As key component for the flexible dc distributed power system, the dual active bridge (DAB)-converter-based solid-state transformer (SST) with high efficiency for a wide operating range is essential. However, with the traditional phase-shift control, high backflow power and current stress will significantly affect the conversion efficiency. In this paper, the backflow power characteristics in both sides of DAB-based SST converters are comprehensively analyzed. On this basis, complete transmission power, backflow power, and peak current mathematical models are established. Then, a minimum-backflow-power-based extended-phase-shift control strategy is proposed with the determination of optimal phase-shift pairs by using the Karush-Kuhn-Tucker function for various scenarios. The backflow power and current stress curves with different algorithms are compared. It shows the proposed control can improve the output power regulation flexibility, minimize the backflow power, and improve the efficiency in wide operating range. Finally, a DAB-based SST prototype was developed and the experimental results verified the effectiveness of the proposed control strategy.

Model predictive control (MPC) suffers from high computational burden and cumbersome tuning of weighting factors especially for three-level three-phase inverters. Here a fast finite-switching-state model predictive control (FSS-MPC) algorithm without weighting factors is proposed. Firstly, the deadbeat (DB) solution is used to construct the voltage vector reference. Then, the T-type three-level three-phase inverter topology is selected and voltage vectors are carefully tuned in order to minimize the neutral-point (NP) voltage fluctuation. The voltage vectors that are far from the desired voltage vector reference and go against balancing the NP voltage are abandoned. Thus, only three effective voltage vectors instead of totally 27 vectors are required in the implementation of the proposed FSS-MPC algorithm, which saves computation time up to 46.7%. Furthermore, the proposed algorithm makes the tuning of the weighting factor unnecessary, which simplifies the practical implementation and improves the portability of the algorithm. Finally, an experimental prototype was established and main results including the steady-state and dynamic performance were presented to validate the effectiveness of the proposed algorithm.

DC offset in the input of phase-locked loops (PLLs) is a challenging problem since it will result in fundamental frequency oscillations in the estimated phase and frequency. In this paper, a comprehensive analysis and performance evaluation of several advanced second-order generalized integrator (SOGI)-based PLL methods in enhancing the dc offset rejection capability for single-phase grid-connected power converters is presented. These methods include the cascade SOGI, modified SOGI, $\alpha \beta $ -frame delayed signal cancellation (DSC), complex coefficient filter, in-loop dq-frame DSC, notch filter, and moving average filter-based SOGI-PLL. Main characteristics and design aspects of these methods are presented. Main performance indexes, such as the setting time, frequency or phase errors are defined and these methods are systematically compared under various scenarios with both numerical and experimental results.

Solar forecasting is one of the most promising approaches to address the intermit PV power generation by providing predictions before upcoming ramp events. In this paper, a novel multi-step forecasting (MSF) scheme is proposed for PV power ramp-rate control (PRRC). This method utilizes an ensemble of deep ConvNets without additional time-series models and exogenous variables, thus more suitable for industrial applications. The MSF strategy can make multiple predictions in comparison with a single forecasting point produced by a conventional method while maintaining the same high temporal resolution. Besides, stacked sky images that integrate temporal-spatial (ST) information of cloud motions are used to further improve the forecasting performance. The results demonstrate a favorable forecasting accuracy in comparison to the existing forecasting models with the highest skill score of 17.7%. In the PRRC application, the MSF-based PRRC can detect more ramp-rates violations with a higher control rate of 98.9% compared with the conventional forecasting based control. Thus, the PV generation can be effectively smoothed with less energy curtailment on both clear and cloudy days using the proposed approach.

This paper deals with an optimized 3-level modulated phase shift control using particle swarm optimization (PSO) strategy based on the unified phasor analysis with aim to improve the efficiency of the bidirectional dual active bridge (DAB) converter for the whole operation range. A unified mathematical model based on Fourier transform is built for the DAB converter. All possible operation states under 3-level modulated phase shift control are covered. Accurate complex mathematic expressions for the inductor current, the transmission power and the reactive power are obtained. Both modulus and angle variables are illustrated with respect to the inner and outer phase shift angle with the phasor diagram. The proposed method is able to achieve the minimum reactive power under 3-level modulated phase shift control by obtaining the optimal phase-shift angles directly. The cumbersome process of the optimal operation mode selection for different voltage conversion ratio and load conditions in conventional methods is overcomes successfully, thus greatly simplified the theoretical calculation and implementation difficulty. Simulation and experimental results in terms of the reactive power, soft-switching range, and efficiency are provided to verify the practical feasibility of the proposed method for the bidirectional DAB converters.