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This paper proposes a new adaptive sliding mode (ASM) decentralized excitation controller to improve the stability of multimachine power systems under different perturbations such as system' parametric and structural uncertainties. The stability of the closed-loop system is proved by Lyapunov stability theory. The proposed controller is evaluated through simulation on the standard IEEE 33-bus-bar power system which contains 6 synchronous machines and a HVDC-link. The simulation results indicate good robustness and satisfactory performance of the proposed controller. Moreover, in this paper, using the space-phasor based sequence networks method, a procedure for the dynamic analysis of modern power systems under the transient asymmetrical faults is presented. The method considers the complete dynamics of the synchronous machines and the HVDC-link and provides the possibility of taking into account the sequence networks dynamics.

In order to support the inertia of a microgrid, virtual synchronous generator control is a suitable control method. However, the use of the virtual synchronous generator control leads to unacceptable transient active power sharing, active power oscillations, and the inverter output power oscillation in the event of a disturbance. This study aims to propose a deep neural network controller which combines the features of a restricted Boltzmann machine and a multilayer neural network. To initialize a multilayer neural network in the unsupervised pretraining method, the restricted Boltzmann machine is applied as a very important part of the deep learning controller. The Lyapunov stability method is used to update the weight of the deep neural network controller. The proposed method performs power oscillation damping and frequency stabilization. The experimental and simulation results are presented to assess the usefulness of the suggested method in damping oscillations and frequency stabilization.

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