Microgrid control and operation depend on fault detection and classification because it allows quick fault separation and recovery. Due to their reliance on sizable fault currents, classic fault detection techniques are no longer suitable for microgrids that employ inverter-interfaced distributed generation. Nowadays, deep learning algorithms are essential for ensuring the reliable, safe, and efficient operation of these complex energy systems. They enable quick responses to faults, reduce downtime, enhance energy efficiency, and contribute to the overall sustainability and resilience of microgrids. With this intent, this work proposes a “Discrete Wavelet Transform with Deep Neural Network (DWT-DNN)” for detecting and classifying the various faults that occurred in hybrid energy-based multi-area grid-connected microgrid clusters. The proposed DWT-DNN first extracts the input features from the point of common coupling of the cluster system using DWT, and then, these decomposed features are applied as input variables to train the DNN for the detection and classification of various faults. All the investigations are performed in the “MATLAB/Simulink 2022a” environment. To validate the effectiveness of the proposed DWT-DNN, the results are compared with wavelet packet transforms (WPT) in terms of accuracy in detecting and classifying the faults. From the simulation findings and observations, it is evident that the proposed DNN produced fruitful results.

微电网的控制和运行依赖于故障检测和分类，因为它可以实现快速故障分离和恢复。由于依赖于相当大的故障电流，经典的故障检测技术不再适合采用逆变器接口分布式发电的微电网。如今，深度学习算法对于确保这些复杂能源系统的可靠、安全和高效运行至关重要。它们能够快速响应故障，减少停机时间，提高能源效率，并有助于微电网的整体可持续性和弹性。为此，本文提出了一种“深度神经网络离散小波变换（DWT-DNN）”，用于检测和分类基于混合能源的多区域并网微电网集群中发生的各种故障。所提出的DWT-DNN首先使用DWT从集群系统的公共耦合点提取输入特征，然后将这些分解的特征用作输入变量来训练DNN以检测和分类各种故障。所有研究均在“MATLAB/Simulink 2022a”环境中进行。为了验证所提出的 DWT-DNN 的有效性，将结果与小波包变换 (WPT) 在故障检测和分类的准确性方面进行了比较。从模拟结果和观察结果来看，所提出的 DNN 显然取得了丰硕的成果。