To address the problem, it is difficult to extract fault features for neutral-point-clamped (NPC) three-level inverters under nonstationary conditions. This article proposes a multi-information feature fusion diagnosis method based on attention collaborative stacked long short-term memory (ASLSTM) neural networks. First, parallel structural stacked LSTM networks are constructed, which are used to automatically extract features from multisource time-series data. Then, the attention mechanism is applied to weight these features adaptively. Finally, the fault is identified by features that integrate multiple sources of information. In addition, the quantum particle swarm optimization (QPSO) algorithm is used to intelligently tune the hyperparameters of ASLSTM to improve the reasonableness of hyperparameter selection for the diagnostic model. The simulation results of the fault diagnosis of the NPC three-level inverter circuit show that the proposed method is able to extract high-discriminative features from raw data and has better diagnostic results under various conditions compared with other schemes.

In practical fault diagnosis, the monitoring fault data is accumulated incrementally, it is necessary to detect the newly added fault data. To this end, this paper proposed a broad residual network (BRES) fault diagnosis method with incremental learning capability. Firstly, the deep feature representation of the raw data is obtained by the residual network, and the obtained features and corresponding labels are then updated to the BLS. For the newly collected data, the incremental learning of new fault modes is achieved by automatic feature extraction of the ResNet and the node expansion of the BLS. The effectiveness of the proposed method is verified by motor-driven converters fault diagnosis. Experimental results indicate that the method can effectively update the diagnosis model to incrementally learn new fault categories and new fault modes.

In order to solve the problems of low accuracy of the current mainstream target detection algorithms in identifying small target ships, complex background interference such as coastline buildings and trees, and the influence of ship occlusion on ship target detection, an inland river ship detection method based on improved YOLOv10n: CDS-YOLO is proposed under the premise of keeping the model lightweight. Firstly, the CAA attention module is introduced into the Backbone network, and the C2f_CAA module is constructed at the same time to enhance the features of the central region and improve the understanding ability of complex scenes. Then, the Conv of the Backbone network was replaced with DBB to enhance the expression ability of a single convolution and enrich the feature space. Finally, GSConv and VovGSCSP in Slim-Neck are introduced into the Neck network to optimize the network architecture, reduce part of the model complexity, and further improve the performance of the model. Experimental results show that compared with YOLOv10n, CDS-YOLO has the detection accuracy, recall rate and mAP@0.5 increased by 3.7%, 2% and 0.9% respectively, reaching 98.4%, 97.4% and 99.2% respectively, indicating that CDS-YOLO has good accuracy and robustness in the detection and classification of inshore ships.

Photovoltaic (PV) power forecasting plays a crucial role in renewable energy integration and power system operation, as its accuracy directly affects the security and economic efficiency of the grid. However, PV output is strongly influenced by meteorological conditions and thus exhibits pronounced volatility and intermittency, posing significant challenges for ultra-short-term forecasting. To address this issue, this study proposes a hybrid forecasting model that integrates a parallel architecture combining a multi-scale convolutional neural network and a bidirectional gated recurrent unit to effectively capture both local features and temporal dependencies. In addition, a Kolmogorov–Arnold network is incorporated to enhance the model's ability to represent complex nonlinear patterns. To evaluate the generalization and robustness of the proposed approach, we conduct ablation studies, seasonal and weather-condition comparisons against multiple baseline methods, and multi-step forecasting experiments. The results demonstrate that the proposed method consistently achieves superior forecasting accuracy and stability across diverse scenarios, effectively mitigating the uncertainty arising from the volatility and intermittency of PV power generation.

Accurate remaining useful life (RUL) prediction of proton exchange membrane fuel cells (PEMFCs) is essential for reliability-oriented operation and predictive maintenance of hydrogen energy storage systems. However, PEMFC degradation under dynamic operating conditions is often non-stationary and non-Gaussian, exhibiting long-range dependence, heavy-tailed fluctuations, and abrupt jump behaviors. To address these challenges, this paper proposes a mechanism-constrained fractional Lévy–Poisson (fLP) degradation model for probabilistic RUL prediction and uncertainty quantification. In the proposed framework, fractional Lévy stable motion is employed to characterize continuous degradation dynamics with long memory and heavy-tailed stochastic fluctuations, while a Poisson jump process is introduced to capture abrupt degradation events induced by operating-condition variations. Moreover, a composite degradation factor is embedded into the drift term to impose mechanism-related constraints and enhance physical consistency. Based on adaptive parameter estimation and Monte Carlo simulation, the proposed model enables probabilistic RUL prediction under both steady-state and dynamic operating conditions. Experimental results show that the proposed method outperforms representative benchmark and ablation models, achieving MAE, MAPE, RMSE, and R² values of 0.5833, 1.6826%, 0.7642, and 0.9847, respectively. These results indicate that the proposed fLP model provides an interpretable and reliable prognostic framework for uncertainty-informed maintenance and lifetime management of PEMFCs in hydrogen energy storage systems.