ICA assisted FTIλDN controller for AGC performance enrichment of interconnected reheat thermal power systems

In the incessantly growing size and structure of the modern power system having different load demand uncertainties, the use of intelligent, proficient and robust automatic generation control (AGC) strategy is indispensable to deliver decent superiority of power supply to the consumers. This research presents a fuzzy-tilt-fractional order integral-filtered derivative ((FTIDN)-D-lambda) controller as a novel control tool to improve AGC recital of multi-area interconnected power systems (PSs). The imperialist competitive algorithm (ICA) is employed to optimize the (FTIDN)-D-lambda controller parameters. To boost (FTIDN)-D-lambda controller performance, asymmetrically spaced membership functions (MFs) are utilized, where MFs adjoining to zero are meant for fine-tuning and MFs far of zero are used for coarse-tuning. Firstly, a well adopted linear 2-area reheat thermal PS (RTPS) is explored critically and then to signify the virtue and scalability of the approach, the analysis is conducted on a 5-area unequal RTPS comprising deadband and generation rate constraint nonlinearities. The supremacy of the approach is established by contrasting the results with ICA/bacterial foraging optimization algorithm optimized FTIDN and various existing controllers. Investigation of results affirms the better performance of (FTIDN)-D-lambda over other controllers regarding minutest error criteria and undershoot/overshoot/settling time of frequency/tie-line power deviations at disturbances. Additionally, robustness analysis validates the robust conduct of (FTIDN)-D-lambda controller under variations of parameters, load disturbances and in the incident of temporary/permanent tie-line outage. At last, a real-time hardware-in-the-loop (HIL) simulation test is performed to authenticate the viability and effectiveness of the method for practical applications.