Signal Processing Based Microgrid Islanding Detection

The Kingdom of Saudi Arabia is moving towards renewable based power generation with an aim to install around 60 GW of power from renewable sources in the coming years. PV and wind based sources can be connected at the distribution level forming a microgrid. Microgrids integrated with distributed energy resources (DERs) provide many benefits including high power quality, energy efficiency and low carbon emissions on the power grid. Microgrids are operated either in grid connected or island modes running on different strategies. However, one of the major technical issues in a microgrid is unintentional islanding, where failure to trip the microgrid may lead to serious consequences in terms of protection, security, voltage and frequency stability, and safety. Therefore, fast and efficient islanding detection is necessary for reliable microgrid operations. Islanding detection methods (IDMs) can be classified into two basic types as remote and local techniques. Remote type IDMs are based on communications between main grid and microgrid and are fast, reliable and effective with zero non-detection zone. These techniques can be applied to multi-inverter microgrids and do not degrade the power quality; however, they are complex and expensive. On the other hand, local methods are classified as passive, active and hybrid. Passive techniques monitor microgrids parameters, and detect the islanding fault based on their changes however, they have a relatively large non-detection zone. Active detection techniques inject a perturbation to the system that affects the power quality and hybrid techniques are a combination of passive and active techniques. This project aims to design a signal processing based islanding detection method by modifying the passive method to detect accurately and precisely islanding condition within the shortest period without affecting power quality. The proposed method will overcome non-detection zone and threshold setting requirements of conventional techniques