The development of Internet of Things (IoT) technology promotes the application of fault diagnosis method based on data-driven in power transformer. However, collecting a large number of labeled fault samples in real-world engineering scenarios is often costly and labor-intensive. To address this challenge, a novel graph-based spatiotemporal two-stream network (GSTSN) is proposed to accurately identify transformer fault conditions under limited labeled data. Specifically, the designed two-stream graph module constructs a spatiotemporal feature graph incorporating both real and pseudo labels via a label propagation strategy. Subsequently, a spatiotemporal feature augmentation graph convolutional network (SFA-GCN) is designed to capture both specific and shared information embedded in the spatiotemporal graph. Finally, an adaptive feature fusion module based on the minimum redundancy maximum relevance (mRMR) principle is developed to dynamically and selectively integrate temporal, spatial, and common feature embeddings, achieving reliable diagnostic results with limited supervision. Real vibration data from a 693/400 V test transformer are employed to assess the performance of the GSTSN framework. Experimental results show that under a 5% labeling rate, an average diagnostic accuracy of 96.14% is attained by the proposed method, highlighting its significant applicability in real-world engineering scenarios.

Electrothermal modeling is critical for accurate battery state estimation under dynamically changing loads, especially in real-time digital twin (DT) applications. To address the limitations of traditional decoupled or unidirectional electro-thermal frameworks, this study proposes a cross-task attention-based multitask learning (CTA-MTL) model that enables bidirectional electro-thermal coupling and joint residual correction of voltage and temperature predictions. The model integrates a shared temporal encoder based on a quasi-recurrent neural network, a two-dimensional convolutional neural network enhancement module for spatiotemporal coupling, and a sparse cross-task attention mechanism to capture task interdependencies. Built upon an equivalent circuit model and a lumped thermal model, the framework provides a lightweight and physically interpretable structure suitable for embedded deployment. Experimental evaluations across five discharge scenarios—including high C-rate and pulse conditions—demonstrate superior prediction accuracy and generalization compared to representative soft parameter sharing and single-task correction baselines. Ablation studies further confirm the effectiveness of shared representation learning, task-aware attention, and sparse selection. These results validate CTA-MTL as a robust and efficient solution for real-time electro-thermal state estimation in battery DTs.

The complexity of data and limited model generalization significantly hinder prediction accuracy. A physics-informed long short-term memory model with adaptive weight assignment (PILSTM-AWA) is proposed. First, PILSTM-AWA employs segmented feature extraction to enhance local information capture and improve feature extraction capabilities for nonlinear data. Then, the physical distribution and dynamic changes law of dissolved gas in oil are embedded into the LSTM framework. PILSTM is designed to constrain data fluctuations and predict dissolved gas in oil. Finally, an adaptive dynamic weighting strategy is introduced to balance physical and data information, enhancing forecast accuracy. The study utilizes online monitoring data from a 1000 kV transformer. Experimental results demonstrate that the model outperforms comparative algorithms in evaluation metrics. Notably, it achieves a coefficient of determination exceeding 91% under both normal and abnormal conditions, surpassing the predictive performance of conventional models.

Early fault warning is critical for long-term stable operation of power transformer. However, slight mechanical fault of the power transformer does not lead to obvious changes in the vibration signal, which undoubtedly increases the difficulty of early warning. To solve this problem, this article proposes an early fault warning scheme for power transformer using vibration signal. Specifically, a new unsupervised learning framework, parallel multiscale convolutional autoencoder network (PMCAEN), is first developed to capture multidimensional and multiscale feature information of power transformer. Subsequently, support vector data description is established, and the reconstruction error obtained by PMCAEN under normal conditions is used as new observation information to construct the judgment domain of degraded state. The effectiveness of the proposed method is verified by the data collected from a 220-kV power transformer. The experimental results show that the proposed method achieves higher early warning accuracy with respect to the compared algorithms.

The analysis and interpretation of enormous amounts of data generated by 5G networks present several challenges related to noise, precision, and feasibility validation. Therefore, this study aims to evaluate the effectiveness of channel equalisation in the network and enhance it by distributing signals over all subcarriers and symbols. The error‐free signal received ensures the reliable transmission of signals in the network connection. These simulations were undertaken to fulfil the needs of and adapt the transmission properties according to the specific conditions of the channel. The dataset consists of artificially generated radio waves to train signals through neural networks (NNs) and machine learning algorithms to detect errors properly. The primary objective is to achieve optimal signal performance. In this regard, an artificial neural network (ANN) was initially employed, explicitly utilising the back‐propagation technique and a feedforward multilayer perceptron (MLP). In addition, the signals were subjected to train using a real‐time simulator, employing feedforward neural network and support vector machine (SVM) to validate the proposed methodology. Feedforward MLP achieved the highest performance in simulations compared to SVM. The scheme is promising to achieve optimal signal performance in real‐time.

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.

A large number of electric vehicles, distributed solar, and/or wind turbine generators connected to distribution systems lead to frequent and sharp voltages fluctuations. The action rates of conventional adjustable devices and smart inverters are very different. In this context, a novel dual-timescale voltage control scheme is proposed by organically combining data-driven with physics-based optimization. On fast timescale, a quadratic programming for balanced and unbalanced distribution systems is developed based on branch flow equations. The optimal reactive power of renewable distributed generators and static VAR compensators is configured on several minutes or seconds. Whereas, on slow timescale, a data-driven Markovian decision process is developed, in which the charge/discharge power of energy storage systems, statuses/ratios of switchable capacitors reactors, and voltage regulators are configured hourly to minimize long-term discounted squared voltages magnitudes deviations using an adapted deep deterministic policy gradient deep reinforcement learning algorithm. The capabilities of the proposed method are validated with IEEE 33-bus balanced and 123-bus unbalanced distribution systems.

Extracting sensitive information from vibration signal has become a frequently adopted way in fault diagnosis. However, most previous methods fragmented the relationship between quantification and visualization analysis, which affects the interpretability, accuracy and comprehensiveness of the extracted information. To this end, this paper proposes distribution recurrence plots (DRP) and measures (DRM) to realize the unity of visualization and quantification analysis of the signals. Specifically, DRP is a novel feature graphical representation method following the thought of symbolic dynamics. Derived from DRP, DRM is developed containing four quantifiers for extracting comprehensive fault features that allows a multiclass support vector machine (SVM) to identify the fault types of wind turbine drivetrain system (WTDS). Specially in DRM, pattern entropy is a newly designed quantifier by considering pattern distribution to obtained more accurate quantitative representation of the signals. Using simulated data, DRP and DRM are validated to reveal the intrinsic structural changes for different dynamic systems and robustness to noise. Applications on wind turbine gearbox illustrate that the proposed method has favorable diagnosis performance and stability compared with other competitors. This approach is easy to interpret, is robust to noise, and has a low computational burden, becoming viable for WTDS fault diagnosis.

With the development of blockchain, artificial intelligence, and data mining technology, abnormal network traffic data has become easy to obtain. The traffic detection model detects the traffic patterns in the network to find abnormal traffic that does not conform to the normal traffic law, which has great security significance for Industrial Internet of Things (IIoT) networks and devices in real scenarios. However, previous abnormal detection models rely on expert experience and cannot cope with real-time changes in IIoT scenarios. The manual features cannot be sufficiently representative and adaptive. Moreover, there are few abnormal traffic data in real scenarios, which makes the model unable to fully learn the potential distribution in abnormal data. Therefore, in this work, we propose a deep abnormal network traffic detection model (DANTD) for the security of IIoT using high-order features and novel data augmentation strategies. The DANTD model first adopts a deep convolutional autoencoder to extract effective high-order features to make it more representative. Then, the DANTD model uses generative adversarial networks as data augmentation strategies to enrich the abnormal data, so that the model can fully consider the information of the data distribution. Comprehensive experiments on real IIoT data sets validate the effectiveness of the DANTD model.

Benefiting from the recent successes of convolutional neural networks (CNNs), many studies have modeled the vibration signal of energy system into a two-dimensional (2-D) input graph to amplify and highlight fault features. However, most works seldomly consider the system dynamic characteristics, which may affect the knowledge discovery and diagnosis quality. To address this issue, this article proposes a novel graphical modeling approach, termed as fine-gained recurrence graph (FRG), to capture dynamic characteristics of vibration signals from the view of nonlinear dynamics. FRG focuses on modeling the temporal correlations and tendencies between state vectors in phase space and then visualizes the characteristics to represent intrinsic dynamic changes of the vibration signals into 2-D graphs. The information representation capability of the proposed FRG is verified using different dynamic signals. Based on this finding, an ensemble fault diagnosis model is proposed fusing FRG with deep CNNs. Meanwhile, transfer learning is accompanied to deal with the training difficulties of deep CNNs. Finally, case studies on experimental data and real wind turbine data illustrate its effectiveness and feasibility. Comparisons with four state-of-the-art approaches have confirmed the preferable information representation and diagnosis performance of the proposed approach.

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.

This paper proposes a distributed cooperative control strategy for cyber-physical microgrids in a distributed sparse network with communication delays by using predictive control theory, which enables each distributed generator (DG) unit to achieve voltage restoration. In the proposed control strategy, we first design a primary droop-free model predictive controller to make the output voltage track its nominal set points timely, with the neighbor-based error information a secondary distributed coordinated predictive controller is then proposed to actively compensate the general communication delays for cyber-physical microgrids caused by low-bandwidth communication networks. Sufficient conditions, in terms of communication network connectivity and control gains for the system stability are derived by using the tools of special matrix theory and algebraic graph theory. The proposed control protocols are fully distributed and can be implemented through a sparse communication network and thus, satisfy the plug-and-play feature of the future smart grid. The effectiveness of the control strategy in compensating communication delays is also verified by real-time simulation experiments in OPAL-RT on a test microgrid.

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.

In the process of research, development, production, service, and maintenance of silicon photovoltaic (Si-PV) cells and the requirements for detection technology are becoming more and more important. This paper aims to investigate electromagnetic induction (EMI) and image fusion to improve the detection effect of electrothermography (ET) and electroluminescence (EL) of multidefects in Si-PV cells. First, the principles of ET, EL, and other physical processes including EMI, thermal radiation, and luminescence radiation are analyzed in this paper. 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, curvelet transform, dual-tree complex wavelet transforms, and nonsubsampled contourlet transform. Five objective evaluation indexes including root mean square error, peak signal-to-noise ratio, correlation coefficient, mutual information, and structural similarity index are used to evaluate the fusion results. Overall evaluation results show that the SR algorithm is superior to the other algorithms.

Except for the advantages of supporting arbitrary kernels, probabilistic predictions and automatic estimation of hyper-parameters, relevance vector machine (RVM) also encounters some of training time increase and classification accuracy recession, compared with SVM. In order to suppress such `nuisance' imperfections, this paper proposed a sparsity-aware RVM model for multi-class classification (denoted as Sa-MRVM) by developing a configurable singular entropy decision mechanism. Multiple driven data sets captured from both emulational and actual circuits under test (CUTs) are involved to further improve the model's generalization ability and judging confidence. Experimental results carried out on two CUTs indicate that our proposed learning methodology is speedy and accurate enough for real world fault diagnosis tasks of analog and mixed-signal circuits.