Huafeng Cai

Against the backdrop of the global energy structure accelerating its transition towards a clean and low-carbon model, rooftop-distributed photovoltaic (PV) systems are playing an increasingly prominent strategic role in urban energy supply systems, owing to their notable advantages such as environmental friendliness and high spatial utilization efficiency. Consequently, they are becoming a critical pillar in advancing urban energy transformation and enhancing sustainable development. This paper aims to explore deep learning-based techniques for assessing the potential of large-scale distributed PV installations. To accurately evaluate their dynamic power generation capability, a hybrid prediction model integrating variational mode decomposition (VMD), the mutual information (MI) method, and a cascaded xLSTM-Informer network is proposed. Firstly, the model preprocesses key meteorological sequences using VMD, decomposing them into modal components of different frequencies. Subsequently, the MI method is employed to extract critical sequences. Then, the xLSTM module is utilized to learn the long-term complex dependencies between meteorological conditions and PV power output, while the Informer network captures key global temporal patterns, achieving high-precision time-series forecasting of PV generation. Finally, employing the forecasted time-series power curve as the core input, a comprehensive analytical framework for PV installation potential is constructed, integrating assessments of technical feasibility, economic viability, and environmental performance. This framework aims to scientifically estimate the admissible installed capacity and system value of distributed PV systems, thereby providing a dynamic basis for decision-making in urban planning.

The ultrasonic power supply constitutes the core component of an ultrasonic welding system, and its main function is to convert the industrial frequency electricity into resonant high-frequency electricity in order to achieve mechanical energy conversion. However, factors such as changes in ambient temperature or component aging may cause the resonant frequency of the transducer to drift, thus detuning the resonant system and seriously affecting system performance. Therefore, an ultrasonic welding system requires high-frequency tracking in real time. Traditional frequency tracking methods (such as acoustic tracking, PID control, etc.) have defects such as poor stability, narrow bandwidth, or cumbersome parameter setting, making it difficult to meet the demand for fast tracking. To address these problems, this study adopts a T-matching network and utilizes sliding mode control for frequency tracking. In order to solve the problems of slow convergence and obvious jitter in sliding mode control (SMC), a Hippopotamus Optimization (HO) algorithm is introduced to simulate hippopotamuses’ group behavior and predation mechanisms, thereby optimizing the control parameters. It is verified through simulation that the SMC algorithm optimized by the HO algorithm (HO-SMC) is able to suppress frequency drift more effectively and demonstrates the advantages of fast response, high accuracy, and strong robustness in the scenario of sudden load changes.

Accurate short-term power load forecasting (STPLF) is critical for balancing electricity supply–demand and ensuring grid reliability. To address the challenges of fluctuating power loads and inaccurate predictions by conventional methods, this paper presents a novel hybrid framework combining Variational Mode Decomposition (VMD), Long Short-Term Memory (LSTM), and the Improved Sparrow Search Algorithm (ISSA). First, the power load series is decomposed into intrinsic mode functions (IMFs) via VMD, where the optimal decomposition order K is determined using permutation entropy (PE). Next, the decomposed IMFs and meteorological covariates are reconstructed into feature vectors, which are then input into the LSTM network for component-wise forecasting, and, finally, the prediction results of each component are reconstructed to obtain the final power load prediction result. The Improved Sparrow Search Algorithm (ISSA), which integrates piecewise chaotic mapping into population initialization to augment the global exploration capability, is employed to fine-tune LSTM hyperparameters, thereby enhancing the prediction precision. Finally, two case studies are conducted using Australian regional load data and Detu’an City historical load records. The experimental results indicate that the proposed model achieves reductions of 73.03% and 82.97% compared with the VMD-LSTM baseline, validating its superior predictive accuracy and cross-domain generalization capability.

In order to combat the difficulty of fault feature extraction and fault recognition in the field of bearing fault diagnosis, a bearing fault diagnosis method based on improved variational mode decomposition (VMD) and parallel hybrid neural network is proposed, which combines reweighted kurtosis (RK) with variable mode decomposition (VMD) and uses reweighted kurtosis as the evaluation index to select the decomposition times of variational mode decomposition, while removing part of the interference in the fault signal and retaining its impact characteristics. Afterwards, the processed fault data set is brought into a parallel hybrid neural network model with a global average pooling layer (GAP) for feature extraction, feature fusion, and fault classification. The parallel hybrid neural network model can extract fault signal features more comprehensively and improve the accuracy of fault diagnosis, while the global average pooling layer can speed up the training and testing. Experiments on the Xian Jiao tong University (XJTU) and Case Western Reserve University (CWRU) bearing public data sets show that the diagnosis accuracy reaches 99.72% and 99.73%, respectively, indicating that the method has good fault diagnosis accuracy and better diagnosis performance compared with other models.

A split-phase three-level LCL grid-connected inverter is proposed to match the single-phase three-wire split-phase output power grids in countries such as those in North America. However, influencing factors such as grid impedance and background harmonics in non-ideal power grids may lead to distortion and even instability of the output waveform of the grid-connected inverter. To address the aforementioned issues, through a stability analysis of the dual-feedback system of inverter-side current control and capacitor current active damping, a composite active damping strategy is put forward to enhance the stability of the LCL grid-connected inverter. This composite active damping strategy encompasses a standardized method for designing the robust capacitor current feedback coefficient and a method of embedding leading-phase correction to improve system stability. The strategy proposed in this paper is more streamlined and standardized when calculating the capacitor current feedback coefficient, enabling the system to operate stably under a wide range of grid impedance variations. Moreover, an analysis of the mechanism by which grid background harmonics affect the grid-connected current waveform is conducted, and the PR controller is replaced with a multiple proportional-resonant (MPR) controller. The MPR controller can achieve infinite gain at specific harmonics, thereby suppressing specific low-order harmonics in the grid and reducing the total harmonic distortion (THD) of the grid-connected current. Finally, the effectiveness of the proposed control strategy is verified on a 12 kW experimental platform. The results indicate that, compared with the inverter-side current feedback active damping (ICFAD) method, the composite active damping strategy proposed in this paper exhibits stronger robustness, and the added MPR controller significantly enhances the anti-interference ability of the grid-connected inverter against grid harmonics.

Due to the difficulty in achieving a high-power output with a single converter, parallel converters are widely used in high-power applications. However, inconsistency in the output voltage feedback coefficients of individual converters and the associated dispersion in parallel systems often lead to unbalanced current sharing during transient processes, such as load disturbances. This imbalance can result in certain converters being overloaded during transients, leading to premature shutdown. Subsequently, the load on the remaining converters increases, further aggravating the imbalance and triggering additional shutdowns in a domino effect, which may, ultimately, cause the entire parallel converter system to shut down. To address this issue, this study focuses on parallel phase-shift full-bridge systems, analyzing the intrinsic mechanism by which feedback coefficient dispersion affects transient current sharing. A droop control strategy with improved transient virtual impedance is proposed to enhance the current sharing during transients. A simulation and experimental results demonstrate that the proposed strategy significantly improves current sharing during transient processes, effectively enhancing the dynamic performance and reliability of the system.

There are several complex and unpredictable aspects that affect the power grid. To make short-term power load forecasting more accurate, a short-term power load forecasting model that utilizes the VMD-Crossformer is suggested in this paper. First, the ideal number of decomposition layers was ascertained using a variational mode decomposition (VMD) parameter optimum approach based on the Pearson correlation coefficient (PCC). Second, the original data was decomposed into multiple modal components using VMD, and then the original data were reconstructed with the modal components. Finally, the reconstructed data were input into the Crossformer network, which utilizes the cross-dimensional dependence of multivariate time series (MTS) prediction; that is, the dimension-segment-wise (DSW) embedding and the two-stage attention (TSA) layer were designed to establish a hierarchical encoder–decoder (HED), and the final prediction was performed using information from different scales. The experimental results show that the method could accurately predict the electricity load with high accuracy and reliability. The MAE, MAPE, and RMSE were 61.532 MW, 1.841%, and 84.486 MW, respectively, for dataset I. The MAE, MAPE, and RMSE were 68.906 MW, 0.847%, and 89.209 MW, respectively, for dataset II. Compared with other models, the model in this paper predicted better.

Under light load conditions, the phase-shifted full-bridge (PSFB) converter often has difficulty in realizing the zero-voltage switching (ZVS) of the lagging-leg by relying on the energy of its resonant inductor; however, for the series-parallel PSFB converter applied in high-power applications, the lagging-leg still has the problem of difficult realization of ZVS. Based on this, the paper analyzes the reasons why the series-parallel PSFB converter has difficulty in achieving ZVS for the lagging-leg under light and heavy loads. Under interleaved control, the ZVS of the lagging-leg over the full load range is realized by adding an auxiliary LC branch at the midpoint of the lagging-leg of both submodules. Based on the double-bridge input-parallel-output-series (IPOS) PSFB converter, analyzing the working principle of the circuit after adding the auxiliary LC branch and extending it to the series-parallel PSFB converter. The design requirements of the LC auxiliary branch of the dual-bridge series-parallel PSFB converter are given and the effects of the LC auxiliary branch on the module operating state and device stress are analyzed. On this basis, an extension is carried out to give the working principle and design method of the auxiliary LC branch of the N-bridge series-parallel PSFB converter. Finally, a 100 kW Matlab/Simulink simulation model verifies the superior performance of the proposed LC auxiliary branch to realize the lagging-leg ZVS of the series-parallel PSFB converter under light and heavy loads and achieves a 1.09% peak efficiency improvement at rated load.

The application of high-power DC equipment further increases the power supply scale of DC systems. But, it is difficult for a single converter to support high transmission power, so multiple converters must operate in parallel for efficient power transmission. In a parallel system comprising many IPOS phase-shifting full-bridge converters, current sharing can be realized via droop control. However, the stability of the parallel system using current-sharing control will appear poor in light load conditions, so it is necessary to analyze the stability of parallel systems in light load conditions. Firstly, a single IPOS phase-shifted full-bridge control system is modeled; on this basis, the state space model of the n-module paralleled IPOS phase-shifted full-bridge converters system is derived. Then, the influence of load power and the number of parallel IPOS phase-shifted full-bridge converters on the system stability is analyzed via eigenvalue analysis, and an optimal control strategy based on a particle swarm optimization algorithm is proposed. The control parameters are optimized for the parallel system of eight IPOS phase-shifted full-bridge converters. Finally, the above results are simulated to verify the accuracy of the stability analysis and the feasibility of the optimized control strategy.