We show that by appropriately composing these two classes of models it is possible to leverage on their respective advantages.To this end, we propose an interface between components that are modeled using Real-Time Calculus [Chakraborty, Künzli and Thiele, DATE 2003] and those that are modeled using Event Count Automata [Chakraborty, Phan and Thiagarajan, RTSS 2005]. The resulting modeling technique is as expressive as Event Count Automata, but is amenable to more ef cient analysis. We illustrate these advantages using a number of examples and a detailed case study.
Photovoltaic (PV) system output electricity is related to PV cells' conditions, with the PV faults decreasing the efficiency of the PV system and even causing a possible source of fire. In industrial production, PV fault detection is typically laborious manual work. In this paper, we present a method that can automatically detect PV faults. Based on the observation that different faults will have different impacts on a PV system, we propose a method that systematically and iteratively reconfigures the PV array until the faults are located based on the specific current-voltage (I-V) curve of the (sub-)array. Our method can detect several main types of faults including open-circuit faults, mismatch faults, short circuit faults, etc. We evaluate our methods by Matlab/Simulink-based simulation. The results show that the proposed methods can accurately detect and classify the different faults occurring in a PV system.
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