Abstract: Machine learning, ML, using labeled data is performed to thereby train a ML model to generate decision logic for a controller of an IACS, in particular a power system controller. The ML model has ML model inputs and ML model outputs. The ML model is decomposed into a plurality of computational sub-processes, wherein intermediate signals output by one of the computational sub-processes are input into a consecutive one of the computational sub-processes. Information on the trained ML model is generated based on the intermediate signals and is output for interpretation, verification, visualization, and/or inspection of at least one of the computational sub-processes.

Abstract: The present disclosure relates to a method for distance protection of a transmission line carrying a plurality of phases for a phase-to-phase-to-ground fault comprising a first phase and a second phase of the plurality of phases as faulted phases in the phase-to-phase-to-ground fault, wherein the first phase is different from the second phase, the method comprising: obtaining a first impedance of a first electrical loop formed by a first phase carried on the transmission line and a ground potential based on a zero-sequence current (S601); obtaining a second impedance of a second electrical loop formed by a second phase carried on the transmission line and a ground potential based on the zero-sequence current (S602); computing an apparent impedance of the transmission line and a ground potential based on the transmission line seen at a first terminal based on the first impedance and the second impedance (S603); and performing the distance protection based on the apparent impedance (S604).

Abstract: Machine learning, ML, using labeled data is performed to thereby train a ML model to generate decision logic for a controller of an IACS, in particular a power system controller. The ML model has ML model inputs and ML model outputs. The ML model is decomposed into a plurality of computational sub-processes, wherein intermediate signals output by one of the computational sub-processes are input into a consecutive one of the computational sub-processes. Information on the trained ML model is generated based on the intermediate signals and is output for interpretation, verification, visualization, and/or inspection of at least one of the computational sub-processes.