Feedback chaotic growth optimizer for parameter extraction of a novel direct current arc model

Area of Science:

• Electrical Engineering
• Materials Science
• Computational Science

Background:

• Direct current (DC) arc faults pose a significant fire hazard in photovoltaic (PV) systems.
• Accurate modeling of DC arc faults is crucial for understanding their behavior and developing effective detection methods.

Purpose of the Study:

• To propose a novel exponent segmented noise model for DC arc faults.
• To introduce a new metaheuristic algorithm, the feedback chaotic growth optimizer (FCGRO), for precise parameter extraction.
• To evaluate the performance of the proposed model and algorithm against existing methods.

Main Methods:

• Development of the exponent segmented noise model characterizing arc noise via an exponential relationship between frequency and spectral energy.
• Introduction of the feedback chaotic growth optimizer (FCGRO), enhancing the traditional growth optimizer (GRO) with feedback and chaos mechanisms.
• Comparative evaluation of FCGRO against eight state-of-the-art algorithms using benchmark problems and experimental PV arc fault data.

Main Results:

• FCGRO achieved an average root mean square error (RMSE) of 0.0418 with a standard deviation of 0.00818, outperforming other algorithms in accuracy and stability.
• The proposed exponent segmented noise model reduced RMSE by an average of 53.26% compared to existing models.
• FCGRO demonstrated competitive computational efficiency with an average processing time of 9.969 seconds.

Conclusions:

• The proposed exponent segmented noise model offers superior capability for modeling DC arc faults.
• FCGRO provides a more accurate, stable, and computationally competitive approach for parameter extraction in DC arc fault analysis.
• The findings contribute to enhanced safety and reliability in photovoltaic systems.