Online Corrective Contingency Analysis for Floating Green Microgrid Using Holomorphic Embedding Method and Extreme Learning Machine

This work presents an integration of the holomorphic embedding power flow method (HEPFM) with the functional expanded extreme learning machine (FEELM) to develop an online corrective contingency analyzer for evaluating ‘whatif’s scenarios in a floating green power solution. The HEPFM offers a rapid and accurate power flow solution, outperforming traditional search-based methods in efficiency and precision. Performance indices based on active power and voltage are computed to assess the impact of contingency events, enabling the recommendation of corrective actions to enhance system resilience under emergency conditions. Three operational modes-black start, baseline operation, and generation outage are simulated and analyzed using MATLAB/Simulink software. The FEELM method is proposed as a computationally efficient approach for generating corrective actions by utilizing generation power, energy storage power, and load power as inputs, applicable in both grid-connected and islanded modes. The intelligent HEPFMFEELM framework demonstrates simplicity, practicality, and robustness, providing a comprehensive and efficient solution for managing contingency events in modern power systems.