Power Quality Assessment through Stochastic Equivalent Circuit Analysis

Paper #:
  • 2016-01-1988

  • 2016-09-20
Yeu, R., Wells, J., Miller, C., and Thompson, J., "Power Quality Assessment through Stochastic Equivalent Circuit Analysis," SAE Int. J. Aerosp. 9(1):30-36, 2016, https://doi.org/10.4271/2016-01-1988.
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.Stochastic equivalent circuit analysis calculates network voltage probability distribution utilizing stochastic equivalent circuits assembled into a candidate power system. Utilization of stochastic equivalent circuits allows for rapid analysis of the electric power system under different configurations where the effect of device insertion/removal on power quality is assessed through comparison of the resulting voltage probability distributions with and without the device. In this paper, the stochastic equivalent circuit analysis is introduced along with the methodology to derive the stochastic equivalent circuits, a MATLAB GUI that enables easy configuration and execution of the analysis is presented, and analysis of a representative power system is presented in the context of both traditional EPS power quality metrics as well as new metrics made possible through the stochastic nature of the analysis.
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