In this article, defects in the production process of silicon photovoltaic (Si-PV) cells are urgently needed to be detected due to their serious impact on the normal generation of PV system. In view of the shortcomings, such as low-defect efficiency, few detection data, and high detection error rate in the existing industrial production line, the main research purpose of this article is to complete an intelligent classification method for efficient and innovative defect detection for Si-PV cells and modules. The purpose is to improve the detection efficiency of Si-PV cell, to ensure the safety and reliability of Si-PV cell production process, to achieve large number of Si-PV cell defects detection and classification. First, the eddy current thermography system of Si-PV cells is established. Second, principal component analysis, independent component analysis, and nonnegative matrix factorization algorithms are compared for thermography sequences processing. Third, LeNet-5, VGG-16, and GoogleNet models are compared for Si-PV cell defects classification. Finally, the results show that the proposed method have successful application in Si-PV cell defects detection and classification.

Defects can affect the generation efficiency and service life of photovoltaic cells and modules, even can cause serious damage to grid connected photovoltaic power generation station. This work proposes active electromagnetic induction infrared thermography (EIIT) defect detection methods for photovoltaic cells and modules, which have the advantages of non-contact, rapid, full-field, subtle and quantitative detection. Firstly, the mechanism of infrared radiation after electromagnetic induction of photovoltaic cells is described and a digital EIIT system is established. Then, the thermal image sequences of photovoltaic cells and modules are obtained under pulse and lock-in modes of excitation. Fast Fourier transform (FFT), Independent component analysis (ICA), and Principal component analysis (PCA) are used to deal with the thermal image sequences. Finally, the visual detection of defects, including scratch, hot spot, micro crack, surface impurity pollution and broken grid in photovoltaic cells and modules are realized. The experimental results have shown that the proposed method can distinguish the background noise and the defects very well, thus providing a reliable and rapid inspection means for the research, testing, manufacturing, service and maintenance of photovoltaic cells and modules.

Effective fault diagnosis for cascaded H‐bridge multilevel inverter (CHMLI) can reduce failure rate and prevent the unscheduled shutdown. Nevertheless, traditional signal‐based feature extraction and feature selection methods show poor distinguishability for insufficient fault features in a one‐dimensional space. The shallow learning models are prone to fall into local extremum, slow convergence speed and overfitting. To cope with these problems, a novel image‐oriented fault diagnosis strategy based on sparse representation (SR) and deep convolutional neural network (DCNN) is proposed for CHMLI. Initially, Hilbert–Huang transform (HHT) is applied to obtain the HHT spectral images of original monitoring signals, where these images comprehensively represent the features with detailed information of multiple domains on the time‐frequency plane. Furthermore, an image fusion method based on the SR algorithm is employed on these spectral images of the same fault category to construct fused feature images, which effectively reflects the complicated relationships between the measured signals and fault features. Ultimately, the DCNN models can not only mine the relationship between the various fault categories and the different fused feature images but also can alleviate the problem of overfitting that is caused by the limited availability of training samples.

Machine learning methods are effective for the diagnosis of power transformer faults. However, influenced by uncertainty and noise in data, machine-learning-based diagnostic methods are still in the initial phase of certain assets in power systems. To mitigate this gap, a deep noisy filtering diagnostic model is proposed for accurate and rapid evaluations of power transformer faults using noisy vibration signals. A balanced isolation forest method is employed to detect abnormal data from the original vibration signals. Two deep noisy filter networks suppress the level of noise, based on which contrastive learning obtains the transformer fault states. Datasets collected from a 10-kV real power transformer validate the proposed model. The results demonstrate that the proposed method acquires a higher fault diagnostic accuracy with respect to the compared algorithms, showing the superiority and efficacy of the proposed model.

In the process of research, development, production, service and maintenance of silicon photovoltaic (Si-PV) cells, the requirements for detection technology become more and more important. This paper aims to investigate electromagnetic induction (EMI) and image fusion to improve detection effect of Electro-thermography (ET) and Electroluminescence (EL) of multi-defects in Si-PV cells. Firstly, the principle of ET, EL and other physical processes including EMI, thermal radiation, luminescence radiation are analyzed in this work. ET and EL techniques after EMI improvement are used to detect different defects including scratch, broken gridline, surface impurity, hidden crack and so on. The qualitative results show that EMI can greatly improve the defect detection ability of ET and EL. Then, an image fusion rule based on L1 norm is proposed to fuse the sparse vector of the ET and EL images. The integration and complementarity of the two wavelength detection data are achieved. Finally, the image fusion results of Sparse Representation (SR) algorithm is compared with Discrete Wavelet Transform (DWT), Curvelet Transform (CVT), Dual-tree Complex Wavelet Transforms (DTCWT) and Nonsubsampled Contourlet Transform (NSCT). Five objective evaluation indexes including Root Means Square Error (RMSE), Peak Signal to Noise Ratio (PSNR), Correlation Coefficient (CC), Mutual Information (MI) and Structural Similarity Index (SSIM) are used to evaluate the fusion results. Overall evaluation results show that the SR algorithm is superior to the other algorithms.

This article focuses on data-driven approaches for icing detection (ID) on wind turbine blades. In light of the widespread application of sensor technologies in wind turbines, such data-driven ID methods have become increasingly prominent. However, current methods have deficiencies, particularly in acknowledging the structural properties of multivariate sensor data and in differentiating icing stages, both critical for the identification of failure patterns. To bridge these gaps, we propose a spatiotemporal attention Siamese network (STASN) for blade ID. This model employs a Siamese network architecture for efficient few-shot learning amidst class imbalance. It uniquely incorporates a graph attention network and gated recurrent unit for extracting spatiotemporal features from sensor data. This design not only acknowledges the spatial structure of the data but also distinctly identifies features pertinent to various icing stages. The efficacy of STASN was validated using actual sensor data from supervisory control and data acquisition systems. The results demonstrate STASN's capability in discerning distinct icing stage features and its potential in early icing prediction. This research underscores STASN's utility in providing advanced, flexible fault alarms for blade icing, representing a significant stride in wind turbine maintenance and safety.