Search Results (807)

Permanent magnetic couplings provide critical advantages for deep-sea systems through static-sealed, contactless power transmission. However, conventional metallic isolation sleeves incur significant eddy current losses, limiting efficiency and high-speed operation. Limited torque capacities fail to meet the operational demands of harsh marine environments. This study presents a novel permanent magnet coupling featuring a ceramic isolation sleeve engineered for deep-sea cryogenic ammonia submersible pumps. The ceramic sleeve eliminates eddy current losses and provides exceptional corrosion resistance in acidic/alkaline environments. To withstand 3.5 MPa hydrostatic pressure, a 6-mm-thick sleeve necessitates a 10 mm operational air gap, challenging magnetic circuit efficiency. To address this limitation, an improved 3D magnetic equivalent circuit (MEC) model was developed that explicitly accounts for flux leakage and axial end-effects, enabling the accurate characterization of large air gap fields. Leveraging this model, a Taguchi method-based optimization framework was implemented by balancing key parameters to maximize the torque density. This co-design strategy achieved a 21% increase in torque density, enabling higher torque transfer per unit volume. Experimental validation demonstrated a maximum torque of 920 Nm, with stable performance under simulated deep-sea conditions. This design establishes a new paradigm for high-power leak-free transmission in corrosive, high-pressure marine environments, advancing applications from deep-sea propulsion to offshore energy systems. Full article

Transmission towers are particularly vulnerable to extreme wind events, which can lead to structural damage or collapse, thereby compromising the stability of power transmission systems. Enhancing the wind-resistant capacity of these towers is therefore critical for improving the reliability and resilience of electrical infrastructure. This study utilizes finite element analysis (FEA) to evaluate the structural response of a 220 kV transmission tower subjected to fluctuating wind loads, effectively capturing the dynamic characteristics of wind-induced forces. A comprehensive dynamic analysis is conducted to account for uncertainties in wind loading and variations in wind direction. Through this approach, this study identifies the most critical wind angle and local structural weaknesses, as well as determines the threshold wind speed that precipitates structural collapse. To improve structural resilience, a concurrent multi-scale modeling strategy is adopted. This allows for localized analysis of vulnerable components while maintaining a holistic understanding of the tower’s global behavior. To mitigate failure risks, the traditional perforated plate reinforcement technique is implemented. The reinforcement’s effectiveness is evaluated based on its impact on load-bearing capacity, displacement control, and stress redistribution. Results reveal that the critical wind direction is 45°, with failure predominantly initiating from instability in the third section of the tower leg. Post-reinforcement analysis demonstrates a marked improvement in structural performance, evidenced by a significant reduction in top displacement and stress intensity in the critical leg section. Overall, these findings contribute to a deeper understanding of the wind-induced fragility of transmission towers and offer practical reinforcement strategies that can be applied to enhance their structural integrity under extreme wind conditions. Full article

Multiband transmission is, nowadays, being implemented worldwide to increase the optical transport network capacity, mainly because it uses the already-installed single-mode fiber (SMF). The G.654E SMF, due to its attributes (e.g., low-loss, and large-effective area in comparison with the standard G.652 SMF), can also increase network capacity and can also be used for multiband (MB) transmission. Nevertheless, in MB transmission, power mode coupling arises when bands with wavelengths below the cut-off wavelength are used, inducing multipath interference (MPI). This work investigates the impact of the MPI, due to mode coupling from G.654E SMF, in the transmission reach of a C+L+S band transmission system. Our results indicate that for the S-band scenario, the band below the wavelength cut-off, an approximately 25% reach decrease is observed when the MPI/span increases to −26 dB/span, considering quadrature phase-shift keying (QPSK) signals with a 64 GBaud symbol rate. We also concluded that if the L-band were not above the wavelength cut-off, it would be much more affected than the S-band, with an approximately 52% reach decrease due to MPI impact. Full article

Transformers are critical assets for power system reliability, as they connect different voltage levels across generation, transmission, and distribution. Their failure can lead to significant impacts on multiple aspects. Given the aging transformer fleet, supply chain challenges, and constrained investment capacity, the adoption of risk-based strategies is essential to support long-term maintenance planning and investment. This paper proposes a multicriteria framework to assess the probability and impact of transformer failure, enabling a more comprehensive and data-driven risk evaluation. The method was applied to a sample fleet, enabling the identification and prioritization of the most critical units through a risk plot. The framework enhances asset management by identifying critical units within a transformer fleet, promoting efficiency, reliability, and long-term planning based on objective risk indicators. Full article

A deterministic Mixed-Integer Nonlinear Programming (MINLP) model for the Integrated Generation and Transmission Expansion Planning (IGTEP) problem is presented. The proposed framework is distinguished by its foundation on the complete AC power flow formulation, which is solved to global optimality using BARON, a deterministic MINLP solver, which ensures the identification of truly optimal expansion strategies, overcoming the limitations of heuristic approaches that may converge to local optima. This approach is employed to establish a definitive, high-fidelity economic and technical benchmark, addressing the limitations of commonly used DC approximations and metaheuristic methods that often fail to capture the nonlinearities and interdependencies inherent in power system planning. The co-optimization model is formulated to simultaneously minimize the total annualized costs, which include investment in new generation and transmission assets, the operating costs of the entire generator fleet, and the cost of unsupplied energy. The model’s effectiveness is demonstrated on the IEEE 14-bus system under various dynamic load growth scenarios and planning horizons. A key finding is the model’s ability to identify the most economic expansion pathway; for shorter horizons, the optimal solution prioritizes strategic transmission reinforcements to unlock existing generation capacity, thereby deferring capital-intensive generation investments. However, over longer horizons with higher demand growth, the model correctly identifies the necessity for combined investments in both significant new generation capacity and further network expansion. These results underscore the value of an integrated, AC-based approach, demonstrating its capacity to reveal non-intuitive, economically superior expansion strategies that would be missed by decoupled or simplified models. The framework thus provides a crucial, high-fidelity benchmark for the validation of more scalable planning tools. Full article

Under the “dual carbon” goals and rapid clean energy development, power grids face challenges including rapid load growth, uneven power flow distribution, and limited transmission capacity. This paper proposes a novel FACTS device with fault tolerance and switchable topology that maintains power flow control over multiple lines during N-1 faults, enhancing grid safety and economy. The paper establishes a steady-state mathematical model based on additional virtual nodes and provides power flow calculation methods to accurately reflect the device’s control characteristics. An entropy-weighted TOPSIS method was employed to establish a quantitative evaluation system for assessing the grid performance improvement after FACTS device integration. To address interaction issues among multiple flexible devices, an optimization planning model considering th3e coordinated effects of UPFC and VSC-HVDC was constructed. Multi-objective particle swarm optimization obtained Pareto solution sets, combined with the evaluation system, to determine the optimal configuration schemes. Considering wind power uncertainty and fault risks, we propose a system-level coordinated operation strategy. This strategy constructs probabilistic risk indicators and introduces topology switching control constraints. Using particle swarm optimization, it achieves a balance between safety and economic objectives. Simulation results in the Jiangsu power grid scenarios demonstrated significant advantages in enhancing the transmission capacity, optimizing the power flow distribution, and ensuring system security. Full article

Given the increases in renewable energy penetration, appropriately allocating transmission costs is important in generation and transmission investment decisions. This study examines how a generator-side transmission charge affects investment decisions by power generation companies (PC) and the transmission system operator (TSO) under two frameworks differing in who decides investment timing. We compare two frameworks: (1) TSO determines investment timing and the PC determines capacity (TL framework); (2) PC determines investment timing and capacity (GL framework). We examine how variations in generator-side charges and demand uncertainty affect the optimal investment timing, capacity, and social surplus. Regarding investment timing, increases in the generator-side charge led to earlier investment in the TL framework but delayed investment in the GL framework. Concerning investment capacity, the TL framework yielded greater capacity with low uncertainty, while the GL framework supported greater capacity with high uncertainty. The magnitude of the relative social surplus of the two frameworks was reversed according to the generator-side charge and uncertainty. Specifically, the GL framework became increasingly superior to the TL framework as uncertainty increased, and this advantage was amplified by a higher generator-side charge. Policymakers should consider uncertainty and calibrate the level of generator-side charge and the allocation of decision-making authority. Full article

Hybridization of solar and wind energy sources is a promising solution to enhance the dispatch capability of renewables. The complementarity of wind and solar radiation, as well as the sharing of transmission lines and other infrastructures, can notably benefit the deployment of renewable power. Mapping of hybrid solar–wind potential can help identify new emplacements or existing power facilities where an extension with a hybrid system might work. This paper presents an analysis of a hybrid solar–wind potential by considering a reference power plant of 40 MW in the Iberian Peninsula and comparing the hybrid and non-hybrid energy generated. The generation of energy is estimated using SAM for a typical meteorological year, using PVGIS and ERA5 meteorological information as input. Modeling the hybrid plant in relation to individual PV and wind power plants minimizes the dependence on technical and economic input data, allowing for the expression of potential hybridization analysis in relative numbers through maps. Correlation coefficient and capacity factor maps are presented here at different time scales, showing the complementarity in most of the spatial domain. In addition, economic analysis in comparison with non-hybrid power plants shows a reduction of around 25–30% in the LCOE in many areas of interest. Finally, a sizing sensitivity analysis is also performed to select the most beneficial sharing between PV and wind. Full article

Terahertz (THz) communications and simultaneous wireless information and power transfer (SWIPT) hold the potential to energize battery-less Internet-of-Things (IoT) devices while enabling multi-gigabit data transmission. However, severe path loss, blockages, and rectifier nonlinearity significantly hinder both throughput and harvested energy. Additionally, high-power THz beams pose safety concerns by potentially exceeding specific absorption rate (SAR) limits. We propose a sensing-adaptive power-focusing (APF) framework in which a reconfigurable intelligent surface (RIS) embeds low-rate THz sensors. Real-time backscatter measurements construct a spatial map used for the joint optimisation of (i) RIS phase configurations, (ii) multi-tone SWIPT waveforms, and (iii) nonlinear power-splitting ratios. A weighted MMSE inner loop maximizes the data rate, while an outer alternating optimisation applies semidefinite relaxation to enforce passive-element constraints and SAR compliance. Full-stack simulations at 0.3 THz with 20 GHz bandwidth and up to 256 RIS elements show that APF (i) improves the rate–energy Pareto frontier by 30–75% over recent adaptive baselines; (ii) achieves a 150% gain in harvested energy and a 440 Mbps peak per-user rate; (iii) reduces energy-efficiency variance by half while maintaining a Jain fairness index of 0.999;; and (iv) caps SAR at 1.6 W/kg, which is 20% below the IEEE C95.1 safety threshold. The algorithm converges in seven iterations and executes within <3 ms on a Cortex-A78 processor, ensuring compliance with real-time 6G control budgets. The proposed architecture supports sustainable THz-powered networks for smart factories, digital-twin logistics, wire-free extended reality (XR), and low-maintenance structural health monitors, combining high-capacity communication, safe wireless power transfer, and carbon-aware operation for future 6G cyber–physical systems. Full article

Energy storage plays an essential role in stabilizing fluctuations in renewable energy sources such as wind and solar, enabling surplus electricity retention, and delivering dynamic frequency regulation. However, relying solely on a single form of storage often proves insufficient due to constraints in performance, capacity, and cost-effectiveness. To tackle frequency regulation challenges in remote desert-based renewable energy hubs—where traditional power infrastructure is unavailable—this study introduces a planning framework for an electro-hydrogen energy storage system with grid-forming capabilities, designed to supply both inertia and frequency response. At the system design stage, a direct current (DC) transmission network is modeled, integrating battery and hydrogen storage technologies. Using this configuration, the capacity settings for both grid-forming batteries and hydrogen units are optimized. This study then explores how hydrogen systems—comprising electrolyzers, storage tanks, and fuel cells—and grid-forming batteries contribute to inertial support. Virtual inertia models are established for each technology, enabling precise estimation of the total synthetic inertia provided. At the operational level, this study addresses stability concerns stemming from renewable generation variability by introducing three security indices. A joint optimization is performed for virtual inertia constants, which define the virtual inertia provided by energy storage systems to assist in frequency regulation, and primary frequency response parameters within the proposed storage scheme are optimized in this model. This enhances the frequency modulation potential of both systems and confirms the robustness of the proposed approach. Lastly, a real-world case study involving a 13 GW renewable energy base in Northwest China, connected via a ±10 GW HVDC export corridor, demonstrates the practical effectiveness of the optimization strategy and system configuration. Full article

The growing demand for direct current transmission emphasizes the need for advanced insulation suitable for high-capacity, long-distance applications. Thermoplastics, especially polypropylene, offer several advantages over conventional materials like XLPE (cross-linked polyethylene) and EPR (ethylene propylene rubber), including higher thermal stability, recyclability, and reduced space charge accumulation. However, due to the inherent rigidity and limited flexibility of PP, its mechanical aging becomes a critical factor in assessing its long-term reliability as a cable insulation. In this study, mechanical aging characteristics, specifically declines in tensile strength and elongation, were selected as key indicators of insulation aging. Accelerated aging tests were conducted at 90 °C, 110 °C, and 130 °C for up to 5000 h. The experimental data were fitted to exponential models to derive aging coefficients, which formed the basis for the proposed aging model and the ACI (aging condition index). The ACI enables quantitative assessment of the current insulation condition and estimation of the remaining lifetime until a predefined threshold (e.g., ACI = 0.5) is reached. These findings contribute to the development of condition-based maintenance strategies and long-term asset management for power cables, offering practical insights for improving the reliability of future power grid systems. Full article

Object detection technology enables the automatic identification of photovoltaic (PV) panel locations and conditions, significantly enhancing operational efficiency for maintenance teams while reducing the time and cost associated with manual inspections. Challenges arise due to the low resolution of remote sensing images combined with small-sized targets—PV panels intertwined with complex urban or natural backgrounds. To address this, a parallel architecture model based on YOLOv5 was designed, substituting traditional residual connections with parallel convolution structures to enhance feature extraction capabilities and information transmission efficiency. Drawing inspiration from the bottleneck design concept, a primary feature extraction module framework was constructed to optimize the model’s deep learning capacity. The improved model achieved a 4.3% increase in mAP, a 0.07 rise in F1 score, a 6.55% enhancement in recall rate, and a 6.2% improvement in precision. Additionally, the study validated the model’s performance and examined the impact of different loss functions on it, explored learning rate adjustment strategies under various scenarios, and analyzed how individual factors affect learning rate decay during its initial stages. This research notably optimizes detection accuracy and efficiency, holding promise for application in large-scale intelligent PV power station maintenance systems and providing reliable technical support for clean energy infrastructure management. Full article

Continuous multi-day extremely low or high new energy outputs have posed significant challenges in relation to power supply and new energy accommodations. Conventional reservoir hydropower, with the advantage of controllability and the storage ability of reservoirs, can represent a reliable and low-carbon flexibility resource to safeguard against continuous extreme new energy fluctuations. This paper proposes a mid-term scheduling method for reservoir hydropower to enhance our ability to regulate continuous extreme new energy fluctuations. First, a data-driven scenario generation method is proposed to characterize the continuous extreme new energy output by combining kernel density estimation, Monte Carlo sampling, and the synchronized backward reduction method. Second, a two-stage stochastic hydropower–new energy complementary optimization scheduling model is constructed with the reservoir water level as the decision variable, ensuring that reservoirs have a sufficient water buffering capacity to free up transmission channels for continuous extremely high new energy outputs and sufficient water energy storage to compensate for continuous extremely low new energy outputs. Third, the mathematical model is transformed into a tractable mixed-integer linear programming (MILP) problem by using piecewise linear and triangular interpolation techniques on the solution, reducing the solution complexity. Finally, a case study of a hydropower–PV station in a river basin is conducted to demonstrate that the proposed model can effectively enhance hydropower’s regulation ability, to mitigate continuous extreme PV outputs, thereby improving power supply reliability in this hybrid renewable energy system. Full article

Dynamic Line Rating (DLR) technology is presented as a key solution to optimize the transmission capacity of power lines without the need to make investments in new infrastructure. Unlike traditional methods based on static estimates, DLR allows the thermal capacity of conductors to be evaluated in real time, considering the environmental and operational conditions. This article presents a state-of-the-art analysis of this technology, including a review of the main solutions currently available on the market. Likewise, the influence of variables such as ambient temperature, wind speed and direction or solar radiation in the determination of dynamic load capacity is discussed. It also reviews various pilot and commercial projects implemented internationally, evaluating their results and lessons learned. Finally, the main technological, regulatory, and operational challenges faced by the mass adoption of DLR are identified, including aspects such as the prediction of the dynamic capacity value, combination with other flexibility options, or integration with network management systems. This review is intended to serve as a basis for future developments and research in the field. Full article

In response to the increasing demands for reliable, fast, and secure communications beyond 5G scenarios, the high-capacity networks have become a focal point. Quantum communication is at the forefront of this research, offering unmatched throughput and security. A free space optics (FSO) communication system integrated with fiber-end is designed and investigated using the Bennett–Brassard 1984 quantum key distribution (BB84-QKD) protocol. Simulation results show that reliable transmission can be achieved over a 10–15 km fiber length with a signal power of −19.54 dBm and high optical-to-signal noise of 72.28–95.30 dB over a 550 m FSO range under clear air, haze, fog, and rain conditions at a data rate of 1 Gbps. Also, the system using rectilinearly and circularly polarized signals exhibits a Stokes parameter intensity of −4.69 to −35.65 dBm and −7.7 to −35.66 dBm Stokes parameter intensity, respectively, over 100–700 m FSO range under diverse weather conditions. Likewise, for the same scenario, an FSO range of 100 m incorporating 2.5–4 mrad beam divergence provides the Stokes power intensity of −6.03 to −11.1 dBm and −9.04 to −14.12 dBm for rectilinearly and circularly polarized signals, respectively. Moreover, compared to existing works, this work allows faithful and secure signal transmission in free space, considering FSO–fiber link losses. Full article