Real-time Detection of Microgrid Islanding Considering Sources of Uncertainty Using Type-2 Fuzzy Logic and PSO Algorithm

Abstract

Background: Nowadays, in microgrids based on renewable energy resources (RESs), the uncertainties of load and power generation of distributed generation (DGs) resources is inevitable, which if not taken into account, will lead to errors in network analysis. Results: In this paper, a new method based on type-2 fuzzy logic isproposed to detect microgrid islanding; in whichthe power system does not misoperateduring complex operations, can correctly discriminate the microgrid islandingand other network events at the proper time, and prevent the undesirable performance of DGs. This controller detects islanding in the fastest time under different conditions, and the uncertainties in the system will not considerablyaffect the controller's performance. Conclusions: The proposed method is simulated on the sample system and examined in different scenarios. Then, a comparison is made in different conditions and scenarios between the suggested method and some common methods that have been presented so far to determine the ability of the proposed method to detect islanding.

Vigil AI Assemble Leveraging Ensemble Technique for Enhanced Detection of Multi-Converter Synchronous Active Islanding Mode in Hybrid Power Distribution Systems

“…Machine learning methods, deep neural networks, and ensemble techniques have shown promise in the detection and categorization of complex electrical events in power systems [8,9]. Since these AI-based solutions can learn from previous data, adapt to new situations, and handle nonlinear interactions between different sections, they are well-suited to the complex hybrid power distribution systems [10], [11].…”