A novel approach is presented in this paper for the detection of islanding disturbances in a multiple photovoltaic distribution generation‐based radial and mesh types DC microgrid using adaptive variational mode decomposition (AVMD) hybridized with detrended fluctuation analysis (DFA). Here, the current signals from the DC bus are extracted and processed through AVMD to yield a set of intrinsic mode functions (IMFs). The optimal selection of VMD parameters like the number of modes K and penalty factor (γ) is achieved using improved particle swarm optimization sine cosine levy flight algorithm that considers minimum mean envelope entropy as its objective function. Further from the set of optimal modes, the most significant IMFs are selected using an effective weighted kurtosis index based on a chosen threshold value. For the classification of islanding and non‐islanding events, DFA takes the significant IMFs as its inputs to yield three scaling exponents (α) values according to the window size setup. The α values play a key role in distinguishing various islanding and non‐islanding events occurring on the DC microgrid under various topological changes in two‐dimensional and three‐dimensional scatter plots. The efficacy and superiority of the proposed system are validated by classification accuracy and relative computational time in comparison to the existing methods. The entire study is carried out in MATLAB/Simulink platform.

中文翻译：

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使用改进模式分解技术的多个光伏分布式发电直流微电网在不同配置下的孤岛检测

本文提出了一种新方法，该方法使用自适应变模分解（AVMD）与去趋势波动分析（DFA）混合，在基于多个光伏发电的径向和网格类型DC微电网中检测孤岛干扰。此处，来自DC总线的电流信号通过AVMD提取并处理，以产生一组固有模式函数（IMF）。VMD参数的最佳选择，例如模数K和惩罚因子（γ）是使用改进的粒子群优化正弦余弦征值飞行算法实现的，该算法将最小平均包络熵视为其目标函数。从最佳模式的集合中进一步，基于所选阈值，使用有效加权峰度指数来选择最重要的IMF。对于孤岛和非孤岛事件的分类，DFA将重要的IMF作为输入，根据窗口大小设置产生三个缩放指数（α）值。该α值在区分二维和三维散点图中各种拓扑变化下在DC微电网上发生的各种孤岛和非孤岛事件中起着关键作用。与现有方法相比，该系统的有效性和优越性通过分类准确性和相对计算时间得到了验证。整个研究是在MATLAB / Simulink平台上进行的。