Deep Learning-Based Microgrid Protection

Microgrids are rapidly gaining popularity as a reliable solution for localized power generation and distribution. However, ensuring microgrids' reliable operation and protection remains a critical challenge. Traditional protection methods often fail to address the complexities of microgrids, such as integrating renewable energy sources and multiple interconnected systems. This chapter introduces a unique microgrid protection system based on tunable-Q wavelet transform and multi-layered long short-term memory-based deep learning. Besides accurately detecting and classifying the faults, the system can also determine the specific phase affected by the fault, enabling prompt and targeted responses. A tunable-Q wavelet transform is employed to obtain unique patterns from three-phase current signals, and a multi-layered long short-term memory-based deep learning network is applied for fault identification and classification. Accurate fault detection ensures a quick response to faults, minimizing downtime and maximizing microgrid reliability. Secondly, fault classification enables the system to distinguish between different types of faults, which aids in implementing appropriate mitigation strategies. Lastly, the faulted phase identification capability allows for targeted interventions, streamlining maintenance efforts and reducing costs. The performance of the protection system is evaluated on a simulated microgrid model. The results demonstrate the system's ability to accurately detect faults, classify them with high precision, and identify the faulted phase correctly. These findings highlight the potential of deep learning-based microgrid protection as an effective and efficient solution for enhancing microgrids' reliability and operational efficiency.