This study proposes one-sided self-starting truncated EWMA (SST-EWMA) control charts for effective high-quality process monitoring in situations where extensive in-control time-between-events (TBE) observations are unavailable. By constructing a pivot quantity and establishing variable mappings, a self-starting framework specifically tailored for Gamma distributed TBE observations is developed. The integration of a variable truncation mechanism into this framework further enhances sensitivity to small to moderate process shifts. To investigate the detection properties of the proposed schemes, simulation were conducted to examine the effects of the shape parameter alpha, the number of reference TBE observations M, and the smoothing parameter lambda on the average time to signal (ATS). Based on the simulation results, guidelines are provided for achieving ATS performance comparable to that of the corresponding known-parameter schemes. Comparative analysis demonstrates that, although slightly inferior to the one-sided TEWMA TBE charts under known parameters, the proposed charts exhibit superior adaptability in scenarios with scarce TBE data, and also outperform the existing self-starting EWMA TBE chart, validating the effectiveness of the variable truncation mechanism. Finally, two case studies are presented to illustrate the practical implementation of the proposed control charts in industrial engineering applications.

In this letter, we propose a hierarchical projection Krylov subspace basis function method (HP-KSBFM) with LSMR-Tikhonov regularization to address the memory and convergence limitations of traditional block-Krylov methods. The method constructs a compact basis set through a hierarchical structure with error-controlled truncation. By integrating LSMR-Tikhonov regularization, the method enhances solution stability and convergence, especially for ill-posed problems. Numerical studies on both scattering and radiation problems verify that the proposed HP-KSBFM significantly reduces memory usage and improves accuracy compared with traditional block-Krylov approaches.

As the core component of new energy electric vehicles, the operation state of three-phase inverter is directly related to the stability and safety of the system. In addition, the strong suddenness of the open-circuit fault of the inverter often leads to the problem that the samples are scarce and the fault features are difficult to extract, and the traditional data-driven method under small samples is difficult to ensure the diagnostic accuracy and generalization performance. Aiming at the above problems, a set of fault diagnosis methods for three-phase inverters based on multi-source data fusion and transfer learning is proposed. The three-phase current signal is mapped to the frequency domain by fast Fourier transform (FFT), and a two-dimensional image sample with multi-channel coupling characteristics is generated by SDP fusion. Then, Pre-Norm encoder, explicit Q/K/V attention mechanism, SiLU activation function and optimized classification head structure are introduced on the basis of MobileViT backbone network, and combined with transfer learning and phased fine-tuning strategy to improve feature extraction efficiency and small sample generalization ability. The experiment is carried out on seven types of fault data sets. SDP fusion has been verified to have higher separability and accuracy. Under the same data and training configuration, the classification accuracy of the proposed method is about 2% higher than that of the sub-optimal model. The ablation study demonstrates that transfer learning, fine-tuning, and the improved classification head and encoder contribute significantly to performance, while the k-shot experiments further confirm the robustness of the model under very limited samples.

One-sided EWMA-type control charts are widely recognized for their efficiency in process monitoring. However, most existing designs primarily rely on the reflecting boundary method, with limited systematic studies exploring schemes based on the truncation method, particularly for high-quality processes modeled by the Gamma distribution. This study proposes a framework that employs the truncation method to design one-sided Gamma truncated EWMA-type control charts for detecting deterministic and non-deterministic mean shifts, addressing scenarios with known and unknown parameters. To evaluate the performance of the proposed schemes, Markov chain models are constructed, leading to the computation of the average time to signal (ATS) and expected ATS (EATS) metrics. Additionally, parameter optimization strategies are introduced to enhance the practical applicability of these proposed schemes. Simulation results indicate that the proposed charts outperform competing schemes in both known and estimated parameter scenarios, with monitoring efficiency improving as the cumulative number of events increases. Finally, the effectiveness of the proposed schemes is demonstrated through two industrial applications, highlighting their potential for improving process monitoring.

The lack of fault characteristics and sample quantity information will seriously lead to the fault diagnosis effect of motor drive system. In order to mine deep fault information and improve fault detection accuracy, a fault diagnosis strategy based on WGAN-GP data enhancement and WVD image feature extraction is proposed in this paper. Firstly, the frequency spectrum characteristics of the three-phase current signal are obtained by FFT algorithm. Meanwhile, the wasserstein generative adversarial network with gradient penalty (WGAN-GP) is used to generate sufficient data of fault samples. Subsequently, the samples under each fault mode are subjected to Wigner-Ville distribution (WVD) which can obtain two-dimensional image feature. Finally, the WVD fault features are classified by CNN model to identify the fault modes. The experimental results show that, compared with other feature transformation methods such as continuous wavelet transform (CWT) and markov transition fields (MTF), the identification results can reach to 99.77% by the proposed method, which verifies the feasibility and superiority under unbalanced sample conditions.

To address the challenge of accurately detecting tender tea buds under natural conditions due to occlusion, uneven lighting, and missed small targets, this study proposes a lightweight detection method called YOLOv8n-RGS, based on YOLOv8n. The method focuses on small object detection in occluded environments. First, Region Attention Networks (RAN) are embedded into the backbone to adaptively enhance key region features and effectively suppress interference caused by leaf occlusion. Second, a GSConv (Group Shuffle Convolution) structure is introduced in the neck to combine the advantages of standard convolution and depthwise separable convolution, which improves multi-scale feature representation while reducing model complexity. Finally, the Slide loss function is used to dynamically adjust the weight of positive and negative samples, addressing sample imbalance in scenarios with occlusion and uneven lighting, and further improving detection accuracy. Experimental results show that, compared with the original YOLOv8n, the proposed optimized model reduces model size and computational cost by 3.2% and 4.8% respectively, and increases inference speed by 4.1%. Meanwhile, the F1 score (balanced F Score), recall, and mean average precision (mAP) are improved by 1%, 4%, and 3.1%, respectively. Compared with other mainstream lightweight models such as YOLOv4, YOLOv5n, and YOLOv7-Tiny, YOLOv8n-RGS achieves significantly better detection performance. This model provides an effective solution for high-precision bud detection and occlusion suppression in tea-picking robots.

针对传统的模块化多电平换流器控制策略过于依赖控制器参数整定,以及传统的模型预测控制中存在权重因子选取困难等问题,提出一种基于模型预测控制的模块化多电平换流器多目标级联式控制策略,对多个控制目标逐级独立优化实现输出电流控制、桥臂环流控制以及子模块均压控制.控制策略在避免权重因子选取的同时还兼顾良好的控制效果.在MATLAB/Simu-link中搭建仿真模型,验证了所提策略的有效性.

Information and large number of fault labels are required to achieve intelligent health status assessment of three-phase inverters. However, the current signals of inverters cannot be sufficiently collected since open-circuit faults (OCFs) occur briefly, which makes it difficult to determine the OCF mode of the various power switches. A transfer learning model that effectively uses a small amount of sample data to achieve domain adaptation is proposed to address this problem. First, collected fault-sensitive signals are subjected to a continuous wavelet transform (CWT) to obtain two-dimensional image data with more abundant fault feature information. Second, the source domain and target domain features are projected into the same feature space through a domain adversarial neural network (DANN) to achieve multi-domain feature extraction and adaptation. Then, in the feature extraction module of the DANN, the deep residual network (Resnet) structure is used to replace the typical convolutional neural network (CNN) structure. Finally, an intelligent diagnosis network is used to identify the health status of the inverter samples under variable conditions. Experimental results show that the proposed model can accurately and effectively realize the cross-domain health assessment of three-phase inverters in the case of small samples. The accuracy of the proposed model is better than that of other classical transfer learning models.

Aluminum electrolytic capacitor (AEC) is one of the most pivotal components that affect the reliability of power electronic systems. The electrolyte evaporation and dielectric degradation are the two main reasons for the parametric degradation of AEC. Remaining useful life (RUL) prediction for AEC is beneficial for obtaining the health state in advance and making reasonable maintenance strategies before the system suffers shutdown malfunction, which can increase the reliability and safety. In this paper, a hybrid machine learning (ML) model with GRU and PSO-SVR is proposed to realize the RUL prediction of AEC. The GRU is used for the recursive multi-step prediction of AEC to model the times series of AEC, SVR optimized by PSO for hyper-parameters is applied for error compensation caused by recursive GRU. Finally, the proposed model is validated by two kinds of data sets with accelerated degradation experiments. Compared with the other methods, the results show that the proposed scheme can obtain greater prediction performance index of RUL under different prediction time points, which can support the technology of health management for power electronic system.

With the rapid development of new energy vehicles, the brushless DC motor (BLDCM) drive system's reliability and safety have attracted extensive attention. The three-phase full-bridge inverter (TFI) of the BLDCM drive system has a high fault occurrence rate under actual working conditions. It is difficult to identify the fault directly, which leads to imbalanced fault datasets. In addition, it is challenging to obtain fault samples directly, which increases the difficulty of fault diagnosis. In response to these problems, a data augmentation method based on Wasserstein distance and auxiliary classification generative adversarial network (WAC-GAN) for TFI fault diagnosis has been proposed. First, based on the Auxiliary Classification Generative Adversarial Network (ACGAN), one-dimensional convolutions are constructed to replace two-dimensional convolutions for the characteristics of a three-phase current signal to improve the extraction efficiency of signal features. Then, the Wasserstein distance is introduced to improve the model's objective function. Based on the principle of the mutual game between the generator and discriminator, the generator can mine the sample distribution characteristics from few fault mode samples and generate numerous fault samples of specific categories to accomplish the purpose of data augmentation. The experimental results show that the fault diagnosis accuracy of the WAC-GAN model under different datasets and different fault modes can achieve satisfactory fault recognition performance. Compared with other data augmentation methods, the effectiveness and superiority of the proposed method has been verified.

提出一种应用于T型三电平并网逆变器的基于电流模型预测的虚拟同步发电机控制策略,构建电流离散预测控制模型,通过进一步优化分区,增加小矢量的选择,在减少控制器计算量的同时,兼顾中点电位平衡的控制效果并改善并网电流质量;根据转子角速度的偏差和角速度变化率来自动调整转动惯量和阻尼系数,缩短暂态调节时间,实现对频率和频率波动的有效抑制.最后通过 MATLAB/Simulink平台搭建仿真模型,验证了所提控制策略的可行性和有效性.

With the high-speed development of digital signal processors, model predictive current control (MPCC) has been widely used in power converters. However, the control robustness of MPCC is poor because of its strong dependence on the model parameters. In this paper, an ultra-local model-free predictive current control (MFPCC) for three-level grid-connected inverters (GCI) with LCL filters is proposed. Based on ultra-local theory, a third-order ultra-local model of the GCI with LCL filters is constructed. Then, a Kalman filter (KF) is introduced to estimate the three perturbations. Moreover, the perturbations are compensated to the predictive current model, thus reducing the number of model parameters involved in the predictive control. Finally, simulation results show that the proposed MFPCC improves the tracking performance of the grid current and strengthens the dynamic response capabilities under the parameter mismatch. Furthermore, the output power quality becomes higher, and the system robustness is also enhanced under noisy environment.

Aluminum electrolytic capacitors (AECs) get multiple superior functions such as filtering, energy storage and decoupling, which have a great effect on the performance and lifetime for power converters. Therefore, analyzing and predicting the faults of Aluminum electrolytic capacitors (AECs) is conducive to improve the safety and reliability of the power converters. In order to establish the AECs’ fault prediction model and improve the accuracy, an integrated model based on complete ensemble empirical mode decomposition with adaptive noise, grey wolf optimization algorithm and regularized extreme learning machine (CEEMDAN-GWO-RELM) is proposed. The CEEMDAN is used to decompose the time series of AEC degradation process into several sequences, which can decouple the feature of local fluctuations from global degradation in the AEC time series. Then, the RELM optimized by GWO is used to predict each sequence after decomposition. RELM has the advantages of fewer hyperparameters and less operation time, and GWO with strong astringency is used for its optimization to obtain better fault prediction. Eventually, the predicted values are reconstructed to obtain the predicted values of the integrated model. The results show that, based on the aging data of AEC, the integrated model based on CEEMDAN-GWO-RELM can provide better prediction progress than traditional models, and the maximum relative error of each prediction time point is lower than 1.6%.

Aiming at the problem of incomplete fault types existing in power switches fault detection for three phase inverters, a novel diagnosis method based on generative adversarial network (GAN) and convolutional neural network (CNN) is proposed. Firstly, the phase current is used as the fault-sensitive signal, and the fast Fourier transform (FFT) is performed to obtain the frequency domain features, and the normalization preprocessing is performed. Then, the GAN model is used for confrontation training to generate virtual samples by few real sample characteristics, in order to get balanced samples with different fault modes. Finally, convolutional neural network model is built to complete the power inverter fault diagnosis. The experimental results show that GAN-CNN can effectively improve the diagnosis accuracy and stability in the case of sample imbalance.

在弱电网条件下,由于并联逆变器之间以及逆变器与电网之间的耦合作用,系统会产生谐振.同时,实际系统中的电网阻抗以及逆变器到并网点处的线路阻抗又会导致谐振点发生偏移,加剧系统谐振失稳.以单台并网逆变器为基础,建立考虑电网阻抗和线路阻抗的多逆变器并联数学模型,通过探讨弱网下并联系统的谐振形成机理以及分析电网和线路阻抗对系统谐振产生的影响,研究一种进网电流全前馈与PCC点并联虚拟导纳相结合的谐振抑制方法.在Simulink中搭建3台基于LCL滤波器的T型三电平逆变器并联系统仿真模型并进行仿真.仿真结果表明该方法可以有效抑制LCL型并网逆变器的自身固有谐振以及弱电网引发的谐振,同时还可以有效提高系统稳定性,增强多机并联系统对线路阻抗和电网阻抗变化的鲁棒性.

To address the problem of poor fault diagnosis for three-phase inverter faults when the effective data samples are insufficient under variable operating conditions. An inverter fault diagnosis method based on convolutional neural network(CNN) and transfer learning(TL) is proposed to migrate the fault diagnosis knowledge learned by the model on the source domain to the target domain. It is used in a small sample of three- phase inverter fault diagnosis research. First, the acquired faultsensitive signal is continuously wavelet transformed to obtain colorful two-dimensional time-frequency images conducive to CNN training. Secondly, a pre-training-fine-tuning transfer learning method is used to train the network using a sufficient number of source domain samples to avoid the overfitting phenomenon caused by insufficient data. After migrating the network structure and parameters to the target domain, the deeper network parameters are fine-tuned to make the network adapt to the data distribution of the target domain samples. Finally, TL experiments and fault classification diagnosis were performed on the dataset. The case analysis proves that combining continuous wavelet transform(CWT) and CNN can achieve automatic feature extraction and highly effective use of samples. The introduction of TL enables the accurate classification of small samples under other working conditions. It has a certain value for the research and application of TL learning theory in inverter fault diagnosis.

With the rapid development of new energy vehicles, the reliability and safety of Brushless DC motor drive system, the core component of new energy vehicles, has been widely concerned. The traditional open circuit fault detection method of power electronic converters have the problem of poor feature extraction ability because of inadequate signal processing means, which lead to low recognition accuracy. Therefore, a fault recognition method based on continuous wavelet transform and convolutional neural network (CWT-CNN) is proposed. It can not only adaptively extract features, but also avoid the complexity and uncertainty of artificial feature extraction. The three-phase current signal is converted into time-frequency spectrum by continuous wavelet transform as the input data of AlexNet. At the same time, the changes of time domain and frequency domain under different fault modes are analyzed. Finally, the softmax classifier with Adam optimizer is used to classify the fault features extracted by CNN to realize the state recognition of different fault modes of power electronic converter. The experimental results show that the CWT-CNN model achieves satisfactory fault detection accuracy under different working conditions and different fault modes. The effectiveness and superiority of the proposed method are verified by comparing with other networks.

Aluminum electrolytic capacitors (AECs) play a crucial role in traction power electronic converters, which are also the most likely to be responsible for breakdowns. Fault prediction for AECs is helpful to realize preventive maintenance and to reduce the cost of the entire system. However, it is restricted by the collected data scale and the period of the deteriorative process of AECs. Thus, this paper takes the advantages of the synthetic minority oversampling technique (SMOTE) that is used to augment the degradation data information and the gate recurrent unit (GRU) that can be suitable for degradation samples to establish a prediction model. An improved particle swarm optimization (IPSO) algorithm is utilized to optimize the hyper parameters of the GRU to promote the feature learning and prediction performance. Thereupon, a prognostics model based on data augmentation and a GRU optimized by IPSO is simulated on a degradation dataset of AECs under an aging test. The results show that the integrated prediction model achieves better accuracy and reliability when compared to some traditional models. Furthermore, the relative error of each prediction point is less than 2.5% for single step and 3.0% for multi-step, respectively.

As the voltage level is relatively low under medium and low voltage distribution network,the number of modular multilevel converter (MMC) sub-modules is also small. Therefore,the modulation mode of MMC with small quantities of sub-modules has an important influence on the performance of the MMC system. In order to improve the output quality and output capability of the MMC with small quantities of sub-modules,a hybrid modulation strategy suitable for MMC with small quantities of sub-modules based on level step switching at a high modulation degree is proposed. On the basis of the nearest level modulation (NLM),the MMC is controlled to switch between NLM and the carrier phase shift pulse width modulation (CPS-PWM) at the output level step point in real time. At the same time,the circulation control and the sub-module voltage equalization control are combined to further ensure the normal operation of MMC with small quantities of sub-modules under the hybrid modulation. Then,the simulation model of MMC hybrid modulation with four sub-modules is established,and a simulation study is carried out on the MMC hybrid modulation strategy of the MMC with small quantities of sub-modules. The results show that the proposed hybrid modulation strategy not only takes into account the characteristics of low output harmonics and low switching loss,but also improves its output quality. And its operation in high-profile system increases the DC voltage utilization rate and improves the output capacity of the MMC with small quantities of sub-modules.

In the new energy power generation system,the inverter is connected to the grid in parallel,which expands the grid capacity. In addition,due to the existence of grid impedance,the stability of the inverter is reduced and the difficulty of power quality governance in the load increases. The problem of power quality governance of parallel inverter in weak current network is studied. Firstly,H repetitive control combined with voltage feedforward is used as the current inner loop control strategy and the stability performance of single and multiple parallel inverters in weak current network is analyzed. The composite strategy of feedforward channel series complex filter and forward channel series lead correction link is adopted to improve the stability of inverter. Secondly,the current detection algorithm is used to separate the unbalanced,harmonic and reactive currents in the load,which solves the governance of power quality problems caused by unbalanced,nonlinear and reactive loadd. Finally,the simulation experiment is carried out in Simulink. The simulation results show that the power quality problems of multiple loads under the condition of weak power grid can be solved by parallel inverter.