An Enhanced Cassie–Mayr-Based Approach for DC Series Arc Modeling in PV Systems

Abstract

DC series arc faults are known as a great threat in photovoltaic (PV) systems that may lead to fire and electric shock hazards. This article concentrates on driving effective models based on Cassie and Mayr equations for dc series arc using actual recorded data. Despite Cassie and Mayr equations that use invariant parameters during arc modeling, the proposed models employ the combinations of both Cassie and Mayr equations in which the time-variant nature of arc’s coefficients is taken into account. While the original Cassie and Mayr equations have two influential parameters for arc modeling, the first model presents a developed Cassie–Mayr equation with three parameters. Also, to further improvement in the model’s accuracy, the second model presents a developed Cassie–Mayr equation with five parameters. While the first and second models provide more prevision comparing with the original Mayr and Cassie–Mayr modeling, a third model is presented that treats with parameters of the original model as time-series parameters. To obtain the arc’s coefficients, a new formulation for coefficient estimation based on Levenberg–Marquardt (LM) algorithm is presented. Evaluating the proposed models with the actual recorded data, the results show the proposed models have more accuracy in comparison with the original Mayr and Cassie–Mayr.