Search Results (5,931)

Accurate load profile identification is crucial for effective and sustainable power system planning. This study proposes a characterization methodology based on clustering techniques applied to consumption data from medium- and low-voltage users, as well as distribution transformers from an electric utility. Three algorithms—K-means, DBSCAN (Density-Based Spatial Clustering of Applications with Noise), and Gaussian Mixture Models (GMM)—were implemented and compared in terms of their ability to form representative strata using variables such as observation count, projected energy, load factor ($LF$), and characteristic power levels. The methodology includes data cleaning, normalization, dimensionality reduction, and quality metric analysis to ensure cluster consistency. Results were benchmarked against a prior study conducted by Empresa Eléctrica Regional Centro Sur C.A. (EERCS). Among the evaluated algorithms, GMM demonstrated superior performance in modeling irregular consumption patterns and probabilistically assigning observations, resulting in more coherent and representative segmentations. The resulting clusters exhibited an average $LF$ of 58.82%, indicating balanced demand distribution and operational consistency across the groups. Compared to alternative clustering techniques, GMM demonstrated advantages in capturing heterogeneous consumption patterns, adapting to irregular load behaviors, and identifying emerging user segments such as induction-cooking households. These characteristics arise from its probabilistic nature, which provides greater flexibility in cluster formation and robustness in the presence of variability. Therefore, the findings highlight the suitability of GMM for real-world applications where representativeness, efficiency, and cluster stability are essential. The proposed methodology supports improved transformer sizing, more precise technical loss assessments, and better demand forecasting. Periodic application and integration with predictive models and smart grid technologies are recommended to enhance strategic and operational decision-making, ultimately supporting the transition toward smarter and more resilient power distribution systems. Full article

To address the persistent challenge of reliable real-time flowrate estimation in complex offshore oil production systems using Electric Submersible Pumps (ESPs), this study proposes a hybrid modeling approach that integrates a first-principles hydrodynamic model with Long Short-Term Memory (LSTM) neural networks. The aim is to enhance prediction accuracy across five offshore wells (A through E) in Brazil, particularly under conditions of limited or noisy sensor data. The methodology encompasses exploratory data analysis, preprocessing, model development, training, and validation using high-frequency operational data, including active power, frequency, and pressure, all collected at one-minute intervals. The LSTM architectures were tailored to the operational stability of each well, ranging from simpler configurations for stable wells to more complex structures for transient systems. Results indicate that prediction accuracy is strongly correlated with operational stability: LSTM models achieved near-perfect forecasts in stable wells such as Well E, with minimal residuals, and effectively captured cyclical patterns in unstable wells such as Well B, albeit with greater error dispersion during abrupt transients. The model also demonstrated adaptability to planned interruptions, as observed in Well A. Statistical validation using ANOVA, Levene’s test, and Tukey’s HSD confirmed significant performance differences (α < 0.01) among the wells, underscoring the importance of well-specific model tuning. This study confirms that the LSTM-based hybrid approach is a robust and scalable solution for real-time flowrate forecasting in digital oilfields, supporting production optimization and fault detection, while laying the groundwork for future advances in adaptive and interpretable modeling of complex petroleum systems. Full article

Methane pyrolysis produces hydrogen (H₂) with solid carbon black as a co-product, eliminating direct CO₂ emissions and enabling a low-carbon supply when combined with renewable or low-carbon heat sources. In this study, we propose a hybrid geothermal pyrolysis configuration in which an enhanced geothermal system (EGS) provides base-load preheating and isothermal holding, while either electrical or solar–thermal input supplies the final temperature rise to the catalytic set-point. The work addresses four main objectives: (i) integrating field-scale geothermal operating envelopes to define heat-integration targets and duty splits; (ii) assessing scalability through high-pressure reactor design, thermal management, and carbon separation strategies that preserve co-product value; (iii) developing a techno-economic analysis (TEA) framework that lists CAPEX and OPEX, incorporates carbon pricing and credits, and evaluates dual-product economics for hydrogen and carbon black; and (iv) reorganizing state-of-the-art advances chronologically, linking molten media demonstrations, catalyst development, and integration studies. The process synthesis shows that allocating geothermal heat to the largest heat-capacity streams (feed, recycle, and melt/salt hold) reduces electric top-up demand and stabilizes reactor operation, thereby mitigating coking, sintering, and broad particle size distributions. High-pressure operation improves the hydrogen yield and equipment compactness, but it also requires corrosion-resistant materials and careful thermal-stress management. The TEA indicates that the levelized cost of hydrogen is primarily influenced by two factors: (a) electric duty and the carbon intensity of power, and (b) the achievable price and specifications of the carbon co-product. Secondary drivers include the methane price, geothermal capacity factor, and overall conversion and selectivity. Overall, geothermal-assisted methane pyrolysis emerges as a practical pathway to turquoise hydrogen, if the carbon quality is maintained and heat integration is optimized. The study offers design principles and reporting guidelines intended to accelerate pilot-scale deployment. Full article

Organoid technology has emerged as a revolutionary tool in cancer research, offering physiologically accurate, three-dimensional models that preserve the histoarchitecture, genetic stability, and phenotypic complexity of primary tumors. These self-organizing structures, derived from adult stem cells, induced pluripotent stem cells, or patient tumor biopsies, recapitulate critical aspects of tumor heterogeneity, clonal evolution, and microenvironmental interactions. Organoids serve as powerful systems for modeling tumor progression, assessing drug sensitivity and resistance, and guiding precision oncology strategies. Recent innovations have extended organoid capabilities beyond static culture systems. Integration with microfluidic organoid-on-chip platforms, high-throughput CRISPR-based functional genomics, and AI-driven phenotypic analytics has enhanced mechanistic insight and translational relevance. Co-culture systems incorporating immune, stromal, and endothelial components now permit dynamic modeling of tumor–host interactions, immunotherapeutic responses, and metastatic behavior. Comparative analyses with conventional platforms, 2D monolayers, spheroids, and patient-derived xenografts emphasize the superior fidelity and clinical potential of organoids. Despite these advances, several challenges remain, such as protocol variability, incomplete recapitulation of systemic physiology, and limitations in scalability, standardization, and regulatory alignment. Addressing these gaps with unified workflows, synthetic matrices, vascularized and innervated co-cultures, and GMP-compliant manufacturing will be crucial for clinical integration. Proactive engagement with regulatory frameworks and ethical guidelines will be pivotal to ensuring safe, responsible, and equitable clinical translation. With the convergence of bioengineering, multi-omics, and computational modeling, organoids are poised to become indispensable tools in next-generation oncology, driving mechanistic discovery, predictive diagnostics, and personalized therapy optimization. Full article

This paper presents the third-generation design of Bimu, a compact wearable inertial measurement unit (IMU) tailored for advanced human motion tracking. Building on prior iterations, Bimu R2 focuses on enhancing thermal stability, data integrity, and energy efficiency by integrating onboard memory, redesigning the power management system, and optimizing the communication interfaces. A detailed performance evaluation—including noise, bias, scale factor, power consumption, and drift—demonstrates the device’s reliability and readiness for deployment in real-world applications ranging from clinical gait analysis to high-speed motion capture. The improvements introduced offer valuable insights for researchers and engineers developing robust wearable sensing solutions. Full article

We demonstrate a dual-wavelength broad-area diode laser system with narrow linewidth and wide spectral tunability using a composite external cavity comprising a volume Bragg grating and a Littrow-type transmission grating. One wavelength is stabilized at 780.25 nm with a linewidth of ~0.13 nm, while the other achieves a continuous tuning range of 772.24–786.43 nm with a linewidth of ~0.17 nm. The system exhibits a side-mode suppression ratio exceeding 20 dB across the entire tuning range. At a dual-wavelength separation of 4.29 nm, the total output power reaches 2.62 W. Additionally, we successfully validate the system’s potential for nonlinear optical applications. Full article

The growing penetration of distributed photovoltaic (PV) systems presents significant operational challenges for power grids, driven by the scarcity of historical data and the high spatiotemporal variability of PV generation. To address these challenges, we propose Generative Reconstruction and Adaptive Identification via Latents (GRAIL), a unified, end-to-end framework that integrates generative modeling with adaptive clustering to discover latent structures and representative scenarios in PV datasets. GRAIL operates through a closed-loop mechanism where clustering feedback guides a cluster-aware data generation process, and the resulting generative augmentation strengthens partitioning in the latent space. Evaluated on a real-world, multi-site PV dataset with a high missing data rate of $45.4\%$, GRAIL consistently outperforms both classical clustering algorithms and deep embedding-based methods. Specifically, GRAIL achieves a Silhouette Score of 0.969, a Calinski–Harabasz index exceeding $4.132\times {10}^6$, and a Davies–Bouldin index of $0.042$, demonstrating superior intra-cluster compactness and inter-cluster separation. The framework also yields a normalized entropy of $0.994$, which indicates highly balanced partitioning. These results underscore that coupling data generation with clustering is a powerful strategy for expressive and robust structure learning in data-sparse environments. Notably, GRAIL achieves significant performance gains over the strongest deep learning baseline that lacks a generative component, securing the highest composite score among all evaluated methods. The framework is also computationally efficient. Its alternating optimization converges rapidly, and clustering and reconstruction metrics stabilize within approximately six iterations. Beyond quantitative performance, GRAIL produces physically interpretable clusters that correspond to distinct weather-driven regimes and capture cross-site dependencies. These clusters serve as compact and robust state descriptors, valuable for downstream applications such as PV forecasting, dispatch optimization, and intelligent energy management in modern power systems. Full article

Floating photovoltaic (FPV) systems are promising for coastal aquaculture where reliable electricity is essential for pumping, oxygenation, sensing, and control. A sustainable FPV–storage hybrid tailored to monsoon-prone sites is developed, with emphasis on energy efficiency and structural resilience. The prototype combines dual-axis solar tracking with a spray-cooling and cleaning subsystem and an active wind-protection strategy that automatically flattens the array when wind speed exceeds 8.0 m/s. Temperature, wind speed, and irradiance sensors are coordinated by an Arduino-based supervisor to optimize tracking, thermal management, and tilt control. A 10 W floating module and a fixed-tilt reference were fabricated and tested outdoors in Penghu, Taiwan. The FPV achieved a 25.17% energy gain on a sunny day and a 40.29% gain under overcast and windy conditions, while module temperature remained below 45 °C through on-demand spraying, reducing thermal losses. In addition, a hybrid energy storage system (HESS), integrating a 12 V/10 Ah lithium-ion battery and a 12 V/24 Ah lead-acid battery, was validated using a priority charging strategy. During testing, the lithium-ion unit was first charged to stabilize the control circuits, after which excess solar energy was redirected to the lead-acid battery for long-term storage. This hierarchical design ensured both immediate power stability and extended endurance under cloudy or low-irradiance conditions. The results demonstrate a practical, low-cost, and modular pathway to couple FPV with hybrid storage for coastal energy resilience, improving yield and maintaining safe operation during adverse weather, and enabling scalable deployment across cage-aquaculture facilities. Full article

The power system’s dynamic frequency stability is affected by common-mode ultra-low-frequency oscillation and differential-mode low-frequency oscillation. Traditional frequency control based on generators is facing the problem of capacity reduction. It is urgent to explore new regulation resources such as photovoltaics. To address this issue, this paper proposes a distributed active support method based on photovoltaic systems via state–disturbance observation and dynamic surface consensus control. A three-layer distributed control framework is constructed to suppress low-frequency oscillations and ultra-low-frequency oscillations. To solve the high-order problem of the regional grid model and to obtain its unmeasurable variables, a regional observer estimating both system states and external disturbances is designed. Furthermore, a distributed dynamic frequency stability control method is proposed for wide-area photovoltaic clusters based on the dynamic surface control theory. In addition, the stability of the proposed distributed active support method has been proven. Moreover, a parameter tuning algorithm is proposed based on improved chaos game theory. Finally, simulation results demonstrate that, even under a 0–2.5 s time-varying communication delay, the proposed method can restrict the frequency deviation and the inter-area frequency difference index to 0.17 Hz and 0.014, respectively. Moreover, under weak communication conditions, the controller can also maintain dynamic frequency stability. Compared with centralized control and decentralized control, the proposed method reduces the frequency deviation by 26.1% and 17.1%, respectively, and shortens the settling time by 76.3% and 42.9%, respectively. The proposed method can effectively maintain dynamic frequency stability using photovoltaics, demonstrating excellent application potential in renewable-rich power systems. Full article

Hybrid cascaded multi-terminal HVDC systems represent a significant advancement in HVDC transmission technology. A notable real-world implementation of this concept is the bipolar hybrid cascaded multi-terminal high voltage direct current (MTDC) project in China, which successfully transmits hydropower from Baihetan to Jiangsu. This system combines MMCs for system support with LCCs for high-power transmission, offering both flexibility and efficiency in long-distance power delivery. This research explores the characteristics of main DC fault types in such systems, classifying faults based on sections and modes while analyzing their unique outcomes depending on DC fault locations. By focusing on the DC-side terminal behavior of the MMCs and LCCs, the main response processes to asymmetrical DC faults are investigated in detail. This study offers a detailed analysis of asymmetrical DC faults in bipolar HVDC systems, proposing a new classification based on fault characteristics such as current, voltage, active power, and reactive power. A supporting theoretical analysis is also presented. It identifies specific control demands needed for effective fault mitigation. PSCAD/EMTDC simulation results demonstrate that DC faults with similar characteristics can be consistently grouped into distinct categories by this new classification method. Each category is further linked to specific control demands, providing a strong basis for developing advanced protection strategies and practical solutions that enhance the stability and reliability of hybrid cascaded HVDC systems. Full article

In this study, we investigate a shielded capacitive power transfer (S-CPT) system that employs cast iron road covers as transmission electrodes for both dynamic and static charging of electric vehicles. Coupling capacitance was evaluated from S-parameters using copper, aluminum, ductile cast iron, structural steel, and carbon steel electrodes, with additional comparisons of ductile iron surface conditions (casting, machining, electrocoating). In a four-plate S-CPT system operating at 13.56 MHz, capacitance decreased with electrode spacing, yet ductile cast iron reached ~70 pF at 2 mm, demonstrating a performance comparable to that of copper and aluminum despite having higher resistivity and permeability. Power transmission experiments using a Ø330 mm cast iron cover meeting road load standards achieved 58% efficiency at 100 W, maintained around 40% efficiency at power levels above 200 W, and retained 45% efficiency under 200 mm lateral displacement, confirming robust dynamic performance. Simulations showed that shield electrodes enhance grounding, stabilize potential, and reduce return-path impedance. Finite element analysis confirmed that the ductile cast iron electrodes can withstand a 25-ton design load. The proposed S-CPT concept integrates an existing cast iron infrastructure with thin aluminum receiving plates, enabling high efficiency, mechanical durability, EMI mitigation, and reduced installation costs, offering a cost-effective approach to urban wireless charging. Full article

Hybrid AC/DC microgrids have emerged as a promising solution for integrating diverse renewable energy sources, enhancing efficiency, and strengthening resilience in modern power systems. However, existing control schemes exhibit critical shortcomings that limit their practical effectiveness. Traditional linear controllers, designed around nominal operating points, often fail to maintain stability under large load and generation fluctuations. Optimization-based methods are highly sensitive to model inaccuracies and parameter uncertainties, reducing their reliability in dynamic environments. Intelligent approaches, such as fuzzy logic and ML-based controllers, provide adaptability but suffer from high computational demands, limited interpretability, and challenges in real-time deployment. These limitations highlight the need for robust control strategies that can guarantee reliable operation despite disturbances, uncertainties, and varying operating conditions. Numerical performance indices demonstrate that the reviewed robust control strategies outperform conventional linear, optimization-based, and intelligent controllers in terms of system stability, voltage and current regulation, and dynamic response. This paper provides a comprehensive review of recent robust control strategies for hybrid AC/DC microgrids, systematically categorizing classical model-based, intelligent, and adaptive approaches. Key research gaps are identified, including the lack of unified benchmarking, limited experimental validation, and challenges in integrating decentralized frameworks. Unlike prior surveys that broadly cover microgrid types, this work focuses exclusively on hybrid AC/DC systems, emphasizing hierarchical control architectures and outlining future directions for scalable and certifiable robust controllers. Also, comparative results demonstrate that state of the art robust controllers—including H∞-based, sliding mode, and hybrid intelligent controllers—can achieve performance improvements for metrics such as voltage overshoot, frequency settling time, and THD compared to conventional PID and droop controllers. By synthesizing recent advancements and identifying critical research gaps, this work lays the groundwork for developing robust control strategies capable of ensuring stability and adaptability in future hybrid AC/DC microgrids. Full article

The increasing integration of photovoltaic (PV) power systems poses challenges for nighttime voltage regulation because long high-voltage (HV) and ultra-high-voltage (UHV) underground cables generate capacitive reactive power that elevates the grid voltage. Conventional compensators based on passive inductors and capacitors are bulky, costly, and inflexible, rendering them unsuitable for substation use. This study proposes an optimization-based strategy that leverages the existing inverter infrastructure of PV plants to provide nighttime reactive power compensation without additional hardware. A genetic algorithm (GA) determines the optimal number and spatial deployment of inverters to minimize line losses. Field validation at a 120 MW PV plant with 1292 inverters shows that the strategy reduces reverse reactive power from 0.84 MVAr to 0.00214 MVAr and line losses from 1.8235 kW to 0.386 kW using only 55 inverters, achieving near-zero additional capital expenditure (CAPEX). This method enhances the voltage stability and system efficiency while reducing the investment and maintenance costs. Full article

Modern energy systems are undergoing a profound transformation characterized by the active replacement of conventional fossil-fuel-based power plants with renewable energy sources. This transition aims to reduce the carbon emissions associated with electricity generation while enhancing the economic performance of electric power market players. However, alongside these benefits come several challenges, including reduced overall inertia within energy systems, heightened stochastic variability in grid operation regimes, and stricter demands on the rapid response capabilities and adaptability of emergency controls. This paper presents a novel methodology for selecting effective control laws for low-inertia energy systems, ensuring their dynamic stability during post-emergency operational conditions. The proposed approach integrates advanced techniques, including feature selection via decision tree algorithms, classification using Random Forest models, and result visualization through the Mean Shift clustering method applied to a two-dimensional representation derived from the t-distributed Stochastic Neighbor Embedding technique. A modified version of the IEEE39 benchmark model served as the testbed for numerical experiments, achieving a classification accuracy of 98.3%, accompanied by a control law synthesis delay of just 0.047 milliseconds. In conclusion, this work summarizes the key findings and outlines potential enhancements to refine the presented methodology further. Full article

High-performance electrical contact materials are crucial for electric power systems, new energy vehicles, and rail transportation, as their properties directly impact the reliability and safety of electronic devices. Enhancing these materials not only improves energy efficiency but also offers notable environmental and economic advantages. However, traditional composite contact materials often suffer from poor dispersion of the reinforcing phase, which restricts further performance improvement. Graphene (G), with its unique two-dimensional structure and exceptional electrical, mechanical, and tribological properties, is considered an ideal reinforcement for metal matrix composites. Yet, its tendency to agglomerate poses a significant challenge to achieving uniform dispersion. To overcome this, the study introduces a dual approach: modulation of the zeta potential (ζ) in the silver-plated liquid to enhance G’s dispersion stability, and concurrent optimization of the composite electrodeposition process. Experimental results demonstrate that this synergistic strategy enables the uniform distribution of G within the silver matrix. The resulting silver–graphene (Ag-G) composite coatings exhibit outstanding overall performance at both micro and macro levels. This work offers a novel and effective pathway for the design of advanced electrical contact materials with promising application potential. Full article