Designing a fast fourier transform based islanding detection method for DC microgrids

Abstract

The scarcity of the natural resources, environmental issues and rising population in the world, demands for innovative concepts, such as microgrids to the modern power system. Nowadays, microgrids are becoming very popular and the most appropriate options to enrich the power system with renewable generation. In addition to that, rapid growth in the DC nature of the loads within the power system is apparent due to the popularity of power electronic devices and recent trends in electrified transportation systems. Hence, researchers are introducing direct current microgrid concepts to the power system, and it has become a highly emerging and trending research area at present. DC microgrids can operate under two main operating modes: grid-connected and islanded operation. The main difficulties of implementing the concept in this concept are the lack of proper international standards, safety features, and protection issues within the systems. Islanding detection is the most challenging and vital requirement in microgrid protection to ensure the safety of the personnel and microgrid equipment and to maintain a smooth and reliable operation of the DCMG. Islanding detection is used to detect the disconnection of the DC microgrid from the utility and switch to proper controls to serve critical loads in the power island. This thesis presents a novel method of islanding detection for DC microgrids by using Fast Fourier Transform based analysis of DC-link voltage. Further, testing was carried out adopting a 10-kW low voltage DC microgrid with a single-phase bidirectional inverter interface. In addition, a DC microgrid consisting of photovoltaic model with maximum power point tracking, DC loads, AC loads and a battery module with stateof- charge based multi-mode battery management system was modeled. All the modeling and simulations were carried out considering several network configurations and network conditions with the EMTDC/PSCAD v4.2 environment. Simulations were evaluated according to the IEEE 1547-2018 standard. The probabilistic approach was applied to show the robustness of the experimental results of the proposed method.