Protection and Security Method for Multiple Energy Power Plant-Based Microgrids Using Dual Filtering Algorithm

The multiple energy power plant-based microgrids (MEPPBM) gradually incorporates multiple energy sources such as solar, wind, and battery energy storage, ensuring reliable security & protection has become a paramount challenge. Conventional fault detection methods often fail to address the unique dynamics of these MEPPBM, leading to delays in fault detection and classification. The need for cutting-edge protection schemes that can operate efficiently in such environments is paramount to maintaining system stability and avoiding potential damage. The main objective of this research is to design such protection and security schemes which detect, classify, and locate faults with high accuracy and rapidly with very low computational burden. Therefore, the paper presents a robust security & protection method for modern MEPPBM, employing a hybrid methodology using the Unscented Kalman Filter (UKF) and Particle Filter (PF) algorithms. The UKF is employed for accurate state estimation of the current & voltage signal from faulty bus. While the PF is employed to calculate fault detection & classification indices named PF based residuals (PFBR) and PF-based Harmonic distortion (PFBHD) from UKF-estimated current signal. Then, the Fault section identification index named PF-computed reactive power (PFCRP) is generated from UKF estimated current & voltage signals. Extensive simulations are performed IEC 61850 microgrid test bed using MATLAB/Simulink 2023b software. The presented scheme effectively detects both high-impedance faults (HIF) and solid faults with a remarkable 99.9% accuracy in under 4 milliseconds. Furthermore, the scheme offers low computational burden, making it highly appropriate & efficient for real-time applications in modern MEPPBM.