Parameters identification of isolated wind-diesel power systems (WDPS) is a significant issue in stability analysis of the power system as well as guaranteeing the power generation through the control system. In this paper, enhanced whale optimization algorithms (EWOA) are proposed to deal with the parameter identification problem of a WDPS system. The proposed EWOA effectively tackles the premature convergence problem of WOA by splitting the population into two subpopulations and updating the position of each whale according to the position of the best agent in its current subpopulation, the position of the other subpopulation’s best agent, and the position of the best neighboring agent. Furthermore, fractional chaotic maps are embedded in the search process of EWOA to increase its performance in terms of accuracy. For validation purposes, the proposed algorithms are applied to identify the unknown parameters of WDPS, where different statistical analyzes and comparisons are carried out with other recent state-of-the-art algorithms. Simulation results confirm that the algorithms have less deviation in parameter estimation, more convergence speed, and higher precision in comparison with other algorithms.

There always exists a conflict between ride comfort and suspension deflection performances during the vibration control of suspension systems. Active suspension control systems, which are designed by linear methods, can only serve as a trade-off between these conflicting performance criteria. Both performance objectives can only be accomplished at the same time by using a nonlinear controller. This paper addresses the non-linear induced L₂ control of an active suspension system, which contains non-linear spring and damper elements. The design method is based on the linear parameter varying (LPV) model of the system. The proposed method utilizes the bilinear damping characteristic, stiffening spring characteristic when the suspension deflection approaches the structural limits, mass variations and parameter-dependent weighting filters. Simulation studies both in time and frequency domain demonstrate that the active suspension system controlled by the proposed method always guarantees an agreement between acceleration (comfort) and suspension deflection magnitudes together with a high ride performance.

Recent advances in technology have paved the way for the increased penetration of wind energy conversion systems (WECSs) into the grid, worldwide. However, the existence of model uncertainties and intermittency of wind power can lead to malfunction of the stabilizing controllers and degrade the WECSs' power production performance. In this work, a compound control scheme comprising active fault-tolerant fractional-order nonsingular terminal sliding mode controllers (AFTSMCs) and a sliding mode observer (SMO) is developed to enhance the robustness of doubly-fed induction generator (DFIG) -based WECSs against uncertainties and maintain their desired performance. The developed AFTSMCs alleviate the chattering problem and overcome the compromise between fast response and the undesirable chattering problem. At the same time, it performs the speed trajectory tracking and rotor current regulation tasks. Moreover, under inevitable false fault detections due to unavoidable gradual performance degradations in the current sensors, a tolerance boundary is circumscribed for actual faults occurrence, allowing the developed robust SMO to estimate and reconstruct the rotor current during sensor faults with a high level of reliability. Evaluations of comparative performance are provided and validate the cooperative fault-tolerant method's superior control performance over other advanced approaches.