This paper uses active flux concept to review fundamental frequency sensorless algorithms for both induction and permanent magnet motors in one framework. Fundamentally, sensorless torque estimation can be directly solved using voltage model (VM) estimator, or indirectly solved using current model (CM) estimator. The latter turns the torque estimation problem into a speed estimation problem. The stator flux in VM and the d-axis angle in CM are deemed as the two sets of original states for sensorless drive. Through change of states, the direct torque estimation can be realized via observer designs; whereas the speed dependency of the unknown state (e.g., active flux and emf) gives rise to a class of speed estimation methods, known as model reference adaptive system (MRAS). The idea of a general speed observer is proposed to summarize various separate speed estimation methods needed for direct torque estimation. It is suggested to adopt inherently sensorless designs such that two-way coupling between torque estimation and speed estimation is avoided. For induction motors, it turns out the unmodelled voltage in the active flux dynamics reveals current flowing in rotor bars and can be further modelled, for which the solutions to regeneration instability problem are discussed, and change of states is recommended to attain global stability. Finally discussed are the results of slow reversal test, where local weak observability of ac motors can be potentially preserved.

To insure a stable and reliable operation of the existing and future grids, it is important to study the stability of the power electronic based converters’ controls which can replace synchronous generation. Most existing studies use a Thévenin equivalent model of the grid to test the grid following controls. The present article demonstrates the limits of these studies. An alternative setup is proposed and is shown to provide more insight into the underlying mechanisms of the grid following control behaviour in weak grids, using a linearized state space model. These insights are exploited to propose further improvements to the control. These improvements are shown to extend the stability of the control while reducing the interactions among converters.

This is a preprint version of the manuscript submitted to IEEE on June 4, 2020. This paper gives an overview of the Artificial Intelligence (AI) applications for power electronic systems. The three distinctive life-cycle phases, design, control, and maintenance are correlated with one or more tasks to be addressed by AI, including optimization, classification, regression, and data structure exploration. The applications of four categories of AI are discussed, which are expert system, fuzzy logic, metaheuristic method, and machine learning. More than 500 publications have been reviewed to identify the common understandings, practical implementation challenges, and research opportunities in the application of AI for power electronics.

DC-DC converters are key topologies in many industries such as renewable energy, transportations, utility, and perform an increasingly important role in the power electronics. The research of the modulation strategies with efficiency improvement is therefore critical for the DC-DC converters. As one of the most classic DC-DC converters, the dual active bridge (DAB) converter has attracted more and more attention in the utilization of the isolated DC-DC power conversion systems, due to its good performance. Over the past decade, many advanced techniques have been used to promote the efficiency for the DAB converter. Aiming to provide comprehensive analysis of different optimizing techniques for the DC-DC converters, this paper gives an overview of the efficiency optimization modulation strategies with a case study on the DAB converter. Firstly, typical phase-shift modulation methods and variable frequency modulation methods for the DAB converter are analyzed. After that, the application of three categories of optimization techniques are described, which including numerical optimization methods, metaheuristic methods and reinforcement learning (RL) methods. On this basis, this paper provides corresponding suggestions for further improving the operation efficiency of the DAB converters.