High step-up dc–dc converters are widely used in new energy applications such as photovoltaic cell, fuel cell, dc microgrid, etc. Active switched-inductor (ASL) converter has simple operation and low stress on switches. To further increase the voltage gain, some SC (switched-capacitor)/ASL step-up converters have been proposed recently. However, these SC/ASL step-up converters have voltage oscillation on switches, which leads to the voltage stress on switches is higher than the theoretical value. In this article, a novel high step-up SC/ASL converter is proposed. Compared with other SC/ASL step-up converters, the proposed converter has lower voltage stress on the switches, output diodes, and output filter capacitors, and the efficiency is higher. In addition, the proposed converter can achieve self-voltage balancing on switches. This article analyzes the proposed converter from operation principle, continuous conduction mode (CCM) analysis, CCM operation with unbalanced inductors, input current ripple analysis, discontinuous conduction mode and boundary conduction mode analysis, switches self-voltage balancing characteristic, voltage stress, current stress, comparison analysis, and design considerations. Finally, a 200 W, 25–45 V/380 V, 50 kHz experimental prototype has been established in the laboratory to evaluate the proposed converter, which reached a peak efficiency of 97.3%.

The larger capacity of power converters increases with the size of wind turbines over the years, which implies more failures occur in the components of power converter. Meanwhile, the strategies of fault diagnosis of power converter in wind turbine are still under discussion and research. The main purpose of this paper is proposing a strategy containing wavelet transform, feature analysis, judgment and back propagation neural network (BPNN) classification (WT-FA-JD-BP) to identify the single and double power components open-circuit faults accurately, happen in grid-side converter (GSC) of permanent magnet synchronous generator (PMSG) wind turbine systems. First, as the original signals, the three-phase bridge legs voltage of the GSC are collected under different faults. Second, wavelet transform is used to decompose and reconstruct the signals. Third, a new method includes feature analysis and judgment is conducted to amplify the divergence of data obtained by wavelet transform. Finally, the data are used as the inputs of BPNN for decision-making and classification. The simulation and experimental results show that the proposed strategy can classify the single and double open-circuit faults, and the accuracy is higher than that without data feature analysis and judgment.

The decomposition effect of variational mode decomposition (VMD) mainly depends on the choice of decomposition number $K$ and penalty factor $\alpha$. For the selection of two parameters, the empirical method and single objective optimization method are usually used, but the aforementioned methods often have limitations and cannot achieve the optimal effects. Therefore, a multi-objective multi-island genetic algorithm (MIGA) is proposed to optimize the parameters of VMD and apply it to feature extraction of bearing fault. First, the envelope entropy ($E_e$) can reflect the sparsity of the signal, and Renyi entropy ($R_e$) can reflect the energy aggregation degree of the time-frequency distribution of the signal. Therefore, $E_e$ and $R_e$ are selected as fitness functions, and the optimal solution of VMD parameters is obtained by the MIGA algorithm. Second, the improved VMD algorithm is used to decompose the bearing fault signal, and then two intrinsic mode functions (IMF) with the most fault information are selected by improved kurtosis and Holder coefficient for reconstruction. Finally, the envelope spectrum of the reconstructed signal is analyzed. The analysis of comparative experiments shows that the feature extraction method can extract bearing fault features more accurately, and the fault diagnosis model based on this method has higher accuracy.

A phase-shifted full-bridge (PSFB) converter is widely used in various applications due to its soft-switching characteristics. However, one of the recognized shortcomings of the PSFB converter is that it cannot simultaneously have a wide soft switching range and a small secondary-side duty cycle loss. The shortcoming hinders the high frequency and high efficiency of the converter. In this article, the change of the energy demand of the converter on the external series inductor with input voltage and load is analyzed. On this basis, a variable saturation inductor with controllable energy is proposed to meet the energy demand and solve the problem of core heating. The controllable change characteristics of the inductor energy are studied directly instead of the controllable change of the inductance value. By adjusting the inductor energy, the PSFB can not only achieve soft switching under light loads, but also reduce duty cycle loss under heavy loads. Theoretical analysis and experimental verification are carried out on the soft switching and duty cycle loss of the converter. Since the transformer ratio can be increased due to thesmaller duty cycle loss. The efficiency of the converter can be further improved.

A novel multi-objective robust optimization model of an integrated energy system with hydrogen storage (HIES) considering source–load uncertainty is proposed to promote the low-carbon economy operation of the integrated energy system of a park. Firstly, the lowest total system cost and carbon emissions are selected as the multi-objective optimization functions. The Pareto front solution set of the objective function is applied by compromise planning, and the optimal solution among them is obtained by the maximum–minimum fuzzy method. Furthermore, the robust optimization (RO) approach is introduced to cope with the source–load uncertainty effectively. Finally, it is demonstrated that the illustrated HIES can significantly reduce the total system cost, carbon emissions, and abandoned wind and solar power. Meanwhile, the effectiveness of the proposed model and solution method is verified by analyzing the influence of multi-objective solutions and a robust coefficient on the Chongli Demonstration Project in Hebei Province.

A variety of supervised learning methods using numerical weather prediction (NWP) data have been exploited for short-term wind power forecasting (WPF). However, the NWP data may not be available enough due to its uncertainties on initial atmospheric conditions. Thus, this study proposes a novel hybrid intelligent method to improve existing forecasting models such as random forest (RF) and artificial neural networks, for higher accuracy. First, the proposed method develops the predictive deep belief network (DBN) to perform short-term wind speed prediction (WSP). Then, the WSP data are transformed into supplementary input features in the prediction process of WPF. Second, owing to its ensemble learning and parallelization, the random forest is used as supervised forecasting model. In addition, a data driven dimension reduction procedure and a weighted voting method are utilized to optimize the random forest algorithm in the training process and the prediction process, respectively. The increasing number of training samples would cause the overfitting problem. Therefore, the k-fold cross validation (CV) technique is adopted to address this issue. Numerical experiments are performed at 15-min, 30-min, 45-min, and 24-h to indicate the superiority and signal advantages compared with existing methods in terms of forecasting accuracy and scalability.