SIMULATION-BASED EVALUATION OF MACHINE LEARNING ALGORITHMS FOR FAULT DETECTION IN MICROGRIDS

Engineering notes

Engineering notes will be added by the Power for Solar editorial team.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为光伏效率、钙钛矿太阳能电池、储能技术、太阳能热利用、BIPV、并网技术等高价值论文补充中文说明。

Original abstract

This paper presents a simulation-based analysis of fault detection in alternating current microgrids through data-driven methods. A detailed simulation-based microgrid model is created for conducting the fault analysis, including single phase ground fault, line fault, two phase fault and three phase fault situations. Simulations were carried out under different loading conditions and fault impedance values to obtain an extensive set of data containing voltage, current, and signal characteristics. This data was used to test the performance of various machine learning techniques for fault classification. The results show that the ensemble methods perform better than standalone models, with Random Forest (RF) demonstrating the highest classification performance. This findings of this research underscore the potential of data-driven techniques in improving the robustness and flexibility of AC protection systems in contemporary microgrids. Future work will focus on extending the analysis to more complex fault conditions and validating the proposed approach through real-time implementation.

Links and sources

• PDF from original source

Comments