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.

With the development of Industry 4.0, as a pivotal part of the power system, large-capacity power transformers are requiring fault diagnostic methods with higher intelligence, accuracy and anti-interference ability. Considering the powerful capability for extracting non-linear features and the sensitivity differences to features of deep learning methods, this paper proposes a deep parallel diagnostic method for transformer dissolved gas analysis (DGA). In view of the insufficient and imbalanced dataset of transformers, adaptive synthetic oversampling (ADASYN) was implemented to augment the fault dataset. Then, the newly constructed dataset was normalized and input into the LSTM-based diagnostic framework. Then, the dataset was converted into images as the input of the CNN-based diagnostic framework. At the same time, the problem of still insufficient data was compensated by the introduction of transfer learning technology. Finally, the diagnostic models were trained and tested respectively, and the Dempster–Shafer (DS) evidence theory was introduced to fuse the diagnostic confidence matrices of the two models to achieve deep parallel diagnosis. The results of the proposed deep parallel diagnostic method show that without complex feature extraction, the diagnostic accuracy rate could reach 96.9%. Even when the dataset was superimposed with 3% random noises, the rate only decreased by 0.62%.

Industrial 4.0 placed higher demands on the field of intelligent equipment monitoring. The transformer is one of the critical power devices, its intelligent monitoring and fault positioning require in-depth studies. In this study, an efficient fault localization method for transformer internal thermal faults was proposed by introducing different deep convolutional neural networks (CNNs) and image segmentation. First, the transformer monitoring images of temperature and velocity fields in fault conditions were simulated using the lattice Boltzmann method (LBM), and the images were also used to highlight features information. In practice, transformer degradation does not frequently occur, so that the fault samples for deep learning are insufficient. To solve this problem, a transfer learning method was employed. Subsequently, fault locations were defined as classification labels, and different CNN’s were used to classify the labels to achieve the fault localization results. Next, image segmentation was performed to extract the features of fault areas and simplify the data volumes. Likewise, the CNN’s were employed to perform the fault localization again. Afterward, since the monitoring sensors were not located everywhere in a transformer in practical applications, information of partial monitoring areas where the monitoring sensors located was trained following a similar procedure. After image segmentation, the average fault localization accuracy using the information obtained by sensors decreased from 97.95% to 94.42%, while the data volume was reduced to nearly 1% of the original one. Besides, the average calculation time per iteration decreased by 8.816%, while the loss value was reduced by 37.68%. Finally, the Friedman hypothesis test and Nemenyi post hoc test were performed to compare the evaluation indicators of different networks, and the performance of GoogLeNet in this case was considered the best.

The idea of Ubiquitous Power Internet of Things (UPIoTs) accelerates the development of intelligent monitoring and diagnostic technologies. In this paper, a diagnostic method suitable for power equipment in an interference environment was proposed based on the deep Convolutional Neural Network (CNN): MobileNet-V2 and Digital Image Processing (DIP) methods to conduct fault identification process: including fault type classification and fault localization. A data visualization theory was put forward in this paper, which was applied in frequency response (FR) curves of transformer to obtain dataset. After the image augmentation process, the dataset was input into the deep CNN: MobileNet-V2 for training procedures. Then a spatial-probabilistic mapping relationship was established based on traditional Frequency Response Analysis (FRA) fault diagnostic method. Each image in the dataset was compared with the fingerprint values to get traditional diagnosing results. Next, the anti-interference abilities of the proposed CNN-DIP method were compared with that of the traditional one while the magnitude of the interference gradually increased. Finally, the fault tolerance of the proposed method was verified by further analyzing the deviations between the wrong diagnosing results with the corresponding actual labels. Experimental results showed that the proposed deep visual identification (CNN-DIP) method has a higher diagnosing accuracy, a stronger anti-interference ability and a better fault tolerance.

Most of the power transformer fault diagnostic researches so far focuses on its fault type diagnosis, but there are less related researches on fault positioning, and the diagnostic methods of which are still less intelligent. This paper proposes a two-dimensional Hilbert ID considering multi-window feature extraction for deep vision fault positioning of the transformer winding. Firstly, sweep frequency response data containing complex fault characteristics is obtained based on pspice simulation. Next, a multi-window feature extraction method with logarithmic constraints is introduced to process the original data to obtain feature sequences. Then the proposed Hilbert visualization is used to further highlight the graphic feature of the feature sequences, and obtain Hilbert ID (MAPE) dataset. Finally, it is used to conduct transfer learning on the convolutional neural network. Different intelligent positioning methods are compared, and the proposed deep vision fault positioning method is 6.51% higher than other methods on average. What’s more, the positioning effects based on different data processing methods are also compared. The accuracy of the proposed Hibert ID (MAPE) dataset is 10.35% higher than the other data processing methods on average. Finally, the positioning accuracy of Hilbert ID (MAPE+CC) combining two feature sequences can reach 96.09%, having an increase of 2.50%.

In this paper, a novel fault localization method combining frequency response analysis (FRA) and evidential reasoning process is proposed. FRA test is conducted at first so that we can obtain corresponding responses of several monitoring nodes on the winding. Traditional FRA interpretation methods simply compares those response curves, while in our work, evidential reasoning method is adopted to present beliefs about the fault locations. Extracted features from monitoring sensors are mapped into deviation matrix by which possibility assignments of each fault location is computed. So we finally obtain fault distribution probabilities. According to test calculation results, proposed method can give fault probabilities of each section of transformer winding, which correctly indicate the fault position.