Search Results (300)

The smart grid concept is based on the full integration of different types of energy sources and intelligent devices. Due to the short- and long-term volatility of these sources, new flexibility measures are necessary to ensure the smart grid operates stably and reliably. One option is to convert renewable energy into hydrogen, especially during periods of generation overcapacity, in order that the hydrogen that is produced can be stored effectively and used “just in time” to stabilize the power system by undergoing a reverse conversion process in gas turbines or fuel cells which then supply power to the network. On the other hand, in order to achieve a sustainable general energy system (GES), it is necessary to replace other forms of fossil energy use, such as that used for heating and other industrial processes. Research indicates that a comprehensive hydrogen supply infrastructure is required. This infrastructure would include electrolyzers, conversion stations, pipelines, storage facilities, and hydrogen gas turbines and/or fuel cell power stations. Some studies in Germany suggest that the existing gas infrastructure could be used for this purpose. Further, nuclear and coal power plants are not considered reserve power plants (as in the German case), and an additional 20–30 GW of generation capacity in H₂-operated gas turbines and strong H₂ transportation infrastructure will be required over the next 10 years. The novelty of the approach presented in this article lies in the development of a unified modeling framework that enables the simultaneous and coherent representation of both economic and technical aspects of hydrogen production systems which will be used for planning and pre-decision making. From the technical perspective, the model, based on the black box approach, captures the key operational characteristics of hydrogen production, including energy consumption, system efficiency, and operational constraints. In parallel, the economic layer incorporates capital expenditures (CAPEX), operational expenditures (OPEX), and cost-related performance indicators, allowing for a direct linkage between technical operation and economic outcomes. This paper describes the systematic transformation from today’s power system to one that includes a hydrogen economy, with a particular focus on practical experiences and developments, especially in the German energy system. It discusses the components of this new system in depth, focusing on current challenges and applications. Some scaled current applications demonstrate the state of the art in this area, including not only technical requirements (reliability, risks) and possibilities, but also economic aspects (cost, business models, impact factors). Full article

Unplanned downtime in industrial machinery remains a major challenge, causing substantial economic losses and safety risks across sectors such as manufacturing, food processing, oil and gas, and transportation. This systematic review investigates the application of machine learning (ML) techniques for anomaly and fault detection within the broader context of predictive maintenance. Following a hybrid review methodology, relevant studies published between 2010 and 2025 were collected from major databases including IEEE Xplore, ScienceDirect, SpringerLink, Scopus, Web of Science, and arXiv. The review categorizes approaches into supervised, unsupervised, and hybrid paradigms, analyzing their pipelines from data collection and preprocessing to model deployment. Findings highlight the effectiveness of deep learning architectures such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), autoencoders, and hybrid frameworks in detecting faults from time series and multimodal sensor data. At the same time, key limitations persist, including data scarcity, class imbalance, limited generalizability across equipment types, and a lack of interpretability in deep models. This review concludes that while ML-based predictive maintenance systems are enabling a transition from reactive to proactive strategies, future progress requires improved hybrid architectures, Explainable AI, and scalable real-time deployment. Full article

Gas-insulated transmission lines (GILs) are increasingly deployed in dense urban power networks, where complex construction activities may introduce external mechanical impacts and pose risks to pipeline structural integrity. However, existing GIL monitoring approaches mainly emphasize electrical and gas-state parameters, while lightweight solutions capable of rapidly detecting and localizing impact-induced structural anomalies remain limited. To address this gap, this paper proposes an intelligent end-edge-cloud monitoring system for impact anomaly detection in GIL pipelines. Numerical simulations are first conducted to analyze the dynamic response characteristics of the pipeline under impacts of varying magnitudes, orientations, and locations, revealing the relationship between impact scenarios and vibration mode evolution. An end-tier multi-parameter intelligent sensor is then developed, integrating triaxial acceleration and angular velocity measurement with embedded lightweight computing. Laboratory impact experiments are performed to acquire sensor data, which are used to train and validate a multi-class extreme gradient boosting (XGBoost) model deployed at the edge tier for accurate impact-location identification. Results show that, even with a single sensor positioned at the pipeline midpoint, fusing acceleration and angular velocity features enables reliable discrimination of impact regions. Finally, a lightweight cloud platform is implemented for visualizing structural responses and environmental parameters with downsampled edge-side data. The proposed system achieves rapid sensor-level anomaly detection, precise edge-level localization, and unified cloud-level monitoring, offering a low-cost and easily deployable solution for GIL structural health assessment. Full article

Traditional methods of asset management in electric power systems rely upon fixed schedules and reactive measurements, leading to challenges in the transparent prioritization of maintenance under evolving operating conditions and incomplete data. In this paper, we introduce a new, fully integrated artificial intelligence (AI)-driven approach for enhancing the resilience and reliability of open-source asset management tools to support improved performance and decisions in electric power system operations. This methodology addresses and overcomes several significant challenges, including data heterogeneity, algorithmic limitations, and inflexible decision-making, through a three-module workflow. The data fidelity module provides a domain-aware pipeline for identifying structural (missing) values from explicit missingness using sophisticated imputation methods, including Multiple Imputation Chain Equations (MICE) and Generative Adversarial Network (GAN)-based hybrids. The characterization module employs seven complementary weighting strategies, including PCA, Autoencoder, GA-based optimization, SHAP, Decision-Tree Importance, and Entropy Weighting, to achieve objective feature weight assignment, thereby eliminating the need for subjective manual rules. The optimization module enhanced the action space through multi-objective optimization, balancing reliability maximization and cost minimization. A synthetic dataset of 100 power transformers was used to validate that the MICE achieved better imputation than other methods. The optimized weighting framework successfully categorizes Health Index values into five condition levels, while the multi-objective maintenance policy optimization generates decisions that align with real-world asset management practices. The proposed framework provides the Transmission and Distribution System Operators (TSOs/DSOs) with an adaptable, industry-oriented decision-support workflow system for enhancing reliability, optimizing maintenance expenses, and improving asset management policies for critical power infrastructure. Full article

With the rapid expansion of high-speed railway (HSR) networks, the vibration impact on adjacent energy infrastructure has become a critical safety concern. However, existing research lacks a comprehensive evaluation of buried sour gas pipelines specifically in tunnel-undercrossing scenarios. This research investigates the dynamic response characteristics of a sour natural gas pipeline under train-induced vibration loads using a case study in Chongqing. A three-dimensional dynamic coupling model of the track lining soil pipeline system was established based on FLAC-3D. The study innovatively quantifies the vibration superposition effect during bidirectional train encounters and assesses safety using fatigue life and velocity thresholds. Results indicate that pipeline vibration is predominantly vertical. As train speed increases from 250 km/h to 350 km/h, the response exhibits a non-linear rapid growth within the 300–350 km/h range. Under bidirectional encounters, the peak displacement reaches 2.00 times that of unilateral passage, representing the most critical load condition. The maximum peak vibration velocity is 0.1 mm/s, far below the 2 mm/s safety threshold, ensuring structural integrity under current operational standards. Full article

The leakage detection of oil and gas is very important for the safe operation of pipelines. The existing working condition recognition methods have limitations in processing and capturing complex multi-category leakage signal characteristics. In order to improve the accuracy of oil and gas pipeline leakage detection, a multi-scale convolutional neural network-Transformer (MSCNN-Transformer)-based oil and gas pipeline leakage condition recognition method is proposed. Firstly, in order to capture the global information and nonlinear characteristics of the time series signal, STFT is used to generate the time-frequency image. Furthermore, in order to enrich the feature information from different dimensions, the one-dimensional signal and the two-dimensional time-frequency image are sampled by multi-scale convolution, and the global relationship is established by combining the multi-head attention mechanism of the Transformer module. Finally, the leakage signal is accurately identified by fusing features and classifiers. The experimental results show that the proposed method shows high performance on the GPLA-12 data set, and the recognition accuracy is 96.02%. Compared with other leakage signal recognition methods, the proposed method has obvious advantages. Full article

This study aims to demonstrate the presence of a pronounced coseismic increase in atmospheric methane concentrations during the 2024 Noto Peninsula Earthquake and to examine whether this increase may have originated from underground natural gas release. By analyzing hourly CH₄ data from the Ministry of the Environment’s monitoring network, this study shows that significant methane increases occurred only in areas with seismic intensity of 6– or greater, and that an exceptional anomaly—reaching 29 times the standard deviation of the past year—was recorded at the Nanao station. The validity of this anomaly was confirmed through consultation with local atmospheric officer, and high-time-resolution data (6 min values) were provided, verifying continuous instrument operation. Detailed analysis further shows that two major methane peaks occurred, each rising not immediately after the main shock but synchronously with two large aftershocks approximately 8 and 44 min later. Geological and hydrogeological information indicates the presence of water-soluble gas and unsaturated hydrocarbons beneath the Nanao region, suggesting that seismic shaking may have ruptured clay layers and released accumulated gas. Analyses of public reports and interviews with local officials show that alternative explanations—such as fire smoke, pipeline rupture, instrument malfunction, and gas-cylinder damage—were unlikely. These findings indicate that the observed methane anomaly was most likely caused by earthquake-synchronous underground gas release, suggesting that methane-release risk should be considered in post-earthquake fire-hazard assessments. Full article

Europe’s energy transition beyond 2025 faces a resilience gap as reconfigured pipeline flows, stricter methane rules, and rising variable renewables increase the need for seasonal flexibility and system adequacy. This study examines how Ukraine’s gas transmission network and underground gas storage—among the largest in Europe—can serve as a “seasonal battery” for the EU. We integrate a policy and market review with quantitative scenarios for 2026–2030. Methods include security-of-supply indicators (the rule that the system must keep operating even if its largest single infrastructure element fails, peak-day coverage, and winter adequacy), estimates of market-accessible storage volumes and withdrawal rates for European market participants, and a techno-economic screening of hydrogen-readiness comparing repurposing with new-build options. Methane intensity constraints and compliance with monitoring, reporting, and verification and leak detection and repair requirements are applied. The results indicate that reallocating part of Europe’s seasonal balancing to Ukrainian underground gas storage can enhance resilience to extreme winter demand and liquefied natural gas price shocks, reduce price volatility and the curtailment of variable renewables, and enable phased, cost-effective hydrogen corridors via repurposable pipelines and compressors. We outline a policy roadmap specifying transparent access rules, interoperable gas quality and methane standards, and risk mitigation instruments needed to operationalise cross-border storage and hydrogen-ready investments without carbon lock-in. Full article

Uniform application of fertilizers and pesticides continues to dominate global agriculture despite significant spatial variability in soil and crop conditions. This mismatch results in avoidable yield gaps, excessive chemical waste, and environmental pressures, including nutrient leaching and greenhouse gas emissions. The integration of Artificial Intelligence (AI) and Remote Sensing (RS) has emerged as a transformative framework for diagnosing this variability and enabling site-specific, climate-responsive management. This systematic synthesis reviews evidence from 2000–2025 to assess how AI–RS technologies optimize agrochemical efficiency. A comprehensive search across Scopus, Web of Science, IEEE Xplore, ScienceDirect, and Google Scholar were used. Following rigorous screening and quality assessment, 142 studies were selected for detailed analysis. Data extraction focused on sensor platforms (Landsat-8/9, Sentinel-1/2, UAVs), AI approaches (Random Forests, CNNs, Physics-Informed Neural Networks), and operational outcomes. The synthesized data demonstrate that AI–RS systems can predict critical soil attributes, specifically salinity, moisture, and nutrient levels, with 80–97% accuracy in some cases, depending on spectral resolution and algorithm choice. Operational implementations of Variable-Rate Application (VRA) guided by these predictive maps resulted in fertilizer reductions of 15–30%, pesticide use reductions of 20–40%, and improvements in water-use efficiency of 25–40%. In fields with high soil heterogeneity, these precision strategies delivered yield gains of 8–15%. AI–RS technologies have matured from experimental methods into robust tools capable of shifting agrochemical science from reactive, uniform practices to predictive, precise strategies. However, widespread adoption is currently limited by challenges in data standardization, model transferability, and regulatory alignment. Future progress requires the development of interoperable data infrastructures, digital soil twins, and multi-sensor fusion pipelines to position these technologies as central pillars of sustainable agricultural intensification. Full article

In the context of Industry 4.0, new methods of manufacturing, monitoring, and data generation related to industrial processes have emerged. Over the last decade, a new method of part manufacturing that has been revolutionizing the industry is Additive Manufacturing, which comes in various forms, including the more traditional Fusion Deposition Modeling (FDM) and the more innovative ones, such as Laser Metal Deposition (LMD) and Wire Arc Additive Manufacturing (WAAM). New technologies related to monitoring these processes are also emerging, such as Cyber-Physical Systems (CPSs) or Digital Twins (DTs), which can be used to enable Artificial Intelligence (AI)-powered analysis of generated big data. However, few works have dealt with a comprehensive data analysis, based on Digital Twin systems, to study quality levels of manufactured parts using 3D models. With this background in mind, this current project uses a Digital Twin-enabled dataflow to constitute a basis for a proposed data analysis pipeline. The pipeline consists of analyzing metal AM-manufactured parts’ surface roughness quality levels by the application of a Deep Neural Network (DNN) analytical model and enabling the assessment and tuning of deposition parameters by comparing AM-built models’ 3D representation, obtained by photogrammetry scanning, with the positional data acquired during the deposition process and stored in a cloud database. Stored and analyzed data may be further used to refine the manufacturing of parts, calibration of sensors and refining of the DT model. Also, this work presents a comprehensive study on experiments carried out using the CW-GTAW (Cold Wire Gas Tungsten Arc Welding) process as the means of depositing metal, resulting in hollow parts whose geometries were evaluated by means of both 3D scanned data, obtained via photogrammetry, and positional/deposition process parameters obtained from the Digital Twin architecture pipeline. Finally, an adapted PointNet DNN model was used to evaluate surface roughness quality levels of point clouds into 3 classes (good, fair, and poor), obtaining an overall accuracy of 75.64% on the evaluation of real deposited metal parts. Full article

In order to address the issue of inadequate negative pressure distribution in the gas extraction pipeline network and to enhance the efficiency of gas extraction, a study was conducted on the mechanisms of mutual influence between various branches within the gas extraction pipeline network. Using graph theory principles, a gas extraction network graph was built, and the gas extraction pipeline network was solved through the assignment adjustment iterative method. The study summarizes the sensitivity analysis of various control parameters, regulation patterns of branch valves and extraction pump control parameters at different network positions, and the impact of time parameters on the network’s operational conditions. Based on the solution results, an intelligent gas extraction control strategy was proposed. The results indicate that, under safety and efficiency constraints, gas concentration increased by approximately 11%, and gas purity increased by 0.3 m³/min, resulting in a 4.7% improvement after the implementation of intelligent control. The overall performance of the pipeline network was significantly enhanced. These research findings are of great significance for achieving efficient gas extraction. Full article

As critical national energy arteries, long-distance large-scale cross-regional gas transmission networks are characterized by high operating pressures, extensive spatial coverage, and complex topological structures. Thus, the multi-hazard profiles threatening its safety and reliability operation differ significantly from those of local urban gas distribution networks. This research develops a resilience assessment framework capable of quantifying resistance, adaptation, and recovery capacities of such energy systems. The framework establishes performance indicator systems based on design parameters, installation environments, and construction methods for long-distance trunk pipelines and key facilities such as storage facilities. Furthermore, based on complex network theory, the size of the largest connected component and global efficiency of the transmission network are selected as core topological metrics to characterize functional scale retention and transmission efficiency under disturbances, respectively, with corresponding quantification methods proposed. A cross-regional pipeline transmission network within a representative municipal-level administrative region in China is used as a case for empirical analysis. The quantitative assessment results of pipeline and network resilience are analyzed. The research indicates that trunk pipeline resilience is significantly affected by characteristic parameters, the laying environment, and installation methods. It is notably observed that installation methods like jacking and directional drilling, used for road or river crossings, offer greater resistance than direct burial but considerably lower restoration capacity due to the complexity of both the environment and the repair processes, which increases time and cost. Moreover, simulation-based comparison of recovery strategies demonstrates that, in this case, a repair-time-prioritized strategy more effectively enhances overall adaptive capacity and restoration efficiency than a node-degree-prioritized strategy. The findings provide quantitative analytical tools and decision-support references for resilience assessment and optimization of cross-regional energy transmission networks. Full article

CCUS-EOR combines emission reduction with economic benefits, making it one of the key technologies for addressing global climate change. Addressing the lack of systematic comparative studies on the differences in geological endowments and engineering conditions between China and North America in existing literature, this paper systematically reviews the progress of North American CO₂-EOR in areas such as gas source structure transformation, capture technologies, and pipeline network construction, based on a self-constructed database of typical projects. It then conducts a quantitative comparative analysis of typical projects in China and the United States from three dimensions: reservoir geological endowment, gas source composition, and infrastructure. The study reveals that the advancement of U.S. CO₂-EOR projects benefits from increasing industrial CO₂ supply and the construction of cross-regional pipeline networks. Comparative analysis indicates that North American projects primarily feature miscible displacement in medium-to-low temperature and light oil reservoirs. This contrasts fundamentally with the characteristics of China’s continental reservoirs, which exhibit “strong heterogeneity, high viscosity, and high minimum miscibility pressure (MMP)”. Currently, China’s CCUS-EOR is transitioning from engineering demonstration to commercial application. However, gaps persist compared to more mature international systems in areas such as low-concentration CO₂ capture, pipeline network construction for source-sink matching, and suitability for continental reservoir EOR. Moving forward, China can draw on U.S. CCUS-EOR development experience, accelerate research on relevant technologies tailored to its continental reservoir characteristics, and establish a differentiated whole-industry-chain CCUS-EOR technology system. Full article

This paper proposes a profit optimization method for the natural gas industry chain driven by demand forecasting. The method mainly consists of two core components: the construction of a natural gas demand forecasting model and the solution of an industry chain profit optimization model. In the forecasting stage, three models are trained using historical natural gas demand data, and the optimal model is selected based on performance evaluation indicators to predict natural gas demand for the coming month. In the optimization stage, the physical and operational characteristics of key components in the natural gas pipeline network are fully considered, and a nonlinear programming model is formulated with the objective of maximizing the overall profit of the industry chain. The model is validated using historical data. Finally, the demand forecast results are incorporated into the optimization model to calculate the expected industry chain profit for the next month. The findings of this study can provide theoretical foundations and quantitative decision-making support for natural gas suppliers to develop more economically efficient gas supply strategies. Full article

The growing demand for natural gas and the corresponding expansion of pipeline networks have intensified the need for precise leak detection, particularly due to the increased vulnerability of infrastructure to natural disasters such as earthquakes, floods, torrential rains, and landslides. This research leverages deep learning to develop two hybrid architectures, the Transformer–LSTM Parallel Network (TLPN) and the Transformer–LSTM Cascaded Network (TLCN), which are rigorously benchmarked against Transformer and Long Short-Term Memory (LSTM) baselines. Performance evaluations demonstrate TLPN achieves exceptional metrics, including 91.10% accuracy, an 86.35% F₁ score, and a 95.20% AUC value. Similarly, TLCN delivers robust results, achieving 90.95% accuracy, an 85.76% F₁ score, and 93.90% of the Area Under the ROC Curve (AUC). These outcomes confirm the superiority of attention mechanisms and highlight the enhanced capability realized by integrating LSTM with Transformer for time-series classification. The findings of this research significantly enhance the safety, reliability, sustainability, and risk mitigation capabilities of buried infrastructure. By enabling rapid leak detection and response, as well as preventing resource waste, these deep learning-based models offer substantial potential for building more sustainable and reliable urban energy systems. Full article