Power system control and transient stability analysis play essential roles in secure system operation. Control of power systems typically involves highly nonlinear and complex dynamics. Most of the existing works address such problems with additional assumptions in system dynamics, leading to a requirement for a complete and general solution. This paper, therefore, proposes a novel control framework for various power system control and stability problems leveraging a learning-based approach. The proposed framework includes a two-module structure that iteratively and jointly learns the candidate Lyapunov function and control law via deep neural networks in a learning module. Meanwhile, it guides the learning procedure towards valid results satisfying Lyapunov conditions in a falsification module. The introduced termination criteria ensure provable system stability. This control framework is verified through several studies handling different types of power system control problems. The results show that the proposed framework is generalizable and can simplify the control design for complex power systems with the stability guarantee and enlarged region of attraction.

Cascaded H-bridge topology has been used in grid-tied converter for battery energy storage system due to its modular structure. To fully utilize the converter's modularity, this paper propose a hierarchical distributed control architecture that consists of primary control, secondary control and battery state of charge (SOC) balancing control. Primary control ensures accurate current tracking while a distributed secondary control based on consensus algorithm is presented to regulate power sharing among modules and is proved to be stable theoretically. A distributed SOC balancing control is further introduced to improve energy efficiency of battery energy storage system. Finally, the hierarchical distributed control strategy is implemented using hardware controllers and a software platform. Besides, a carrier phase shift control is also implemented to achieve multilevel output voltage and harmonic reduction. The experimental results demonstrate the performance of the proposed control scheme effectively.

The resilience of distribution systems has been challenged by power outages caused by natural disasters, which calls for novel solutions to system restoration. However, the existing black-start (BS) techniques are mainly developed at the transmission level. This problem can be resolved by adopting dynamic microgrids (MGs), i.e., MGs with dynamic and adjustable boundaries nested in the distribution systems. This article proposes a restoration procedure that adopts self-organizing inverters as BS units and achieves distribution system restoration in the context of dynamic MGs. The proposed restoration procedure is structured as a two-stage approach and designed to restore a complete blackout distribution system to the state where the system is ready for seamless main grid reconnection. A framework of dynamic MG operation is developed, which improves the self-healing capability of off-grid distribution systems with automatic sectionalization and flexible reconfiguration. A sequence of actions during restoration is defined, along with distributed controllers designed with considerations of practical operating challenges. The proposed restoration procedure is validated on a 34-bus system using real-time hardware-in-the-loop simulation.

This article studies how to solve the energy management (EM) problem while balancing economy and security. Firstly, a defense scheme is developed to relieve the negative effects of denial-of-service (DoS) attacks. Secondly, a secure predefined-time distributed EM algorithm via the time-base generator (TBG) is devised which can be suitable for directed graphs, and sensitive information is decomposed into two dynamic variables for key information privacy preservation. Thirdly, a dynamic event-triggered (DET) mechanism is proposed to economize communication resources. By constructing the Lyapunov energy function, it is proven that the convergence time of the devised distributed algorithm can be predefined by users independent of initial conditions. Zeno behavior is proven to be avoided, which exhibits the effectiveness of the DET approach. At last, the simulations are given to show the validity and advancement of the designed secure distributed algorithm.

The state-of-the-art dynamic consensus-based microgrid (MG) secondary controls suffer from the communication delay effect. Specifically, the system could not converge to the desired operating points with time-delayed communications. Such deviations are hard to detect in a decentralized manner and could destabilize the system. This paper proposes an accurate distributed secondary controller for DC MGs based on the surplus consensus algorithm. The proposed controller achieves accurate proportional power sharing and average voltage regulation among distributed generators (DGs) with the presence of variable and bounded communication delays. A surplus consensus-based observer is developed. The developed observer is proved robust against variable and bounded communication delays; it tracks the average of a group of dynamic states with zero steady-state deviations, which cannot be done using the conventional dynamic consensus-based observer. The convergence speed of the developed observer is analyzed and a parameter design procedure is presented. Moreover, the delay-dependent stability analysis of DC MG operation with the proposed secondary controller is derived. The marginal delay that leads the system to instability is calculated. At last, the performance of the proposed secondary controller and the developed stability analysis are validated under various scenarios using MATLAB/Simulink.