Abstract:

Objectives DC microgrids are prone to the issue of large-signal stability due to the low inertia and constant power load characteristics. Traditional model-based methods involve complex calculations and are difficult to solve. To address these issues, this study investigates intelligent analysis methods for large-signal stability of DC microgrids. Methods Common artificial intelligence (AI) classifiers are selected to analyze the stability of DC microgrids. A comparative analysis is conducted on three types of common AI technologies (covering six methods)-deep learning, support vector machine, and decision trees-for large-signal stability assessment in a specific DC microgrid case study. Results Comparative analysis based on specific examples shows that in the large-signal stability assessment of DC microgrids, long short-term memory (LSTM) networks outperform other methods in terms of overall performance (accuracy, real-time capability, and adaptability). Conclusions The LSTM network classifier shows high compatibility with the state-space equations of DC microgrids, making it more suitable than traditional machine learning classifiers for large-signal stability analysis of DC microgrids. Meanwhile, ensuring the performance of the classifier requires appropriate selection of parameter values.

Key words: DC microgrids, distributed energy resources, renewable energy, energy storage, artificial intelligence(AI), machine learning, deep learning, long short-term memory network