A battery-energy-stored quasi-Z-source cascaded multilevel inverter (qZS-CMI)-based photovoltaic (PV) power generation system combines advantages of a qZS inverter, a CMI, and a battery energy storage system. However, unbalanced battery state of charge (SOC) between cascaded H-bridge inverter modules will degrade an entire system's performance and shorten battery lifespan. This paper proposes a control method to balance battery SOCs of all modules, no matter the intermittent states of each module's PV power. The method is on the basis of battery SOCs, SOC limits of each module, and the total power injected into the power grid. Simulation and experimental results verify the proposed control method that ensures identical SOCs for the battery-energy-stored qZS-CMI PV system.

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.