One of the common causes of cross-linked polyethylene (XLPE) insulation aging and eventual cable failure is the presence of voids within the cable insulation. Gas-filled voids can be initiated in XLPE cables during their manufacturing, installation, and operating conditions. Once the size of the void reaches a certain critical limit, noticeable partial discharge (PD) activity develops. Therefore, the size of the void is one of the important factors considered when investigating PD activity in power cables. This paper presents a detailed examination of how different sizes and distributions of voids affect the cable's behavior. A 3D model of XLPE power cable insulation is carried out to improve the understanding of how the electric field distribution responds to different shapes, positions, and sizes of voids within the XLPE insulation. The effect of void size on PD behavior within the XLPE layer of an 18/30 kV cable specimen is studied. Also, the changes in artificial spherical void depths and diameters are investigated.

This paper proposes a self adaptive physics informed neural network (SAPINN) model to predict the degree of polymerization (DP) of oil-impregnated paper insulation to quantify the level of degradation and the remaining useful lifetime. The prediction is performed based on historical DP values and the corresponding prediction time step, which are used as input data points to the proposed model. The DP mathematical model is used to constrain the training phase of the AI-model through a weighted sum loss function. The weights of this loss function are adjusted for each epoch through a self-adaptive weighting method to determine the relative importance of the data component and the mathematical model throughout the training by defining these weights as trainable parameters. The trained model is then tested using different datasets which are not part of the training phase. The training and testing datasets are generated synthetically through an algorithm that considers the deviation from the ideal DP degradation curve and incorporates actual measurement noise. The performance of the proposed SAPINN is compared to the baseline PINN and NN (in the absence of physics) to highlight the importance of embedding the mathematical model and the self adaptation algorithm, and theses experiments demonstrate that SAPINN significantly enhances the DP prediction.

Induction motors are a necessity in many industries, which is why early fault detection is critical to account for damage and industrial downtime. Among the incipient damages, BF and stator winding faults are the most prevalent. Consequently, early detection and classification of these faults are gaining significant attention. This paper investigates the application of multiple gradient boosting machine learning (ML) algorithms, that are known for their robustness, and analyses the accuracy of the models on faulty induction motors (IM) using Motor Current Signal Analysis (MCSA). Five different Supervised machine learning algorithms such as Gradient Boosting Machines, XGBoosts, and LightGBM were used in this study and compared with strong models like RF and KNN. Overall, the experiments provided a classification accuracy of approximately 92% and were able to distinguish the normal, bearing, and stator winding faulty signals. The obtained results show that current signals are a viable option for observing IM electrical and mechanical faults with finetuned optimization of hyperparameters.

Monitoring partial discharges (PDs) in high voltage (HV) electrical equipment is an effective tool to prevent unscheduled shutdown and eliminate possible failures. The inception of PDs can take various forms within the insulation system and can be detected using established approaches. The most common cause of HV rotating machine failure is the initiation of PDs in the stator winding insulation system. Various online and offline techniques have been proposed to monitor, detect, diagnose, and locate PDs in HV rotating machines. This paper gives an overview of existing PD detection and localization technologies in HV rotating machines. A comparison of online and offline PD detection approaches is discussed in this paper. Finally, a brief review of the methods of PD classification is provided.

The management of incipient faults in induction machines (IMs) is crucial for ensuring reliability and efficiency in diverse industrial applications, including power grids, electric vehicles, and manufacturing processes. This review explores advanced fault detection and diagnosis (FDD) strategies, empha-sizing deep learning (DL) methods such as convolutional neural networks (CNN), recurrent neural networks (RNN), and au-toencoders for fault detection and classification. Traditional machine learning (ML) approaches are also discussed, highlighting their integration with signal processing techniques like wavelet transforms and Fourier transforms to enhance FDD accuracy. Additionally, the potential of physics-informed neural networks (PINNs) is examined, demonstrating how incorporating physical knowledge into data-driven models can improve diagnostic precision. The paper presents an analysis of recent publications, identifies current research gaps, and proposes future directions, including the development of robust AI-based FDD systems and the consideration of stochastic industrial data for more accurate predictive maintenance. By offering a comprehensive overview of FDD techniques and highlighting key research areas, this review aims to advance the reliability and performance of IMs.

Photovoltaic (PV) power is progressively being subsumed into power grids. Consequently, reliable PV power forecasting (PVPF) has become essential to avoid ramp events that can adversely affect the operations of integrated power systems. This article presents a deep-learning-based algorithm for PVPF. The gated recurrent units (GRU) network was implemented to predict the non-linear spatiotemporal correlations of the weather data, leading to higher reliability of the PV stations. Experimental results obtained from actual testing demonstrate the validity of the GRU networks for accurate PVPF, contributing to the efficient operation and management of smart grids and renewable energy systems. The conducted case study shows that the proposed model outperforms bidirectional long short term memory (BiLSTM) and long short term memory (LSTM) models in terms of computation power, root-mean-square error, and mean absolute error metrics.