Comparative analysis of machine learning approaches in enhancing power system stability

The low-frequency oscillations (LFOs) are usually considered as the slow-poisoning issues for electric power networks as they can cause system blackout if not resolved in time. However, this LFO issue has recently become a significant concern to the utility body owing to integrating renewable energy (RE) resources in the power networks. Because of the intermittent nature of RE sources, the LFOs are frequently introduced in the power networks and appear as a threatening issue in the end. Therefore, this chapter has addressed an efficient solution: implementing different artificial intelligence (AI) techniques in electric power networks to overcome the undesired LFOs and improve the overall stability of the networks by tuning the power system stabilizer (PSS) parameters. In this case, four machine learning (ML) tools, group method of data handling (GMDH), extreme learning machine (ELM), neurogenetic (NG), and multi-gene genetic programming (MGGP), were employed in two different electric networks to investigate the applicability of AI techniques in enhancing the system's stability. The stability measuring indices of the power networks like minimum damping ratio (MDR), eigenvalues, and the time-domain simulations are evaluated for different operating situations with newly conjectured key parameters of PSS, tuned in real time. Furthermore, the results of the developed ML models were compared with the conventional approach to exhibit the applicability and superiority of AI techniques over similar approaches.