The energy-stored quasi-Z-source inverter (ES-qZSI) has attracted much attention for photovoltaic (PV) power generations, due to its capability to stabilize the PV power fluctuations with simple structure and other advantages of Z-source inverter. The improved dual-mode (DM) ES-qZSI is able to support all-weather operation even at night or cloudy days when PV power is extremely low. However, the traditional proportional–integral (PI) based control suffers from complicated controller design and poor dynamic response during mode transition, due to two sets of PI control required for the daytime and night operation modes. In order to overcome that, in this article, we propose a model predictive control strategy for the DM-ES-qZSI PV power system. The system predictive models in both day and night operating modes are derived. The control strategy is disclosed to ensure high performance of the system, through calling the predictive models and defined cost functions of the two modes within a single control loop. Simulation and experiment are carried out to verify the effectiveness of the proposed control strategy.

Four-switch buck-boost converter supports both voltage step-up and step-down functionalities, but it suffers from mode transfer challenge that would need reliable mode detection when designed to operate in multi different modes. In this paper, a novel control method based on model predictive current control is proposed for such converter with inherent smooth mode transfer capability without extra design on mode detection and transfer scheme. Modulator and mode detection are replaced by an optimization process through cost function. This largely simplifies the design and makes it easily implemented. Seamless transfer between buck and boost modes is achieved with many other system level benefits. Simulation and experimental results are provided to verify the effectiveness of proposed method for the four-switch buck-boost converter.

Active ripple decoupling technique of the single-phase inverter is a popular topic to minimize the dc-link capacitance. However, the existing control methods are based on tracking sinusoidal or predetermined voltage waveforms of the compensation capacitor, assuming the inverter outputs are pure sinusoidal voltage and current. Therefore, the performance of existing methods degrades when the inverter output voltage and current are not purely sinusoidal. Furthermore, the limited dynamic performance threatens the safety of dc-link capacitor when the load changes. This happens, because the inrush ripple power is injected into dc link with small capacitance and the dc-link voltage will suddenly rise up when the ripple power is not buffered during transients. In this paper, a direct instantaneous power predictive control is proposed for the decoupling circuit to buffer ripple power of the single-phase inverter, which combines instantaneous ripple power control with model predictive control to overcome the issues above. The proposed method tracks the instantaneous ripple power rather than voltage or current waveforms. In this way, it can fully buffer all ripple powers in the system even for distorted output voltage and current of the inverter and enables the full utilization of storage capacitor. In addition, model predictive control makes the proposed method have fast dynamic and perfectly compensate ripple power during transients and steady states. The buck-type active ripple decoupling circuit is chosen to implement the proposed method after comparing with another typical decoupling topology. The proposed method is also compared with conventional method using proportional-integral-resonant regulator to track the predetermined capacitor voltage waveform. Experimental tests verify the theoretical analysis and the proposed control method.

The application of smart meters was delayed, leading to sparse power load data collection in industrial and commercial buildings, often encompassing only days to a few months of data. In contrast, deep learning models necessitate extensive data sets, spanning several years. To bridge this data deficit, this paper proposes a hybrid forecasting method combining Time-series Generation Adversarial Network (TimeGAN) with a Convolutional Neural Network (CNN)-enhanced Long Short-Term Memory (LSTM) neural network. Initially, the scarce data set is expanded using synthetic data derived from TimeGAN. Subsequently, the comprehensive data undergoes CNN filtering, optimizing the information extraction and expediting the forecasting network. The extracted information is then channeled into LSTM network for load forecasting. A case study is carried out using two-month power load data from four different industrial and commercial buildings types, underpins this methodology. Comparative analysis reveals that the proposed model effectively improves short-term power load forecasting accuracy.

Wide band-gap field-effect power devices, such as gallium nitride (GaN) high electron mobility transistors are being widely used for a better system-level performance as a result of superior figure-of-merits in power-conversion application. But they could also suffer from more severe bridge-leg crosstalk issue in fast-switching operation due to their special device features. To suppress the bridge-leg crosstalk, an explicit model and theoretical analysis which could guide collaborative optimization of multiparameter is required. However, the superposition of two noise excitations ( di/dt and dv/dt ) and high order circuit structure bring difficulties to modeling and analysis. By the physical behaviors of different switching stages, the generation of crosstalk voltage is analyzed. This article proposes an analytical model of crosstalk voltage, with the explicit representation of parasitic parameters. Based on the model, an exhaustive investigation into the correlation of parameters on the crosstalk voltage is conducted. Further, a scheme for multiparameters collaborative optimization is presented, the safe operational area for crosstalk suppression has been determined. The superiority of the proposed model and the effectiveness of the optimization scheme are verified with simulations and experiments on a double pulse test platform. The model and analysis method can guide the selection of GaN devices and the design of parameters, which is convenient for the pre-test of printed circuit board before power on .

High frequency (HF) ac linked converter functions well to interface between buses with different voltage types. Using a three-phase-to-single-phase matrix converter, a single-phase cyclo converter, and a HF transformer, we can compose a HF ac linked converter to interface between three-phase ac grids and single-phase ac or dc distributed generations (DGs). In this paper, a novel space vector modulation (SVM) method is proposed for such kind of converter. The proposed SVM method converts the mains three-phase ac voltage to a HF ac voltage at the primary side of transformer, and then unfolds the HF ac voltage to a single-phase ac or dc voltage at the secondary side of transformer. Without need to compute separate switching signals for the primary and secondary converters and then coordinate their operations elaborately, the proposed SVM method can be easily implemented by incorporating the HF chopping into the SVM of traditional indirect matrix converter. Therefore, no complicated duty cycle computation or elaborate switching states combination is required. Simulation and hardware-in-loop implementation demonstrate the validity of the proposed method.