Fast Passive Anti-Islanding Strategy for AC Microgrids Using Cubature Kalman Filtering Algorithm

AC microgrids (ACMGs) represent a promising evolution of traditional distribution systems, driven by environmental advantages and concerns over power quality. However, detecting islanding events within ACMGs poses a significant challenge. In this study, we propose the utilization of the Cubature Kalman Filtering Algorithm (CKFA) to address this challenge by leveraging voltage signals at the point of common coupling (PCC). Initially, CKFA is applied to voltage signatures to compute Voltage Residuals (VR) and Voltage Harmonic Signatures (VHS) through state estimation. These estimated VR and VHS indices are then compared against pre-defined threshold settings to identify islanding states. Subsequently, a tripping decision is made based on the OR operation of both estimated VR and VHS. The proposed method demonstrates efficacy in detecting islanding occurrences under both balanced and unbalanced load/generation conditions and effectively discriminating between islanding and non-islanding conditions. Extensive simulations conducted on MATLAB/Simulink-based IEEE 13-bus test bed and UL-1741 test bed validate the effectiveness of the presented scheme. Results signify a high accuracy rate of 99.9%, tied with low computational complexity and the smallest non-detection zone (NDZ). Additionally, the time of operation for the suggested scheme is less than 1 millisecond, without any false operations, emphasizing its effectiveness in practical application.