An Evaluation of Different Controllers Based on the Network Frequency Maintenance Strategy for a Large and Multi-control-area Interconnected Power System

Abstract—Maintaining the network frequency at its nominal value due to the load changes is one of the most important issues in the control and operation schedule of a large and multi-area interconnected power system. For the effective control solution, two types of tie-line bias control strategy-based controllers have been applied, i.e., classical and improved controllers. The classical controllers using conventional regulators, including integral, proportional–integral and proportional-integral-derivative, have achieved initial control results to bring the steady state back to the network. However, due to the very poor control features (e.g., large overshoots and long settling times), they need to be replaced with improved controllers, such as fuzzy logic and artificial neural network. To obtain an entire evaluation of the application of different load-frequency controllers, a five-control-area interconnected power system was built as a typical case study. Three improved controllers using proportional-plus-integral-based fuzzy logic and artificial neural network-based non-linear autoregressive moving average (NARMA-L2) architectures as well as their hybrid combination will also be investigated in this study. Simulation results obtained reveal that the improved controllers have obtained smaller overshoots (from 13.95 to 84.18%) and shorter settling times (from 19.91 to 65.71%) compared with the classical controllers.