Reliability remains an issue for the Si/SiC hybrid switch adopting the conventional switching strategy of the internal SiC MOSFET that turns on earlier, and off later. Such issue is attributable to the overcurrent stress under the heavy load operating condition, which adversely affects the SiC MOSFET during the gate delay time. To solve this problem without increasing the extra power loss, a novel variable-frequency current-dependent switching strategy combining the variable switching pattern strategy, and the variable pulsewidth modulation (PWM) frequency strategy is proposed. Variable switching pattern strategy can avoid the overcurrent stress of the SiC MOSFET at the heavy load operating condition, and the designed optimal delay time in different switching patterns can achieve the compromise between the excellent reliability, and the power loss of Si/SiC hybrid switch. Variable PWM frequency strategy can effectively reduce the switching loss of the Si/SiC hybrid switch by decreasing the switching frequency around the peak current region. An Si/SiC-hybrid-switch-based single-phase inverter platform is constructed and tested. Test results show that the power loss of the single-phase inverter adopting such switching strategy outperforms the current-dependent switching strategy with 9.4% reduction of power loss, and overcurrent stress of SiC MOSFET is avoided.

Varying gate delay time of the Si insulated-gate bipolar transistor/SiC MOSFET hybrid switch is the key to achieving high efficiency of Si/SiC-hybrid-switch-based power converters because of time-varying characteristics of its junction temperature and operation current. Based on the swarm intelligent algorithm, a novel adaptive delay-time control method of the Si/SiC hybrid switch in an inverter is proposed to achieve higher efficiency than the traditional fixed delay time. In addition, by evaluating the fitness values, the proposed method can easily achieve this goal without establishing the physics-based analytical model of device loss and additional hardware support. The platform of the Si/SiC-hybrid-switch-based single-phase inverter is established and tested, and the particle swarm optimization algorithm is taken as example. Experimental results demonstrate that the proposed technique yields 6.2% reduction in the total loss of the single-phase inverter compared to a fixed delay time.

A novel droop control with coupling compensation and inertia is proposed for a microgrid to achieve high dynamic performance and stability of distributed generation (DG) units. The coupling compensation scheme consists of angular frequency deviation compensation and voltage deviation compensation. They can reduce the influence of uncontrolled power coupling. The inertia scheme consists of angular frequency inertia and voltage inertia. They can slow down the change rates of the angular frequency and voltage and reduce their fluctuation magnitudes. The combination of this novel droop control method and virtual impedance can help achieve accurate power sharing. An improved particle swarm optimization (PSO) algorithm with multi-swarm and multi-velocity (MMPSO) is also proposed to optimize the parameters of the coupling compensation and inertia with high precision and high reliability. A simulated model and experiment platform of a microgrid are set up for testing, and the simulation and experimental results validate the excellent performance of the MMPSO and the effectiveness of the novel droop control method.

The fault diagnosis of the inverter is fundamental to energy intelligence. Due to the complex characteristics of the inverter (e.g., high-dimensional decision and poor stability), it is challenging to solve the problem using traditional fault diagnosis methods. Recently, artificial intelligence (AI)-based approaches have emerged as the most promising methods. However, they often require to set hyperparameters manually, which hinders further AI-based applications in fault diagnosis of inverters. To fill the gap, we propose an inverter fault diagnosis method using fast Fourier Transform (FFT) and evolutionary neural network. This method combines the amplitude of low-frequency harmonic component of the three-phase inverter output current which is obtained by FFT and the average value in a period of three-phase inverter output current into the fault eigenvector. This method uses an evolutionary neural network trained by combining genetic algorithm (GA), ant colony optimization (ACO) algorithm and Back-propagation (BP) algorithm to realize fault diagnosis. This method can effectively resist noise interference and reduce the number of independent variables in the part of feature extraction, so that it can simplify the network model. In addition, this method can avoid the network training from trapping in local optima in the part of fault classification, with high accuracy and fast response speed. The experimental results show that the proposed algorithm and method of fault feature extraction can effectively detect and locate the insulated-gate bipolar transistor (IGBT) with open circuit (OC) fault in three-phase inverter, and can be applied to online monitoring.