This brief studies the stability of single-machine infinite-bus power systems with superconducting magnetic energy storage (SMES) via simultaneous design of generator excitation control and SMES control. Since the object model is of non-strict-feedback form and thus the traditional backstepping method is not applicable, we propose an extended backstepping method for a class of general nonlinear systems. Then, we apply the proposed method to the object model and design the generator excitation controller and SMES controller simultaneously, which stabilize the power angle, the generator terminal voltage, and the power oscillation. In addition, we improve the transient and steady-state performances of power systems by introducing class K functions in the design process. Simulation results also show the effectiveness and advantage of the proposed controller.

This paper proposes a decentralized control based on static var compensator (SVC) devices to improve the frequency control of high voltage transmission systems. For a realistic structure preserving power system with voltage dependent loads, we propose a sufficient condition based on a strict Lyapunov energy function, which is utilized to design a nonlinear adaptive SVC controller. This controller only requires local bus measures and parameters. Furthermore, we also present an SVC with a conventional frequency droop controller. The performance of the two considered SVC control systems are evaluated on the well known IEEE New England 39-bus 10-machine power system and compared with conventional SVC controllers.

Accurate prediction of the lithium-ion battery's future capacity and remaining useful life (RUL) is of great significance to battery health management. To the best of our knowledge, most of the existing methods only focus on capturing the degradation trend of the battery, which results in an inaccurate prediction because of the occasional capacity regeneration phenomena in practice. To improve the prediction precision, a hybrid approach of the Grey-Markov chain model (GMCM) and the improved Gaussian process is proposed in this article. Firstly, the historical capacity data is decomposed into two components, namely, the degradation trend and regeneration phenomena, by ensemble empirical mode decomposition. Then, the GMCM method and the multioutput mixture of Gaussian processes are developed for degradation and regeneration prediction, respectively. Lastly, an online calibration strategy is proposed for further prediction accuracy improvement. Extensive experiments are established to validate the proposed GPGM approach, with the results demonstrating that the proposed GPGM consistently outperforms other counterpart methods in terms of one-step and multistep prediction.