Realizing of load frequency control design with an advanced artificial hummingbird algorithm using greedy fitness-distance balance selection method

In this thesis, the fitness-distance balance (FDB) selection method, recently introduced in the literature, has been applied to the artificial hummingbird algorithm (AHA), and the developed FDBAHA algorithm is proposed as a control method for solving the load frequency control (LFC) problem in power systems. Initially, the performance of the developed FDBAHA algorithm was compared with the basic AHA algorithm and several significant algorithms selected from the literature based on the CEC 2017 and CEC 2020 problems. Subsequently, to implement LFC, three distinct power systems (two-area non-reheat thermal, two-area multi-source, and photovoltaic-thermal) were modeled in MATLAB/Simulink. For each power system, three separate controllers were designed with parameter values optimized using the FDBAHA algorithm. The performance of the designed controllers was compared by plotting frequency and tie-line power deviation curves for each power system and additionally presenting settling times in tabular form. The improvement in the performance of the developed FDBAHA algorithm was evaluated through statistical and control method analyses, and the results were discussed.