Search Results (102)

Pumped storage hydropower (PSH) is a key regulating resource for renewable energy integration and power system stability. Due to frequent start-stop operations, deep peak-load regulation, and bidirectional operating conditions, stator winding insulation degradation, rotor inter-turn short circuits, and end-winding vibration have become the dominant failure modes of pumped storage units. Conventional monitoring systems are limited by single-source sensing, asynchronous data acquisition, high misdiagnosis rates, and maintenance decisions that rely heavily on expert experience, making traditional periodic maintenance increasingly inadequate. To address these challenges, this study proposes an intelligent diagnosis and decision support system based on multi-source data fusion and hybrid intelligent algorithms. An Intelligent Electronic Device (IED)-based condition monitoring platform is developed by integrating multiple sensing technologies. Complete Variational Mode Decomposition (CVMD) and Kernel Principal Component Analysis (KPCA) are employed to extract representative features from multi-physical-field data, while an attention-enhanced Long Short-Term Memory (LSTM) network is introduced for accurate fault identification. In addition, adaptive time-alignment and joint denoising algorithms are developed to improve data quality and diagnostic robustness. A predictive maintenance framework incorporating health assessment and remaining useful life prediction is further established to optimize maintenance scheduling. Results demonstrate that the proposed system achieves a fault prediction accuracy of over 90% and reduces annual maintenance costs by approximately 15–20%. The proposed framework provides an effective solution for intelligent operation and maintenance of modern pumped storage units. Full article

Accurate assessment of the stator-winding insulation condition in high-voltage machines is crucial for safe operation. Conventional evaluation methods treat the insulation as spatially uniform and provide only a holistic assessment; in practice, however, degradation within the insulation wall is non-uniform, with the inner layers adjacent to the copper conductor typically deteriorating more severely. As a result, existing diagnostic approaches cannot accurately evaluate non-uniform degradation. To address this gap, this paper proposes a diagnosis method for non-uniform aging of stator-winding insulation based on multi-indicator fusion of dielectric spectroscopy. A Frequency Domain Spectroscopy (FDS) simulation model that explicitly incorporates non-uniform aging is established and experimentally validated. The validated model is then used to generate FDS responses under various non-uniform-aging scenarios, from which multiple characteristic indicators that quantify the degree of non-uniformity are extracted. Building on these indicators, a non-uniform aging evaluation method using Multi-Anchor TOPSIS is constructed for diagnosing non-uniform aging. Full article

Power transformers are an integral part of electrical power system infrastructure and play a vital role in the efficient and reliable transmission of bulk electrical power to distribution networks. The careful use of these assets enables the optimization of transmission voltages in power networks, a crucial step for reducing electrical energy losses, enhancing grid reliability, and ensuring uninterrupted electricity supply to end-users in interconnected power networks. Operational reliability of power transformers is a critical aspect in ensuring a continuous power supply. The health index (HI) is a crucial diagnostic tool to determine their real condition. Historically, HI assessments were based on scoring and weighting. Lately, however, there has been a significant change in the attitude towards the use of artificial intelligence (AI) and machine learning (ML) to predict the health of high-voltage power transformers. Although developments are taking place, the existing studies on ML-based HI prediction models for power transformers largely rely on an incomplete dataset containing improper parameters. Moreover, dependency on traditional ML models is a significant limitation when it comes to achieving a higher degree of predictive accuracy. This article presents a sophisticated method for determining the overall health condition of power transformers. A total of twenty of the most appropriate and highly relevant input parameters were selected to effectively evaluate the transformer condition. The dataset for these parameters was collected from real-time testing in accordance with international industry standards (i.e., IEC, IEEE, and ASTM), conducted at 220 kV and 500 kV grid stations in the Multan and Lahore regions, operated by the National Grid Company (NGC) in Pakistan. This comprehensive dataset was fed to five state-of-the-art ML models. The Categorical Boosting Regression (CatBoost Regressor) model demonstrated superior performance, achieving the highest accuracy (R² Score) of 97.2% and the lowest mean absolute error (MAE) of 1.73. The best-performing model was then employed to predict the health index of the power transformers at the 500 kV grid station, Rahim Yar Khan, and the 500 kV grid station, Multan, as a practical case study. To demonstrate the economic importance of the proposed framework, an economic analysis was conducted via an iterative, parameter-skipping imputation strategy for maintenance cost optimization of the electrical power grid. The results verify that the omission of four diagnostic tests (i.e., Dissipation Factor, Capacitance, Insulation Resistance, and Transformer Turn Ratio) can reduce the economic burden by 46.99%, yielding a cost saving of 259,000 PKR per transformer unit. The implementation of this data-driven framework in the national grid can significantly reduce maintenance costs and facilitate an operational shift from traditional preventive maintenance to advanced predictive maintenance. Full article

Dissolved gas analysis (DGA) remains a cornerstone technique for the early detection of internal faults in power transformers. However, the interpretation of gas signatures is inherently complex due to coupled thermochemical processes, operating variability, and measurement uncertainties. Over the past three decades, artificial intelligence (AI) techniques have been widely applied to DGA-based diagnostics, evolving from conventional machine learning to advanced deep learning and hybrid models. Although many studies report diagnostic accuracies exceeding 95%, these results often rely on limited datasets and heterogeneous validation practices, raising concerns about their robustness in real-world operating conditions. This review provides a comprehensive and critical analysis of the evolution of AI-based approaches, with emphasis on methodological foundations rather than algorithmic performance alone. Key aspects examined include dataset construction, validation protocols, class imbalance handling, and experimental design. The analysis highlights a structural Accuracy-Robustness Gap, in which high reported accuracies often coexist with limited evidence of generalisation under realistic industrial variability. To address this issue, a conceptual framework is proposed linking the physical complexity of the oil–paper insulation system, dataset characteristics, and algorithmic modeling strategies. The review further identifies several key research gaps in benchmarking practices, dataset availability, robustness evaluation, and model interpretability. Based on these findings, a standardized evaluation framework is proposed to support more rigorous and comparable assessments of AI-based DGA diagnostic models. Future research directions are discussed, highlighting the need for physics-informed learning, distribution-aware validation, and shared datasets to enable reliable deployment of AI-based transformer diagnostics. Full article

Existing publicly available datasets for insulator recognition primarily focus on overhead transmission lines and are commonly acquired using unmanned aerial vehicles. As a result, they often do not reflect the visual complexity of high-voltage substation environments, which are characterized by dense equipment arrangements, structured industrial backgrounds, frequent occlusions, and substantial variation in object scale. To address this gap, we present UVInsDet, a real-world dataset for insulator-string detection and instance segmentation collected during ground-based robotic inspections of an operational 220 kV substation. The dataset comprises 591 visible-spectrum RGB images acquired using a narrow-angle diagnostic inspection camera and contains 1415 manually annotated insulator-string instances represented by pixel-wise segmentation masks. The images cover daytime and nighttime conditions, varying weather scenarios, different viewing angles, and both target-object and negative samples corresponding to realistic inspection workflows. The dataset includes annotations for glass and porcelain insulator strings and provides data in both LabelMe and COCO formats. UVInsDet is intended as a specialized resource for computer vision research in industrial inspection environments. The dataset can support the development and evaluation of object detection and instance segmentation methods, studies of small-object recognition in complex scenes, robustness assessment under varying observation conditions, domain adaptation research, and the development of intelligent monitoring and inspection systems for power infrastructure. Full article

Increasing power density of electrical devices in electrified transportation is an irreversible trend which involves power electronic-type supply, higher voltage and temperature. However, fast converter-switch rise times, high modulation and carrier frequencies, harmonics, and increased design field and temperature constitute potential causes of accelerated electrothermal aging of insulation, especially if harmful phenomena, as partial discharges (PDs), incept. This paper focuses on solving issues related to PD monitoring under power electronics waveforms, dealing with effective and automatic tools for noise rejection and for the identification of the type of source generating PD, the latter being fundamental for quality control, diagnostic and condition maintenance. It is shown that innovative techniques are available, which allow PD to be measured even under fast switching (rise time) and high frequency, separating, in the time domain, PD pulses from switching noise. This approach can be carried out automatically by the PD detector software presented here, not requiring experts for measurement management and, thus, making it a feasible tool also for on-line PD monitoring and condition-based maintenance. PD monitoring results from accelerated aging tests on a motor under pulse-width modulation (PWM supply) are presented. In order to assess the insulation health condition, progressive degradation of the motor is quantified using a dynamic health index (DHI), primarily based on key PD parameters, i.e., PD magnitude, repetition rate, and likelihood of discharge type (surface or internal). The proposed DHI approach not only provides meaningful metrics for translating PD data into a diagnostic tool, but it also offers insights into residual life estimation and failure risk prediction. Full article

Stator winding insulation failure is a leading cause of electric machine failure. Early detection of winding insulation deterioration is essential to preventing catastrophic damage and ultimate electric machine failure. Various condition monitoring and diagnostic methods have been developed to assess insulation health while the machine is in operation. These diagnostic methods depend on different signal processing techniques that are used to extract insulation-sensitive information from measured signals. This paper presents a review of the diagnostic signal processing techniques that have been applied to stator winding insulation condition monitoring, spanning time-domain, frequency-domain, time–frequency-domain and data-driven approaches. Where appropriate, the underlying mathematical formulation of the reviewed technique is presented, the physical basis for its sensitivity to insulation condition monitoring is discussed, and the key strengths and limitations are identified. A comparative analysis with summary tables is provided to highlight the trade-offs between detection sensitivity, computational cost, hardware requirements and practical deployment considerations. The review shows that time- and frequency-domain methods are simple to implement, while time–frequency and data-driven methods generally offer higher performance, but require greater computation and validation. Also, the comparison shows that turn-to-turn and groundwall insulation monitoring have received more research attention, while phase-to-phase remains less developed. This review concludes by identifying the challenges and future research directions needed to advance this field from laboratory demonstrations toward industrial adoption. Full article

Extreme and sudden temperature fluctuations observed as a result of global climate change increase the environmental pressure on energy transmission infrastructure. These meteorological changes significantly increase the risk of failure for porcelain insulators, which exhibit low thermal resistance and are susceptible to sudden arcing and surface deformations. In this study, a hybrid CNN–BiLSTM–SE architecture augmented with the Squeeze-and-Excitation attention mechanism is proposed using surface leakage current signals to diagnose healthy, cracked, and broken structural conditions in three-unit porcelain insulators. The SE block in the architecture dynamically rescales feature maps from CNN layers on a channel-by-channel basis. Thus, it highlights the signal characteristic that is dominant for fault diagnosis just before the BiLSTM units learn temporal dependencies. Leakage current data were obtained under an experimental setup at 60 kV for 15 different conditions covering all possible combinations of healthy, cracked, and broken insulator units. The raw signals were preprocessed with the Savitzky–Golay filter to suppress noise while preserving the diagnostic waveform morphology. 24 features covering time-domain statistics, frequency-domain spectral characteristics, and wavelet-domain energy components were extracted and used as model inputs. The CNN–BiLSTM–SE architecture achieved a classification accuracy of 93.83%, surpassing the standalone CNN (88.89%), BiLSTM (87.65%), and CNN–BiLSTM (91.36%) models, as well as classical machine-learning baselines (SVM: 87.65%, Random Forest: 90.12%, Boosted Trees: 87.65%). Full article

Inter-turn insulation failure is the primary cause of dry-type air-core reactor burnout, yet early detection remains challenging due to weak power-frequency fault signatures. This paper proposes an integrated diagnostic framework combining impulse oscillation testing, electromagnetic simulation, and a physics-informed graph neural network. A scaled-down four-layer parallel reactor model and an impulse oscillation platform are developed to extract dynamic equivalent inductance and resistance as sensitive fault indicators. Validated finite element simulations reveal that inter-layer insulation near high-voltage terminals endures the highest electric field stress, with local field strength increasing nearly eightfold under short-circuit faults. For fault localization, a Spatio-Temporal Physics-Informed Graph Neural Network (ST-PIGNN) is constructed, representing winding topology as a heterogeneous graph and embedding electromagnetic transient equations as physical constraints. On a test set of 120 samples, the proposed method achieves 94.17% fault layer classification accuracy and 6.84% axial localization mean absolute error under low-noise conditions, and maintains 85.83% accuracy with 8.12% error under strong-noise interference. The proposed method is currently at the proof-of-concept stage, and further validation on full-scale reactors is required before field deployment. Full article

The review presents a comprehensive review of the influence of power quality indicators and operating conditions at industrial enterprises on the technical condition and reliability of electrical equipment. Harmonic distortion, voltage fluctuations and sags, load surges, overvoltages, and voltage unbalance are considered factors that increase thermal, electrical, and mechanical stresses in transformers, induction motors, cable lines, and overhead power lines. It is shown that these disturbances can increase RMS currents, additional losses, hot-spot temperature, vibration, and insulation aging rate, reducing equipment service life and increasing failure probability. The review links power quality disturbances with thermal aging models, remaining useful life assessment, and probabilistic reliability models, including the Weibull distribution. It is established that a correct remaining service life assessment requires considering not only individual disturbances but also the combined influence of voltage and current quality, load conditions, ambient temperature, and humidity. Particular attention is paid to modern monitoring and forecasting technologies, including IoT systems, multi-agent models, machine learning, and predictive diagnostics. These technologies enable the transition from scheduled maintenance to continuous multiparameter monitoring. A structure for quantitative risk assessment and practical recommendations for predictive maintenance of industrial electrical equipment are proposed. Full article

The safe and stable operation of air-insulated switchgear (AIS) in high-altitude and low-pressure environments is significantly affected by partial discharge (PD), which accelerates insulation aging and may threaten power system reliability. Therefore, effective online monitoring and fault diagnosis methods are of considerable engineering importance. This paper proposes a PD-type recognition method based on the concentration ratio of two characteristic decomposition gases, CO and NO₂. First, a hybrid numerical model coupling fluid dynamics and plasma chemistry was established to simulate the microscopic decomposition mechanism of air discharge. The simulation results indicate that CO and NO₂ are relatively stable and detectable among the considered air-discharge products and that their generation is promoted by increased average electron energy under low-pressure conditions. Subsequently, an experimental platform was developed to simulate three typical insulation defects, namely point discharge, air-gap discharge, and surface discharge, under different simulated altitudes. Quantitative analysis using Fourier-transform infrared spectroscopy and gas chromatography revealed clear correlations between defect type and gas concentration characteristics. Based on these results, a diagnostic criterion was established under the tested conditions: a CO/NO₂ concentration ratio less than 1 indicates the epoxy-resin-based surface discharge model, whereas a ratio greater than 1 indicates point discharge or air-gap discharge. The latter two types can be further distinguished according to the time-dependent increasing trend of the ratio for air-gap discharge. Finally, based on the observed diffusion characteristics of these gases in the laboratory switchgear model, a low-cost online detection prototype using semiconductor gas sensors was developed. Laboratory validation using three typical single-defect models showed that the proposed method achieved 100% recognition accuracy when sufficient time-series data were available. However, further field validation is required before large-scale industrial application. The proposed CO/NO₂ ratio method provides a potential low-cost auxiliary diagnostic approach for AIS insulation monitoring, particularly under high-altitude and low-pressure conditions. Full article

Oil-filled current transformers are crucial in high-voltage substations, directly affecting grid safety and reliability. Traditional defect diagnosis methods often show low accuracy and limited monitoring coverage, failing to meet operation and maintenance requirements. This paper proposes an AI-empowered digital twin-based defect diagnosis method that addresses typical issues like oil leakage, insulation damage, and moisture ingress by extracting relevant characteristic parameters to create an evaluation index system. A digital twin model integrates winding, core, and thermal flow characteristics, enabling real-time acquisition of operation parameters and precise mapping between physical and virtual transformers. A dual-model AI framework using Extreme Gradient Boosting (XGBoost) and Support Vector Machine (SVM) is introduced for intelligent defect identification and early defect prediction through multi-source data fusion. Finally, a corresponding diagnostic system is developed and verified using actual operation data from a 220 kV substation in Liaoning Province. The results show that the proposed method enables the online monitoring of multiple operating parameters, and the dual-model framework exhibits higher diagnostic accuracy and faster computation speed compared with Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), providing effective support for intelligent condition-based maintenance of current transformers. Full article

High-sensitivity detection of direct current (DC) partial discharge (PD) in HVDC gas-insulated equipment (GIE) remains challenging because conventional electrical measurements are susceptible to ambient interference and DC PD lacks a phase reference for phase-resolved analysis. Although photon counting techniques provide exceptional sensitivity and noise immunity, their diagnostic application has so far been confined to alternating current (AC) conditions. In this study, a photon-counting-based measurement platform was developed to investigate DC PD generated by three representative gas–solid insulation defects, namely conductor protrusion, surface-attached metal, and free metallic particle. Photon pulse sequences were acquired under both positive and negative voltage polarities. Successive inter-pulse time intervals were then mapped into two-dimensional kernel density estimation heatmaps to visualize defect-dependent temporal characteristics. A Random Forest classifier, integrated with SHapley Additive exPlanations (SHAP) for feature reduction, was employed for quantitative classification. The proposed method achieved classification accuracies of 97.50% and 99.17% for positive and negative polarities, respectively. Notably, the model adaptively prioritized angular-distribution features over radial-distribution features under space-charge-suppressed conditions. These results demonstrate the feasibility of photon-counting-based time-domain characterization and defect classification for DC PD, providing a quantitative, less experience-dependent framework for insulation defect identification in DC gas-insulated systems. Full article

To address the challenges of extracting mechanical fault features in Gas Insulated Switchgear (GIS) under complex operating conditions and the insufficient diagnostic accuracy associated with traditional one-dimensional time-series signals, this paper proposes a GIS fault-classification method based on composite dimensional upscaling images of vibration signals and the Vision Transformer (ViT) algorithm. This method first employs a sliding window slicing strategy to segment the raw long-sequence vibration signals into multiple overlapping time segments. Then, it utilizes the Gramian Angular Summation Field (GASF), Gramian Angular Difference Field (GADF), and Markov Transition Field (MTF) to perform composite dimensional upscaling on these segmented signals, projecting the resulting features into a three-channel RGB composite two-dimensional image. Subsequently, the global self-attention mechanism of the Vision Transformer (ViT) processes the dimensionally upscaled data to achieve the fault classification of the GIS equipment. Experimental results demonstrate that, compared to single-channel ViT variants, Convolutional Neural Networks (CNN), and Residual Networks (ResNet), the proposed algorithm achieves the highest overall performance in the training set experiments, and the superiority of this method is verified through ablation studies and comparative experiments. Furthermore, the average accuracy of the algorithm on the testing set reaches 95.63%, proving the reliability and accuracy of the proposed method. Full article

This study integrates in situ Quantitative Infrared Thermography (QIRT) and Building Energy Simulation (BES) to optimize the energy performance of an existing multi-story residential building in Istanbul, Türkiye. QIRT was utilized to diagnose thermal anomalies at the interfaces of uninsulated walls, the RC skeleton and fenestration junctions, revealing significant thermal bridging and air infiltration while enabling the calculation of the Temperature Index (TI) at critical interfaces. A key finding of the non-destructive diagnostic phase was the discrepancy between in situ (U_INSITU) and theoretical (U_CALC) thermal transmittance values, providing an empirical baseline for subsequent optimization. A multi-objective analysis, employing genetic algorithms (GAs), was conducted to evaluate 192 retrofit combinations, involving three insulation materials at four thicknesses and 16 glazing types. The impacts on primary energy consumption, CO₂ emissions, and 30-year global costs (per EN 15459-1:2017) were quantified under volatile economic conditions. Findings indicate that the energy-optimal solution reduces primary energy by 53% and CO₂ emissions by 51%, while the cost-optimal configuration reduces global costs by 52% relative to the reference case. The Pareto analysis reveals a robust convergence between financial and energy efficiency targets, proving that deep retrofitting is an economically imperative strategy for achieving national decarbonization goals and the 2053 net-zero vision. Full article