{'fraction_non_alphanumeric': 0.018960674157303372, 'fraction_numerical': 0.0, 'mean_word_length': 5.506849315068493, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Optimal integral minus proportional derivative controller design by evolutionary algorithm for thermal‐renewable energy‐hybrid power systems

The goal of this work is to investigate the application of integral minus proportional derivative (IPD) controller for the automatic generation control (AGC) problem comprising of a two-area thermal system integrated with renewable energy (RE)-based sources such as wind, solar and fuel cells. In order to facilitate a realistic environment, each thermal system is equipped with a governor dead band, reheat turbine and generation rate constraint. Moreover, each RE-based power system is modelled by incorporating certain drift and random variations as the key characteristic of RE-based sources. The control performance of IPD is compared with the PID and PI controllers all tuned using an evolutionary technique genetic algorithm by incorporating a step load perturbation in both areas. In order to verify the effectiveness of the control scheme, detailed performance investigations are carried out using random variations in load perturbations and in RE-based power. In addition, sensitivity analysis is also included for wider variations in system parameters in order to test robustness. Based on the extensive simulations for robustness and accuracy, it was observed that IPD outperforms the other controllers and therefore serves as a promising solution to the problem of AGC.