Three-level converters feature with low switching loss and small filter size. In order to realize high power density design for three-level converters, SiC MOSFETs may be selected instead of using Si IGBTs. However, all SiC MOSFETs based converters suffer from extreme high total cost. In this paper, a SiC MOSFET & Si device hybrid ANPC converter is proposed. It consists of four Si active switches and only two SiC MOSFETs. Thus, it has lower total cost compared to all SiC MOSFETs based ANPC converter. Furthermore, a dedicated modulation scheme is proposed to completely move all the switching events from Si devices to SiC MOSFETs by using redundant switching states. As a result, the switching losses are significantly reduced and extremely high efficiency is achieved. The proposed converter has fully utilized the low switching loss advantage of SiC MOSFET and the low-cost advantage of Si devices, which shows significant superiority in high-end grid-connected inverter and rectifier applications.

Wireless power transfer (WPT) is a promising technology for supplying power to various applications with no physical contacts. Recently, bidirectional wireless power transfer (BD-WPT) systems are gaining popularity for grid-to-vehicle and vehicle-to-grid applications which essentially require power transfer in both directions. However, BD-WPT systems are complex in nature, and require sophisticated control strategies to provide synchronization as well as to regulate the power flow between two sides. This paper proposes a new controller that uses measured active power (P) and reactive power (Q) at the resonant network of BD-WPT systems to regulate the power flow in both directions while providing synchronization between two sides without a dedicated communication interface for controlling power transfer. The controller, located on the pickup side, is applicable to BD-WPT systems with either single or multiple loads and ensures that the volt-ampere rating of the pickup converter is at lowest for battery charging, by keeping the reactive power at the resonant tank intake by the load side minimum. Experimental results obtained from a 1-kW prototype system show good agreement with simulated results, validating that the proposed controller can be used to regulate the power flow in BD-WPT systems.

This article considers fault detection in the case of a three-phase three-wire (3P3W) inverter, when only two current sensors are used to save cost or due to a faulty current sensor. With two current sensors, there is no current method addressing the diagnosis of both IGBT open-circuit (OC) faults and current sensor faults. In order to solve this problem, this article proposes a method which innovatively combines two kinds of diagnosis variables, line voltage deviations and phase voltage deviations. The unique faulty characteristics of diagnosis variables for each fault are extracted and utilized to distinguish the fault. Using an average model, the method only needs the signals already available in the controller. Both IGBT OC faults and current sensor faults can be detected quickly in inverter mode and rectifier mode, so that the converter can be protected in a timely way to avoid further damages. In addition, error-adaptive thresholds are adopted to make the method robust. Effects such as system unbalance are analyzed to ensure that the method is robust and feasible. Simulation and experimental results are used to verify and validate the effectiveness of the method.

To combine the advantages of both model-driven and data-driven methods, this paper proposes a model-data-hybrid-driven (MDHD) method to diagnose open-switch faults in power converters. This idea is based on the explicit analytical model of converters and the learning capability of artificial neural network (ANN). The process of the method is divided into two parts: offline model analysis and learning, and online fault diagnosis. For both parts, model-driven and data-driven are combined. With the model information and data-based learning capability, a fast diagnosis for various operating conditions can be achieved without high computation burden, tricky threshold selection and complex rulemaking. This can greatly contribute to the practical application. The open-switch fault diagnosis in a two-level three-phase converter is studied for method validation. For this converter, an ANN is trained with two input elements, seven output elements, and two neurons in the hidden layer. Experimental results are given to demonstrate good performance.

The circulating current of modular multilevel converter (MMC) has a great impact on the power losses, the submodule capacitor voltage ripples and the rating of power devices. Existing analysis of circulating current has considered the capacitor voltage. In this paper, the effect of semiconductor devices on the circulating current is further presented. In order to realize the different circulating current injection targets under different conditions, a unified on-line calculation scheme by using the instantaneous information of MMC is proposed to generate the circulating current injection references. It avoids detecting the amplitude and phase of the output current or using an extensive lookup table. Besides, a concise analysis model is built and it reveal the impact on the capacitor voltage ripple and the RMS value of arm current with different injection references, thus guide the determination of the injection ratio. Experimental results based on a three-phase MMC prototype with 6 submodules per arm are presented to demonstrate the analysis of circulating current and the proposed circulating current injection references generating method is verified by different circulating current control targets with different loads.

This paper presents a model-based method to diagnose single and multiple transistor open-circuit faults in grid-tied three-phase voltage-source inverters (VSIs). The method is based on calculated average bridge arm pole-to-pole (PTP) voltages and error-adaptive thresholds. Only existing signals for close-loop control are needed, thus this method can be embedded in system easily without extra sampling and circuits. Average PTP voltage deviations are chosen as diagnostic variables, which show considerable distinction quickly after fault. Consequently, fast fault diagnosis speed can be achieved. The fault diagnosis time for single fault can be as short as two switching periods. Besides, diagnostic variables show the same faulty characteristics in inverter mode and rectifier mode, thus this method is effective in both modes. Moreover, for the first time the variation of inductance caused by conducted current is considered to obtain a more accurate variable-inductance model, and the thresholds are updated according to mathematically estimated diagnostic variable calculation errors from sampling error, inductance error, dead time and delay time, which maintains high robustness as well as fast speed. Finally, the effectiveness of the proposed method is validated with experiments.