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Experimental Hardware-in-the-loop (xHIL) testing utilizing signal and/or power emulation imposes a hard real-time requirement on models of emulated subsystems, directly limiting their fidelity to what can be achieved in real-time on the available computational resources. Most real-time simulators are CPU-based, for which the overhead of an instruction-set architecture imposes a lower limit on the simulation step size, resulting in limited model bandwidth. For power-electronic systems with high-frequency switching, this limit often necessitates using average-value models, significantly reducing fidelity, in order to meet the real-time requirement. An alternative approach emerging recently is to use FPGAs as the computational platform, which, although offering orders-of-magnitudes faster execution due to their parallel architecture, they are more difficult to program and their limited fabric space bounds the size of models that can be simulated.

Movement toward more-electric architectures in military and commercial airborne systems has led to electrical power systems (EPSs) with complex power flow dynamics and advanced technologies specifically designed to improve power quality in the system. As such, there is a need for tools that can quickly analyze the impact of technology insertion on the system-level dynamic transient and spectral power quality and assess tradeoffs between impact on power quality versus weight and volume. Traditionally, this type of system level analysis is performed through computationally intensive time-domain simulations involving high fidelity models or left until the hardware fabrication and integration stage. In order to provide a more rapid analysis prior to hardware development and integration, stochastic equivalent circuit analysis is developed that can provide power quality assessment directly in the frequency domain.