Search Results (163)

This article presents a hybrid fault diagnosis framework for DC–DC converters in photovoltaic (PV) systems, combining digital twin (DT) modelling and detection with machine learning anomaly classification. The proposed method addresses both hardware faults such as open and short circuits in insulated-gate bipolar transistors (IGBTs) and diodes and sensor-level false data injection attacks (FDIAs). A five-dimensional DT architecture is employed, where a virtual entity implemented using FMI-compliant FMUs interacts with a real-time emulated physical plant. Fault detection is performed by comparing the real-time system behaviour with DT predictions, using dynamic thresholds based on power, voltage, and current sensors errors. Once a discrepancy is flagged, a second step classifier processes normalized time-series windows to identify the specific fault type. Synthetic training data are generated using emulation models under normal and faulty conditions, and feature vectors are constructed using a compact, interpretable set of statistical and spectral descriptors. The model was validated using OPAL-RT Hardware in the Loop emulations. The results show high classification accuracy, robustness to environmental fluctuations, and transferability across system configurations. The framework also demonstrates compatibility with low-cost deployment hardware, confirming its practical applicability for fault diagnosis in real-world PV systems. Full article

The significant waste generated by construction has increased interest in sustainable solutions, including prefabricated interior partition panels. Although different types of alternative panels have been proposed, their performance as interior partitions remains underexplored in systematic comparative studies. To narrow this knowledge gap, this paper presents a comprehensive evaluation and classification of drywall, OSB (Oriented Strand Board), cement–wood, and honeycomb panels, regarding physical, mechanical, microstructural, thermal, acoustic, and combustibility characteristics, in addition to conducting a cost evaluation. The results indicated that the OSB panels exhibited superior results for interior partition applications, showing notable advantages in physical strength, mechanical performance, and thermal insulation, while offering acoustic properties comparable to those of drywall panels. Nevertheless, OSB panels showed lower fire resistance and were associated with the highest cost among the materials analyzed in the present research. Drywall panels, on the other hand, provided the most favorable fire resistance but exhibited the least effective thermal insulation. The findings also indicated that both wood–cement and honeycomb panels require further improvements in their manufacturing processes to meet performance standards suitable for interior partition. Full article

Dissolved gas analysis remains the most widely utilized non-intrusive diagnostic method for detecting incipient faults in insulating liquid-immersed transformers. Despite their prevalence, conventional ratio-based methods often suffer from ambiguity and limited potential for automation applicrations. To address these limitations, this study proposes a unified multiclass classification model that integrates traditional gas ratio features with supervised machine learning algorithms to enhance fault diagnosis accuracy. The performance of six machine learning classifiers was systematically evaluated using training and testing data generated through four widely recognized gas ratio schemes. Grid search optimization was employed to fine-tune the hyperparameters of each model, while model evaluation was conducted using 10-fold cross-validation and six performance metrics. Across all the diagnostic approaches, ensemble models, namely random forest, XGBoost, and LightGBM, consistently outperformed non-ensemble models. Notably, random forest and LightGBM classifiers demonstrated the most robust and superior performance across all schemes, achieving accuracy, precision, recall, and F1 scores between 0.99 and 1, along with Matthew correlation coefficient values exceeding 0.98 in all cases. This robustness suggests that ensemble models are effective at capturing complex decision boundaries and relationships among gas ratio features. Furthermore, beyond numerical classification, the integration of physicochemical and dielectric properties in this study revealed degradation signatures that strongly correlate with thermal fault indicators. Particularly, the CIGRÉ-based classification using a random forest classifier demonstrated high sensitivity in detecting thermally stressed units, corroborating trends observed in chemical deterioration parameters such as interfacial tension and CO₂/CO ratios. Access to over 80 years of operational data provides a rare and invaluable perspective on the long-term performance and degradation of power equipment. This extended dataset enables a more accurate assessment of ageing trends, enhances the reliability of predictive maintenance models, and supports informed decision-making for asset management in legacy power systems. Full article

The insulation layer of flame-retardant cables plays a critical role in mitigating fire hazards by influencing toxic gas emissions and the accuracy of fire modeling. This study systematically explores the pyrolysis kinetics and volatile gas evolution of flame-retardant polyvinyl chloride (PVC) and polyethylene (PE) insulation materials using advanced TG-FTIR-GC/MS techniques. Distinct pyrolysis stages were identified through thermogravimetric analysis (TGA) at heating rates of 10–40 K/min, while the KAS model-free method and Málek fitting function quantified activation energies and reaction mechanisms. Results revealed that flame-retardant PVC undergoes two major stages: (1) dehydrochlorination, characterized by the rapid release of HCl and low activation energy, and (2) main-chain scission, producing aromatic compounds that contribute to fire toxicity. In contrast, flame-retardant PE demonstrates a more stable pyrolysis process dominated by random chain scission and the formation of a dense char layer, significantly enhancing its flame-retardant performance. FTIR and GC/MS analyses further highlighted distinct gas evolution behaviors: PVC primarily generates HCl and aromatic hydrocarbons, whereas PE releases olefins and alkanes with significantly lower toxicity. Additionally, the application of a classification and regression tree (CART) model accurately predicted mass loss behavior under various heating rates, achieving exceptional fitting accuracy (R² > 0.98). This study provides critical insights into the pyrolysis mechanisms of flame-retardant cable insulation and offers a robust data framework for optimizing fire modeling and improving material design. Full article

The awareness of global warming has boosted research on methods to reduce energy consumption and greenhouse gas (GHG) emissions. Livestock buildings, although essential for food production, represent a sustainability challenge due to their high maintenance energy costs, GHG emissions, and impact on the environment and rural landscapes. Since the environment, cultural heritage, and community identity deserve protection, research trends and current knowledge on livestock buildings, building sustainability, energy efficiency strategies, and landscape management were investigated using the Web of Science and Scopus search tools (2005–2025). Research on these topics was found to be uneven, with limited focus on livestock buildings compared to food production and animal welfare, and significant interest in eco-sustainable building materials. A total of 96 articles were selected after evaluating over 5400 records. The analysis revealed a lack of universally accepted definitions for building design strategies and their rare application to livestock facilities, where passive solutions and insulation prevailed. The application of renewable energy was rare and limited to rural buildings, as was the application of sustainable building materials to livestock, agriculture, and vernacular buildings. Conversely, increased attention was paid to the definition and classification of vernacular architecture features aimed at enhancing existing buildings and mitigating or facilitating the landscape integration of those that diverge most from them. Although not exhaustive, this review identified some knowledge gaps. More efforts are needed to reduce environmental impacts and meet the milestones set by international agreements. Research on building materials could benefit from collaboration with experts in cultural heritage conservation because of their command of traditional materials, durability-enhancing methods, and biodeterioration. Full article

The building and construction sector significantly impacts CO₂ emissions and atmospheric pollutants, contributing to climate change. Improving energy efficiency in buildings is essential to achieving carbon neutrality by 2050, as outlined in the European Green Deal. This study presents a decision-support tool for energy retrofit interventions in existing residential buildings. The methodological approach begins with the identification and classification of common roof and wall types in the national residential building stock, segmented by construction period, followed by defining optimized, pre-calculated standardized solutions. The performance evaluations of proposed solutions resulted in a matrix designed to guide practitioners in selecting pre-calculated, efficient, and sustainable prefabricated solutions based on energy performance criteria. The tool developed from this matrix enables preliminary energy assessment, offering an overview of potential retrofit interventions. It assists designers in identifying specific cases based on construction period, building type, and climate zone, allowing for the selection of standardized solutions, energy pre-analyses, energy and cost-saving simulations, and access to detailed performance sheets. Unlike other tools requiring extensive input on opaque envelope components and thermo-physical calculations, this tool streamlines the selection process of vertical and roof closures based on construction age and building type. Additionally, the tool estimates potential economic savings and the Net Present Value (NPV) of proposed insulation solutions, identifying available incentives for the intervention. Full article

This paper presents a comprehensive review of High-Voltage Direct-Current (HVDC) systems, focusing on their technological evolution, fault characteristics, and advanced signal processing techniques for fault detection. The paper traces the development of HVDC links globally, highlighting the transition from mercury-arc valves to Insulated Gate Bipolar Transistor (IGBT)-based converters and showcasing operational projects in technologically advanced countries. A detailed comparison of converter technologies including line-commutated converters (LCCs), Voltage-Source Converters (VSCs), and Modular Multilevel Converters (MMCs) and pole configurations (monopolar, bipolar, homopolar, and MMC) is provided. The paper categorizes HVDC faults into AC, converter, and DC types, focusing on their primary locations and fault characteristics. Signal processing methods, including time-domain, frequency-domain, and time–frequency-domain approaches, are systematically compared, supported by relevant case studies. The review identifies critical research gaps in enhancing the reliability of fault detection, classification, and protection under diverse fault conditions, offering insights into future advancements in HVDC system resilience. Full article

The perceived acoustic comfort on board modern yachts has recently been the subject of specific attention by the most important classification societies, which have issued new guidelines and regulations for the evaluation of noise and vibrations. The evaluation of the acoustic insulation performance of the internal partitions of yachts is, therefore, a very current topic. The estimation of the acoustic performance of internal partitions can be very complex; on the one hand, on-board measurements can be extremely difficult, but on the other hand, manual or software calculation is extremely complex or potentially affected by non-negligible errors, which is also due to the high amount of highly detailed information required. This paper explores the possibility of using simplified models, commonly used in building construction, to determine the acoustic insulation of the internal partitions of yachts in the design phase, without having to resort, even from the beginning, to very advanced calculation tools such as those based on the Finite Elements Method or Statistical Energy Analysis. Using a 44 m yacht as a case study, this paper presents the results of a series of acoustic simulations of single partitions and compares them with the results of an on-board measurement campaign. From the comparison of the obtained results, it was possible to state that the simulations of single partitions (therefore, those not of the whole vessel) can be useful in the design phase to verify compliance with the acoustic requirements requested by the classification societies. Considering that the propagation of sound and vibrations through the structures is a determining factor for the correct acoustic design of the vessel and therefore for the achievement of adequate levels of acoustic comfort, the analysis with simplified models (which consider the single partition) can be extremely useful in the preliminary phase of the design process. Subsequently, starting from the data acquired in the first simulation phase, it is possible to proceed with more complex simulations of specific situations and of the whole vessel. Full article

Transformer winding faults (TWFs) can lead to insulation breakdown, internal short circuits, and catastrophic transformer failure. Due to their low current magnitude—particularly at early stages such as inter-turn short circuits, axial or radial displacement, or winding looseness—TWFs often induce minimal impedance changes and generate fault currents that remain within normal operating thresholds. As a result, conventional protection schemes like overcurrent relays, which are tuned for high-magnitude faults, fail to detect such internal anomalies. Moreover, frequency response deviations caused by TWFs often resemble those introduced by routine phenomena such as tap changer operations, load variation, or core saturation, making accurate diagnosis difficult using traditional FRA interpretation techniques. This paper presents a novel diagnostic framework combining Discrete Wavelet Transform (DWT) and Support Vector Machine (SVM) classification to improve the detection of TWFs. The proposed system employs region-based statistical deviation labeling to enhance interpretability across five well-defined frequency bands. It is validated on five real FRA datasets obtained from operating transformers in Gauteng Province, South Africa, covering a range of MVA ratings and configurations, thereby confirming model transferability. The system supports post-processing but is lightweight enough for near real-time diagnostic use, with average execution time under 12 s per case on standard hardware. A custom graphical user interface (GUI), developed in MATLAB R2022a, automates the diagnostic workflow—including region identification, wavelet-based decomposition visualization, and PDF report generation. The complete framework is released as an open-access toolbox for transformer condition monitoring and predictive maintenance. Full article

The polymeric insulating materials widely used in measuring instrument enclosures must meet specific flammability requirements. In this study, we systematically assessed the impact of minor compositional changes, such as pigments and fillers, on horizontal burning (HB) classification according to the EN 60695-11-10 standard. We tested 64 polymer combinations at thicknesses of 1.5 mm and 3.0 mm, classifying samples into HB, HB40 or HB75 categories. The results demonstrated that additives significantly influenced the HB classifications more than thickness. Specifically, we classified 31 samples as HB, 16 as HB40, 15 as HB75, while two were unclassifiable. Several material groups consistently achieved specific HB classifications regardless of minor additive variations. These findings offer manufacturers clear guidance for selecting polymer-additive systems, facilitating informed decisions and enabling a streamlined “worst-case” testing strategy. Ultimately, this approach enhances manufacturers’ ability to efficiently achieve product safety compliance, reducing certification costs without compromising safety. Full article

Partial discharge (PD) analysis is critical for diagnosing insulation degradation in high-voltage equipment. While conventional methods struggle with multi-source PD classification due to signal overlap and noise, this study proposes a hybrid approach combining five time–frequency analysis (TFA) techniques with deep learning (GoogLeNet for simulation, ResNet50 for experiments). PD data are generated through Finite Element Method (FEM) simulations and validated via laboratory experiments. The Scatter Wavelet Transform (SWT) achieves 96.67% accuracy (F1-score: 0.967) in simulation and perfect 100% accuracy (F1-score: 1.000) in experiments, outperforming other TFAs like HHT (70.00% experimental accuracy). The Wigner–Ville Distribution (WVD) also shows strong experimental performance (94.74% accuracy, AUC: 0.947), though its computational complexity limits real-time use. These results demonstrate the SWT’s superiority in handling real-world noise and multi-source PD signals, providing a robust framework for insulation diagnostics in power systems. Full article

This paper introduces an innovative, environmentally sustainable, and climatic study analysing the impact of overhang depths on heating and cooling building energy demands in the Mediterranean Basin via dynamic energy simulations of a south-oriented reference residential building zone. The adopted bioclimatic approach aims at increasing building sustainability and suggests, for representative Köppen–Geiger climate subtypes, optimal overhang depths and climate-correlated depth domains. The definition of a large geoclimatic study based on 80 locations and the classification of results based on climate subtypes are two novelties introduced in this work. From the energy point of view, overhangs can reduce local building cooling needs by, on average, 27%, while decreasing the total final energy needs (Q_TOT) by 17%. A new approach is also introduced: comparing the energy reduction due to the addition of an overhang to commonly applied envelope retrofitting solutions, such as wall insulation or window substitutions. Overhangs show great potential in sites with arid climate subtypes and are more effective than other solutions in several locations. This study underlines the need to increase the adoption of passive cooling solutions by local retrofitting regulations in places with a Mediterranean climate, following a bio-regionalist approach able to increase the local buildings’ sustainable development. Full article

To address the challenges in insulator defect detection for transmission lines, including complex background interference, varying defect region scales, and sample imbalance, we propose a detection method that effectively integrates scene perception information and detailed content guidance. First, a scene perception enhancement module is employed to extract global environmental information, improving the baseline model’s adaptability to complex backgrounds. Second, a detailed content attention module is introduced to enable the model to more accurately capture fine-grained features of small defect regions. Furthermore, a normalized Wasserstein distance metric function is adopted to mitigate the sensitivity of the regression branch in the baseline model. Simultaneously, a sample weighting function is utilized to reduce the impact of sample imbalance on the classification branch. Experimental results demonstrate that the proposed method achieves superior detection performance on a real-world transmission line insulator defect dataset. Full article

Modeling many-body quantum systems is widely regarded as one of the most promising applications for near-term noisy quantum computers. However, in the near term, system size limitation will remain a severe barrier for applications in materials science or strongly correlated systems. A promising avenue of research is to combine many-body physics with machine learning for the classification of distinct phases. We present a workflow that synergizes quantum computing, many-body theory, and quantum machine learning (QML) for studying strongly correlated systems. In particular, it can capture a putative quantum phase transition of the stereotypical strongly correlated system, the Hubbard model. Following the recent proposal of the hybrid quantum-classical algorithm for the two-site dynamical mean-field theory (DMFT), we present a modification that allows the self-consistent solution of the single bath site DMFT. The modified algorithm can be generalized for multiple bath sites. This approach is used to generate a database of zero-temperature wavefunctions of the Hubbard model within the DMFT approximation. We then use a QML algorithm to distinguish between the metallic phase and the Mott insulator phase to capture the metal-to-Mott insulator phase transition. We train a recently proposed quantum convolutional neural network (QCNN) and then utilize the QCNN as a quantum classifier to capture the phase transition region. This work provides a recipe for application to other phase transitions in strongly correlated systems and represents an exciting application of small-scale quantum devices realizable with near-term technology. Full article

Significant amounts of coal fly and bottom ash are generated globally each year, with especially large quantities of bottom ash accumulating in landfills. In this study, fly ash and bottom ash were used to create fire-resistant materials. A mix of 30 wt% gypsum, 9.5 wt% vermiculite, and 0.5 wt% polypropylene fibers was used, maintaining a constant water-to-solid ratio, with varying fly ash/bottom ash ratios (40/20, 30/30, and 20/40). The density, as well as various mechanical properties (compressive strength, flexural strength, and surface hardness), fire insulation capacity, and leaching behavior of both ashes were evaluated. When comparing the 40/20 and 20/40 compositions, a slight decrease in density was observed; however, compressive strength dropped drastically by 80%, while flexural strength decreased slightly due to the action of the polypropylene fibers, and fire resistance dropped by 8%. Neither of the ashes presented any environmental concerns from a leaching standpoint. Additionally, historical data from various materials with different wastes in previous works were used to train different machine learning models (random forest, gradient boosting, artificial neural networks, etc.). Compressive strength and fire resistance were predicted. Simple parameters (density, water/solid ratio and composition for compressive strength and thickness and the composition for fire resistance) were used as input in the models. Both regression and classification algorithms were applied to evaluate the models’ ability to predict compressive strength. Regression models for fire resistance reached r² up to about 0.85. The classification results for the fire resistance rating (FRR) showed high accuracy (96%). The prediction of compressive strength is not as good as the fire resistance prediction, but compressive strength classification reached up to 99% accuracy for some models. Full article