This paper proposes a new cascaded fractional-order controller (CC-FOC) to solve the load frequency control (LFC) problem of an interconnected power system. The CC-FOC consists of a three-degree-of-freedom fractional-order proportional-integral-differential (3DOF-FOPID) controller and a fractional-order proportional-integral (FOPI) controller. Each area of the two-area interconnected power system in this study consists of a thermal unit, a hydro unit, a diesel unit, and a doubly-fed induction generator (DFIG). The enhanced particle swarm optimization (PSO) and gravitational search algorithm (GSA) under the chaotic map optimization (CPSOGSA) technique are used to optimize the controller gains and parameters to enhance the load frequency control performance of the cascade controller. Moreover, simulation experiments are conducted for the interconnected power system under load perturbation and random wind speed fluctuations. The simulation results demonstrate that the proposed cascaded fractional-order controller outperforms the traditional proportional-integral-differential (PID) controller and three other fractional-order controllers in terms of LFC performance. The suggested cascade controller displays strong dynamic control performance and the resilience of the cascade fractional-order controller by adjusting the load disturbance and analyzing the system characteristics.

The converter is an important component of a wind turbine, and its control system has a significant impact on the dynamic output characteristics of the wind turbine. For the double-fed induction generator (DFIG) converter, the control parameter identification method is proposed. In this paper, a detailed dynamic model of DFIG with the converter is built, and the trajectory sensitivity method is used to study the observation points that are sensitive to the change of control parameters as the observation quantity for control parameter identification; the Whale Optimization Algorithm (WOA) is used to study the converter control system parameters that dominate the output characteristics of DFIG in the dynamic full-process simulation. To validate the proposed method, four classical test functions are used to verify the effectiveness of the algorithm, and the control parameters are identified by setting a three-phase grounded short-circuit fault under maximum power point tracking (MPPT), and the identification results are compared with particle swarm optimization (PSO) and chaotic particle swarm optimization (CPSO) to show the superiority of the proposed method. The final results show that the proposed WOA can identify the control system parameters faster and more accurately.

Variations in generator parameters that occur during the operation of a doubly-fed induction wind turbine (DFIG) constitute a significant factor in the degradation of control performance. To address the problem of difficulty in identifying multiple parameters simultaneously in DFIG, a parameter identification method depending on the adaptive grey wolf algorithm with an information-sharing search strategy (ISIAGWO) is proposed to solve the problem of low accuracy and slow identification speed of multiple parameters in DFIG. The easily obtainable generator outlet current was selected as the observed quantity, and the trajectory sensitivity analysis was performed on the five electrical parameters of the DFIG to derive its discriminability. Finally, the parameter recognition of the DFIG was carried out using the ISIAGWO algorithm in the MATLAB/Simulink simulation software, applying the proposed calculation functions. The experimental results show that the ISIAGWO algorithm has better identification accuracy, stability, and convergence for DFIG’s generator parameter identification.