Search Results (324)

Onshore and offshore wind energy are two of the best options from an environmental point of view. Nevertheless, the volatile and intermittent nature of the wind resource hampers its integration into the power system. Accurate wind speed forecasting facilitates the operation of the electric grid, guaranteeing its stability and safety. However, most existing studies focus on very-short- and short-term time horizons, typically ranging from a few minutes to six hours, and rely exclusively on data measured at the prediction site. In contrast, only a few works address medium-term horizons or incorporate offshore data. Therefore, the main objective of this study is to predict medium-term (24 h ahead) onshore wind speed using the most influential offshore predictors, which are water surface temperature, atmospheric pressure, air temperature, wave direction, and spectral significant height. A new methodology based on twenty-seven machine learning regression models was developed and compared using the root mean squared error (RMSE) as the main evaluation metric. Unlike most existing studies that focus on very-short- or short-term horizons (typically below 6 h), this work addresses the medium-term (24 h ahead) forecast. After hyperparameter tuning, the CatBoost regressor achieved the best performance, with a root mean squared error of 2.06 m/s and a mean absolute error of 1.62 m/s—an improvement of around 40% compared to the simplest regression models. This approach opens new possibilities for wind speed estimation in regions where in situ measurements are not available. This will potentially reduce the cost, time, and environmental impacts derived from onshore wind resource characterisation campaigns. It also serves as a basis for future applications using combined offshore data from several locations. Full article

External insulation of coastal power grids transmitting offshore wind power faces significant threats from salt fog flashovers. Current arc monitoring and early warning technologies for flashover are severely inadequate. Research on salt fog discharge processes and determining the threshold at the flashover brink for transmission equipment external insulation is crucial for ensuring the safe operation of coastal grids delivering offshore wind power. Fiber Bragg Grating (FBG), with its advantages of compact size, excellent insulation, and fast response, enables effective discharge monitoring and identification of the critical flashover state on external insulation surfaces. In this study, FBGs were embedded at the interfaces of typical external insulation specimens, including silicone rubber plates and epoxy resin plates, to conduct contaminated AC salt fog discharge tests. Synchronized measurements of visible light images, infrared thermal images, and FBG interface temperature were conducted to investigate the discharge physical processes on silicone rubber insulating surfaces and the flashover threshold based on FBG temperature rise rate. The results indicate that discharge process can be divided into three phases: arc initiation, extension, and flashover based on the characteristics of arc visible light images. By comparing arc locations in infrared and visible light images with the corresponding FBG interface temperature rise, the arc phase criterion of FBG interface temperature rise rate and position were proposed. Furthermore, through multiple experiments, it has been found that flashover occurs when both interface temperatures reached above 4.6 × 10⁻² °C/s. This study provides a novel research methodology for physical process of external insulation discharge and flashover warning in coastal salt fog environments. Full article

The Modular Multilevel Matrix Converter (M3C) has the potential to contribute to onshore grid frequency response by utilizing the electrostatic energy stored in its submodules. However, in the current offshore wind power domain, control schemes for M3C-based Low-Frequency AC transmission systems (M3C-LFACs) fail to effectively exploit the capacitor energy of M3C to provide adequate inertia support. Existing M3C controls are typically grid-following and thus suffer from stability issues under weak-grid conditions. To address this challenge, a dual-port grid-forming control strategy for M3C-LFAC systems is proposed, based on an energy synchronization loop. This approach enables phase-locked-loop-free synchronization between the M3C and the grid while establishing low-frequency link voltage vectors. Building on this foundation, an optimized energy utilization method for M3C total energy is introduced, featuring a two-stage preset curve to maximize the system’s inherent energy for frequency response. Under varying levels of grid load disturbances, the proposed scheme ensures that M3C-LFAC systems can provide optimal inertia support. Finally, simulation studies in MATLAB 2024b/Simulink validate the effectiveness and advantages of the proposed method. Full article

To address the issue of frequent power frequency overvoltage disconnection accidents in offshore WF caused by the capacitive effect of submarine cables, this paper proposes a double-layer RP optimal configuration method for large-scale offshore WF based on an adaptively improved GSA. Firstly, this paper considers both the RP capabilities of offshore WT themselves and RP compensation equipment, designing a two-layer “configuration-control” optimization framework for RP. The upper layer establishes an optimization configuration model with the objective of minimizing the total investment cost and operational expenses of the equipment. The lower layer establishes a RP optimization operation model with the objective of minimizing a weighted index that comprehensively considers system network losses, voltage deviations, and RP capacity margins. Then, to address the issue of traditional GSA being prone to local optima, this paper introduces a random factor into the mass calculation, combines elite concepts to selectively synthesize gravitational forces based on fitness values, and assigns larger random numbers to forces corresponding to superior particles. By introducing control parameters to adaptively update particle positions, an adaptively improved GSA is proposed, which is employed to solve the established double-layer RP optimization configuration model for large-scale offshore WF. Finally, simulation analysis is conducted on a large-scale offshore WF constructed using MATLAB R2020a. Compared with the basic GSA algorithm, the proposed method reduces the system loss by 50.59% and the voltage deviation by 64.75%. The research demonstrates that the proposed method can effectively enhance the stability of grid voltage and proves the effectiveness of the improved GSA and the proposed two-layer “configuration-control” optimization model. Full article

Submarine power cables (SPCs), as critical infrastructure for offshore wind farms, are the primary conduits for transmitting electricity from turbines to the grid. Actions such as seabed friction can cause damage to the submarine power cable’s outer sheath, accelerating the penetration of seawater corrosion media. This subsequently leads to corrosion fatigue or excessive loading in the tensile armor layer, which seriously threatens the long-term operational reliability of SPCs and the security of energy transmission. Based on homogenization theory and periodic boundary conditions, a repetitive unit cell (RUC) ABAQUS finite element model for a single-core submarine power cable (SPC) was established in this paper. And the mechanical response of the single-core SPC with the corroded tensile armor layers under tensile loading condition were systematically investigated. By comparing with a full-scale model, the feasibility and accuracy of the cable RUC damaged model proposed in this paper were effectively verified. It was found that the RUC damaged model exhibits significant stress concentration phenomena due to localized corrosion damage in the tensile armor layers, with its maximum von Mises stress being considerably higher than that of the RUC intact model; the elastic tensile stiffness of the SPC continuously decreases with increasing corrosion damage depth, but the magnitude of this reduction is small. This is because the corroded region is relatively small compared to the entire cable model dimension. This research reveals the potential impact of localized corrosion on the mechanical performance of the tensile armor layer, which can hold significant engineering importance for assessing the remaining load-bearing capacity of in-service SPCs and ensuring the reliability of subsea energy transmission corridors. Full article

This paper presents a novel submersible seaweed cultivation infrastructure designed to enhance seaweed growth through deep cycling. The system consists of a square grid of ropes for growing seaweed, supported by buoys, mooring lines, and innovative SubTractors—movable buoys that enable controlled submersion. The grid ropes are stabilized by four SubTractors, an array of small buoys, intermediate sinker weights and mooring lines anchored to the seabed. The SubTractors facilitate dynamic positioning, allowing the seaweed rope grid to be submerged below the thermocline—at depths of 100 m or more—where nutrient-rich deep water accelerates seaweed growth in offshore sites with low surface nutrient levels. Small buoys attached to the grid provide buoyancy, keeping the seaweed rope grid planar and near the surface to optimize photosynthesis when not submerged. This paper first describes the seaweed cultivation infrastructure, then develops a hydroelastic model of the proposed cultivation system, followed by a hydroelastic analysis under varying wave and current conditions. The results provide insights into the system’s dynamic behaviour, informing engineering design and structural optimization. Full article

Pipelines and power cables are critical infrastructures in coastal areas for transporting energy resources from offshore renewable installations to onshore grids. It is important to investigate the hydrodynamic forces on pipelines and cables and their surrounding flow fields, which are highly related to their on-bottom stability. The time-varying hydrodynamic forces coefficients and unsteady surrounding flows of a near-seabed pipeline subjected to a wave-induced oscillatory boundary layer flow are studied through numerical simulations. The Keulegan–Carpenter numbers of the oscillatory flow are up to 400, which are defined based on the maximum undisturbed near-bed orbital velocity, the pipeline diameter and the period of the oscillatory flow. The investigated Reynolds number is set to $1\times {10}^4$, defined based on $U_w$ and $D$. The influences of different seabed roughness ratios $k_s/D$ (where $k_s$ is the Nikuradse equivalent sand roughness) up to 0.1 on the hydrodynamic forces and the flow fields are considered. Both a wall-mounted pipeline with no gap ratio to the bottom wall and a pipeline with different gap ratios to the wall are investigated. The correlations between the hydrodynamic forces and the surrounding flow patterns at different time steps during one wave cylinder are analyzed by using the force partitioning method and are discussed in detail. It is found that there are influences of the increasing $k_s/D$ on the force coefficients at large $\mathrm{K}\mathrm{C},$ while for the small $\mathrm{K}\mathrm{C}$, the inertial effect from the oscillatory flow dominates the force coefficients with small influences from different $k_s/D$. The FPM analysis shows that the elongated shear layers from the top of the cylinder contribute to the peak values of the drag force coefficients. Full article

An accurate estimation of wind energy productivity is crucial for the success of energy transition strategies in developing countries such as Pakistan, for which the deployment of renewables is essential. This study investigates the use of machine learning and deep learning techniques to improve wind farm producibility assessments, tailored to the Pakistani context. SCADA data from a wind turbine in Türkiye were used to train and validate five predictive models. Among these, Random Forest proved most reliable, attaining a coefficient of determination of 0.97 on the testing dataset. The trained model was then employed to simulate the annual production of a 5 × 5 wind farm at two representative sites in Pakistan—one onshore and one offshore—that had been selected using ERA5 reanalysis data. In comparison with conventional estimates based on the theoretical power curve, the machine learning-based approach resulted in net energy predictions up to 20% lower. This is attributable to real-world effects such as wake and grid losses. The onshore site yielded an LCOE of 0.059 USD/kWh, closely aligning with the IRENA’s 2024 national average of approximately 0.06 USD/kWh, thereby confirming the reliability of the estimates. In contrast, the offshore site exhibited an LCOE of 0.120 USD/kWh, thus underscoring the need for incentives to support offshore development in Pakistan’s renewable energy strategy. Full article

This paper aims to unveil the symmetry–asymmetry transition mechanisms in transient fault waveforms of offshore wind power AC/DC transmission systems, addressing the critical limitation of traditional simulation methods of the fact that they cannot characterize the dynamic evolution of system symmetry, such as static impedance adjustment failing to capture transient asymmetry caused by parameter imbalance or converter control. It proposes a fault waveform simulation approach integrating mechanism analysis, scenario extraction, and model optimization. Key contributions include clarifying the quantitative links between key system parameters like submarine cable capacitance and inductance and symmetry–asymmetry characteristics, defining the transient decay rate oscillation frequency and voltage peak as core indicators to quantify symmetry breaking intensity; classifying typical fault scenarios into a symmetry-breaking type with synchronous three-phase imbalance and a persistent asymmetry type with zero-sequence and negative-sequence distortion based on symmetry evolution dynamics and revising grid-connection test indices such as lowering the low-voltage ride-through threshold and specifying the voltage type for different test objectives; and constructing a simplified embedded RLC second-order model with symmetry–asymmetry constraints to reproduce the whole process of symmetric steady state–fault symmetry breaking–recovery symmetry reconstruction. Simulation results verify the method’s effectiveness, with symmetry indicator reproduction errors ≤ 5% and asymmetric feature fitting goodness R² ≥ 0.92, which confirms that the method can effectively reveal the symmetry–asymmetry mechanisms of offshore wind power fault transients and provides reliable technical support for improving offshore wind power fault simulation accuracy and grid-connection test reliability, laying a theoretical basis for the grid-connection testing of offshore wind turbines and promoting the stable operation of offshore wind power systems. Full article

The manuscript adopts the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) protocol to conduct a comprehensive analysis of the methodologies and critical factors influencing the site selection for offshore photovoltaic (PV) projects. The study innovatively proposes a three-stage site selection method model of “exclusion–optimization–correction” and a four-dimensional factor framework, which encompasses “technology, economy, environment, and policy.” The study underscores the pivotal roles of solar radiation, water depth, and grid connection conditions in site selection, while also highlighting the “veto” impact of military exclusion zones and ecological redlines. Additionally, it identifies the challenges associated with data acquisition and standardization in site selection, advocates for the establishment of standardized assessment procedures, and offers theoretical underpinnings and decision-making guidance for the rational site selection of offshore PV projects. Full article

Offshore renewable energy resources are abundant and widely available worldwide, offering significant contributions to the clean energy net-zero carbon emission targets. This paper reviews strong and emerging offshore renewable energy sources, including wind (fixed bottom and floating), hydrokinetic wave and tidal energy, floating solar photovoltaics (FPVs) and hybrid energy systems. A literature review of recent sources yields a timely comprehensive comparison of the levelized cost of electricity (LCOE), technology readiness levels (TRLs), capacity factors (CFs) and global generation installed and potential, where offshore wind is recognized as being the strongest contributor to the clean energy transition and thus receives the most attention. Offshore wind grid integration, converter technologies, criticality, resiliency and energy storage integration are presented, in addition to challenges and research directions. While wave, tidal and FPV will never dominate the global grid, they have vital roles to play in the global energy transition; thus, they are reviewed, including technologies, installations, potential, challenges and research directions. Offshore hybrid energy systems, combining different offshore renewable energy sources, are also discussed along with example installations. The paper concludes with a discussion of the potential environmental impacts of offshore renewable energy development, including recommendations. Full article

Hybrid offshore wind-wave systems play an important role in renewable energy transition. To maximize energy utilization efficiency, a comprehensive assessment to select optimal locations is urgently needed. The hydraulic power characteristics of these systems cause power fluctuations that reduce grid frequency stability. Thus, a site suitability assessment and a grid-forming battery energy storage system (BESS) configuration method are proposed. Considering energy efficiency, dynamic complementary characteristics, and output stability, a framework integrating three indices of Composite Energy Output Index (CEOI), Time-Shifted Cross-Covariance Index (TS-CCI), and Energy Penetration Balance Index (EPBI) is constructed to assess site suitability. To ensure secure and stable operation of microgrid, the frequency response characteristics of the hybrid system are analyzed, and the corresponding frequency constraint is given. A BESS configuration method considering frequency constraint is developed to minimize life cycle costs and maintain grid stability. Applied to a case study along China’s southeast coast, the assessment method successfully identified the optimal offshore station, confirming its practical applicability. The BESS configuration method is validated on a modified IEEE 30-bus system, with a 6.35% decrease in life cycle cost and complete renewable utilization. This research provides a technical and cost-effective solution for integrating hybrid wind-wave energy into island microgrids. Full article

Advanced Persistent Threats (APT) are stealthy multi-step attacks, often executed over an extensive time period and tailored for a specific attack target. APTs represent a “low and slow” type of cyberattack, meaning that they most often remain undetected until the consequence of the attack becomes evident. Energy infrastructure, including power grids, oil and gas infrastructure, offshore wind installations, etc., form the basis of a modern digital nation. In addition to loss of power, financial systems, banking systems, digital national services, etc., become non-operational without electricity. Loss of power from an APT cyberattack could result in loss of life and the possibility of creating digital chaos. Digital payments becomes unavailable, digital identification is affected, and even POS terminals need to run on emergency power, which is limited in time, resulting in challenges in paying for food and beverages. Examples of Advanced Persistent Threats (APTs) targeting energy infrastructures include Triton, which in 2017 aimed to manipulate the safety systems of a petrochemical plant in Saudi Arabia, potentially leading to catastrophic physical consequences. Another significant incident is the Industroyer2 malware attack in 2022, which targeted a Ukrainian energy provider in an attempt to disrupt operations. The paper combines APT knowledge with energy infrastructure domain expertise, focusing on technical aspects while at the same time providing perspectives on societal consequences that could result from APTs. Full article

Shallow foundations are widely used in both terrestrial and marine environments, supporting critical structures such as buildings, offshore wind turbines, subsea platforms, and infrastructure in coastal zones, including piers, seawalls, and coastal defense systems. Accurately determining the soil bearing capacity for shallow foundations presents a significant challenge, as it necessitates considerable resources in terms of materials and testing equipment, as well as a substantial amount of time to perform the necessary evaluations. Consequently, our research was designed to approximate the forecasting of soil bearing capacity for shallow foundations using machine learning algorithms. In our research, four ensemble machine learning algorithms were employed for the prediction process, benefiting from previous experimental tests. Those four models were AdaBoost, Extreme Gradient Boosting (XGBoost), Gradient Boosting Regression Trees (GBRTs), and Light Gradient Boosting Machine (LightGBM). To enhance the model’s efficacy and identify the optimal hyperparameters, grid search was conducted in conjunction with k-fold cross-validation for each model. The models were evaluated using the R² value, MAE, and RMSE. After evaluation, the R² values were between 0.817 and 0.849, where the GBRT model predicted more accurately than other models in training, testing, and combined datasets. Moreover, variable importance was analyzed to check which parameter is more important. Foundation width was the most important parameter affecting the shallow foundation bearing capacity. The findings obtained from the refined machine learning approach were compared with the well-known empirical and modern machine learning equations. In the end, the study designed a web application that helps geotechnical engineers from all over the world determine the ultimate bearing capacity of shallow foundations. Full article

The current standard for sea ice engineering in the Bohai Sea implements a 1/4° grid method, which cannot satisfy the safety of oil and gas activities in the southern Bohai Sea, and therefore more detailed information on ice conditions and a more refined ice zone division are necessary. In the present study, up to 1/12° resolution sea ice characteristic data (period, thickness, concentration, and strength) were obtained based on the NEMO-LIM2 ice–ocean coupling model. On this basis, the design sea ice strength parameters were derived with different return periods from 1 to 100 years. Among the total of 53 grids, the mean ice periods in the southern Bohai Sea from 1951 to 2022 were 2–35 days, the mean ice concentration values were 8.3–64.6%, and the mean ice thicknesses were 2–15 cm. The design uniaxial compressive strengths and shear strengths at almost all grids exceeded 2.00 MPa and 1.00 MPa for return periods over 20 years, respectively. The design flexural strengths for the 100-year return period ranged from 463 to 594 kPa. For the 100-year return period scenario, all grids exhibited design tensile strengths exceeding 200 kPa. Across the southern Bohai Sea, the most severe ice conditions occur in nearshore zones, and the ice conditions display a distinct spatial gradient with Bohai Bay > offshore deep-water areas > Laizhou Bay. The mean ice thickness, concentration, design flexural and tensile strengths derived in this study were lower compared to the ice parameters suggested in the current standard, and design uniaxial compressive and shear strengths derived here were comparable to those suggested in the current standard. The refined grid used here captures more detailed spatial variations in the design strength values of sea ice engineering parameters in the southern Bohai Sea, providing more accurate data support for the anti-ice design of marine structures. Full article