fault detection and classification

Given the short time intervals in which short-circuit faults occur in a power system, a certain time delay between the moment of a fault's inception in the system to the moment in which the fault is actually detected is introduced. In this small time margin, the high amplitudes of the fault current can deal significant damage to the power system. A technique to characterise different types of short circuit faults in a power system for real-time detection, namely AB, BC, CA, ABC, AG, BG and CG faults (and normal operation), is presented based on the geometry of the curve generated by the Clarke transform of the three-phase voltages of the power system. The process was conducted in real time using the HIL402 system and a Raspberry Pi 3, and all programming done in the Python programming language. It was concluded that the tested types of faults can be accurately characterised using the eigenvalues and eigenvectors of the matrix that characterises an ellipse associated with each fault: eigenvalues can be used to determine the fault inception distance and eigenvectors can be used to determine the type of fault that occurred. Next, the design of a machine learning model was done based on the previously mentioned characterisation technique. The model was embedded into a Raspberry Pi 3, thus enabling fault detection and classification in a base power system in real time. Finally, the accuracy of the model was tested under different measurement conditions, yielding satisfactory results for a selected set of conditions and overcoming the shortcomings presented in the current research, which do not perform detection and classification in real time.