Static security assessment of renewable integrated power systems using ensemble of modified ELM with unsupervised feature learning technique

Abstract

The integration of renewable energy sources into power systems poses various challenges for static security assessment, including intermittency and variability of renewable generation, uncertainty in forecasting and impact on grid stability. Overcoming these challenges involves utilizing advanced modelling methods, refining forecasting algorithms, enhancing monitoring and control systems for the grid, and developing robust static security assessment approaches specifically designed for power systems integrated with renewable energy generation. A modified Extreme learning machine (ELM) based ensemble approach is proposed in this study, where ELM is combined with Levenberg-Marquardt (LM) backpropagation technique to improve the accuracy and robustness of prediction. Further, computational efficiency is improved through an unsupervised feature learning technique in the form of autoencoder to reduce the curse of dimensionality. The ensemble technique provides a comprehensive solution for evaluating the static security of power systems in the presence of uncertainties introduced by renewable energy sources. The uncertainties are incorporated into the test systems by simulating random solar and wind scenarios using a well-established Monte Carlo (MC) simulation method. The effectiveness of this approach is demonstrated through numerical testing on modified IEEE 14-bus, 30-bus, 118-bus, and an Indian practical 75-bus systems. Results show that the proposed model outperforms base learners in terms of reliability and efficiency.