Microgrid (MG) is a key part of the future energy system that can operate in either grid-connected or island mode by enabling the growing integration of renewable energy sources such as photovoltaic energy, wind energy and hydroelectric power. One of the most substantial phenomena in microgrids is unintentional islanding which can cause significant problems such as power quality, voltage stability and safety hazards. This paper introduces a new passive islanding detection method (IDM) for synchronous and inverter interfaced MGs. The multi-long short-term memory (LSTM) architecture which is one of the most recent and popular techniques of deep learning is first proposed by utilizing voltage and current harmonic distortion measured at the point of common coupling (PCC) of MG. For the first time, the distorted main grid is taken into account with various operating conditions. Numerical simulations are performed in MATLAB/Simulink and comparative analysis of the proposed method with intelligent IDMs is realized to verify its overall superiorities. The proposed method has achieved remarkable performance like average accuracy of 99.3% and minimum loss of 0.06. The multi-LSTM model is able to detect islanding events with accuracy of 97.93% for small than ± 0.5% power mismatch within 50 ms detection time.

微电网 (MG) 是未来能源系统的重要组成部分，通过实现光伏能源、风能和水力发电等可再生能源的日益融合，可以并网或孤岛模式运行。微电网中最重要的现象之一是无意的孤岛，它会导致诸如电能质量、电压稳定性和安全隐患等重大问题。本文介绍了一种新的无源孤岛检测方法 (IDM)，用于同步和逆变器接口 MG。多长短期记忆 (LSTM) 架构是深度学习的最新和流行技术之一，它首先利用在 MG 的公共耦合点 (PCC) 处测量的电压和电流谐波失真提出。首次，在各种运行条件下都考虑了扭曲的主电网。在MATLAB/Simulink中进行了数值模拟，并通过智能IDMs对所提出的方法进行了对比分析，以验证其整体优势。所提出的方法取得了显着的性能，如平均准确率为 99.3%，最小损失为 0.06。多 LSTM 模型能够在 50 ms 检测时间内以 97.93% 的准确度检测小于 ± 0.5% 的功率失配的孤岛事件。