Search Results (394)

Wind power output sequences exhibit strong randomness and intermittency characteristics; traditional single forecasting models struggle to capture the internal features of sequences and are highly susceptible to interference from high-frequency noise and predictive accuracy is still notably poor at the peaks where the power curve undergoes abrupt changes. To address the poor fitting at peaks, a short-term wind power forecasting method based on the improved Informer model is proposed. First, the temporal convolutional network (TCN) is introduced to enhance the model’s ability to capture regional segment features along the temporal dimension, enhancing the model’s receptive field to address wind power fluctuation under varying environmental conditions. Next, a discrete cosine transform (DCT) is employed for adaptive modeling of frequency dependencies between channels, converting the time series data into frequency domain representations to extract its frequency features. These frequency domain features are then weighted using a channel attention mechanism to improve the model’s ability to capture peak features and resist noise interference. Finally, the Informer generative decoder is used to output the power prediction results, this enables the model to simultaneously leverage neighboring temporal segment features and long-range inter-temporal dependencies for future wind-power prediction, thereby substantially improving the fitting accuracy at power-curve peaks. Experimental results validate the effectiveness and practicality of the proposed model; compared with other models, the proposed approach reduces MAE by 9.14–42.31% and RMSE by 12.57–47.59%. Full article

As part of the carbon peak and neutrality drive, an influx of renewable energy into the grid is imminent. However, the unpredictability of renewables like wind and solar can lead to significant curtailment if the power system relies solely on traditional generators. This paper presents a demand response mechanism to enhance renewable energy uptake by defining an optimal load curve for each node, considering the generator’s dynamic impact, system operations, and renewable energy projections. Once the ideal load curve is published, consumers, influenced by incentives, voluntarily align their consumption, steering the actual load to resemble the proposed curve. This strategy not only guides flexible generation resources to better utilize renewables but also minimizes the communication and control expenses associated with large-scale customer demand response. Additionally, a new evaluation metric for user response is proposed to ensure equitable incentive distribution. The model has been shown to lower both consumer power costs and system generation expenses, achieving a 22% reduction in renewable energy wastage. Full article

The increasing deployment of turbines installed offshore is critical for sustainable energy development, yet accurate performance assessment remains challenging due to complex environmental influences, diverse turbine control strategies, and issues with data quality. Traditional performance metrics and power curve models often fail to provide reliable cross-turbine comparisons because they neglect multivariate environmental factors and turbine-specific biases. To address these limitations, this study develops a novel multivariate environmental factor-driven power assessment framework employing segmented long short-term memory (LSTM) models. A hybrid data cleaning method, combining bidirectional quartile analysis with the power curtailment detection, is proposed to effectively identify outliers, including subtle anomalies within typical data ranges. Samples are segmented based on rated wind speed to reflect differences in control strategies, and turbine-specific operational parameters are excluded to ensure unbiased comparisons among turbines. The proposed method achieves substantial improvements in predictive accuracy, with decreases of 9.39% in mean absolute error (MAE) and 11.75% in root mean square error (RMSE), compared to conventional binning approaches. When applied to three 5.5 MW offshore wind turbines, the proposed method reveals significant differences among the units. Turbine A demonstrates the highest performance, while turbines B and C exhibit reductions of 14.35% and 8.29%, respectively. Operational state analysis shows that turbine B experiences substantially longer maintenance durations, indicating severe faults that adversely affect its operational reliability and power output. These findings provide valuable insights for maintenance prioritization and performance benchmarking among wind turbines. Full article

Wind farm performance monitoring has traditionally relied on deterministic models, such as power curves or machine learning approaches, which often fail to account for farm-wide behavior and the uncertainty quantification necessary for the reliable detection of underperformance. To overcome these limitations, we propose a probabilistic methodology for turbine-level active power prediction and uncertainty estimation using high-frequency SCADA data and farm-wide autoregressive information. The method leverages a Stochastic Variational Gaussian Process with a Linear Model of Coregionalization, incorporating physical models like manufacturer power curves as mean functions and enabling flexible modeling of active power and its associated variance. The approach was validated on a wind farm in the Belgian North Sea comprising over 40 turbines, using only 15 days of data for training. The results demonstrate that the proposed method improves predictive accuracy over the manufacturer’s power curve, achieving a reduction in error measurements of around 1%. Improvements of around 5% were seen in dominant wind directions (200°–300°) using 2 and 3 Latent GPs, with similar improvements observed on the test set. The model also successfully reconstructs wake effects, with Energy Ratio estimates closely matching SCADA-derived values, and provides meaningful uncertainty estimates and posterior turbine correlations. These results demonstrate that the methodology enables interpretable, data-efficient, and uncertainty-aware turbine-level power predictions, suitable for advanced wind farm monitoring and control applications, enabling a more sensitive underperformance detection. Full article

This paper proposes an analysis of voltage stability under small disturbances following the integration of an offshore wind farm into a real power system, considering various load and generation scenarios under both normal and post-disturbance conditions. This study utilizes the southern region equivalent system, simulated with Anarede software version 11.7.2, an offshore wind farm with a maximum capacity of 2010 MW. This wind farm is modeled as a PQ bus, operating at partial (50%) and full (100%) generation levels. Three power factor scenarios are examined: resistive, capacitive, and inductive. Submodule 2.3 of the Brazilian National System Operator guidelines states that the base case operating conditions are considered voltage insecurity. Resistive and capacitive power factor operation improved voltage stability margins but resulted in overvoltage on several buses. Conversely, inductive power factor operation led to reduced stability margins and undervoltages at buses near the wind farm. Contingency analysis further revealed stability margins below security limits. Static Var Compensators were installed at critical buses to mitigate these effects, successfully eliminating the initial overvoltage and undervoltage. Modeling as PQ buses does not guarantee stability, making compensators essential for the safe integration of offshore wind generation in Brazilian test systems. Full article

The widespread deployment of electric vehicles (EVs) has introduced substantial challenges to electricity pricing, grid stability, and renewable energy integration. This paper presents the first real-time quantum-enhanced electricity pricing framework for large-scale EV charging networks, marking a significant departure from existing approaches based on mixed-integer programming (MILP) and deep reinforcement learning (DRL). The proposed framework incorporates renewable intermittency, demand elasticity, and infrastructure constraints within a high-dimensional optimization model. The objective is to dynamically determine spatiotemporal electricity prices that reduce system peak load, improve renewable utilization, and minimize user charging costs. A rigorous mathematical formulation is developed, integrating over 40 system-level constraints, including power balance, transmission limits, renewable curtailment, carbon targets, voltage regulation, demand-side flexibility, social participation, and cyber-resilience. Real-time electricity prices are treated as dynamic decision variables influenced by station utilization, elasticity response curves, and the marginal cost of renewable and grid electricity. The model is solved across 96 time intervals using a quantum-classical hybrid method, with benchmark comparisons against MILP and DRL baselines. A comprehensive case study is conducted on a 500-station EV network serving 10,000 vehicles, coupled with a modified IEEE 118-bus grid and 800 MW of variable renewable energy. Historical charging data with ±12% stochastic demand variation and real-world solar/wind profiles are used to simulate realistic conditions. Results show that the proposed framework achieves a 23.4% average peak load reduction per station, a 17.9% gain in renewable utilization, and up to 30% user cost savings compared to flat-rate pricing. Network congestion is mitigated at over 90% of high-traffic stations. Pricing trajectories align low-price windows with high-renewable periods and off-peak hours, enabling synchronized load shifting and enhanced flexibility. Visual analytics using 3D surface plots and disaggregated bar charts confirm structured demand-price interactions and smooth, stable price evolution. These findings validate the potential of quantum-enhanced optimization for scalable, clean, and adaptive EV charging coordination in renewable-rich grid environments. Full article

This paper presents a robust optimization-based approach for voyage and power generation scheduling to enhance the economic efficiency and reliability of electric propulsion ships powered by polymer electrolyte membrane fuel cells (PEMFCs) and battery energy storage systems (BESSs). The scheduling method is formulated considering generation cost curves of PEMFCs with mixed-integer linear programming (MILP) and is extended to a robust optimization framework that accounts for marine environmental uncertainties. The robust optimization approach, implemented via the column-and-constraint generation (C&CG) method, ensures stable operation under various uncertainty scenarios, such as wave speed and direction influenced by wind and tidal currents. To validate the proposed method, a simulation was conducted under realistic operational conditions, followed by a case study comparing the MILP and robust optimization approaches in terms of economic efficiency and reliability. Additionally, the optimization model incorporated degradation costs associated with PEMFCs and BESSs to account for long-term operational efficiency. The case study assessed the performance of both methods under load variation scenarios across different marine environmental uncertainties. Full article

With the increasing penetration of renewable energy sources, reduced system inertia and weakened frequency regulation capability have emerged as critical issues in power systems. As a result, wind turbines are now required to provide frequency support functions. To enable accurate analysis of the operational characteristics of wind turbines equipped with such control functions, this study proposes a virtual wind turbine model that estimates the operating point of a wind turbine in real-time under the assumption that frequency support functions are not performed. The proposed model is based on a turbine state observer that estimates wind speed and the power coefficient, and subsequently estimates generator power, generator speed, and blade pitch angle across various operating modes. Simulations were conducted under conditions with fluctuating wind speed and grid frequency, including MPPT, speed control, and pitch control operating regions. The accuracy of the proposed estimation model was evaluated, and the results demonstrated low estimation errors for key variables such as generator speed, power output, pitch angle, and wind speed across all conditions. These results quantitatively validate the robustness and applicability of the proposed model. Full article

Offshore wind power is one of the primary forms of utilizing marine green energy in China. Currently, near-shore wind power predominantly employs monopile foundations, with designs typically being overly conservative, resulting in high construction costs. Precise characterization of the interaction mechanisms between marine piles and surrounding soils is crucial for foundation design optimization. Traditional p-y curve methods, with simplified fitting functions, inadequately capture the complex pile–soil behaviors, limiting predictive accuracy and model uncertainty quantification. To address these challenges, this research collected 1852 empirical datasets of offshore wind monopile foundation pile–soil interactions, developing p-y curve and horizontal displacement prediction models using artificial neural network (ANN) expressions and comprehensive uncertainty statistical analysis. The constructed ANN model demonstrates a simple structure with satisfactory predictive performance, achieving average error margins below 6% and low to moderate prediction accuracy dispersion (26%~45%). In contrast, traditional p-y curve models show 30%~50% average biases with substantial accuracy dispersion near 80%, while conventional finite element methods exhibit approximately 40% error and dispersion. By strictly characterizing the probability cumulative function of the neural network model factors, a foundation is provided for reliability-based design. Through comprehensive case verification, it is demonstrated that the ANN-based model has significant advantages in terms of computational accuracy and efficiency in the design of offshore wind power foundations. Full article

Under the dual carbon objectives, wind power penetration has accelerated markedly. However, the inherent volatility and insufficient peak regulation capability in energy storage allocation hamper efficient grid integration. To address these challenges, this paper presents a hybrid storage capacity configuration method that combines Symplectic Geometry Mode Decomposition (SGMD) with Particle Swarm Optimization (PSO). SGMD provides fine-grained, multi-scale decomposition of load–power curves to reduce modal aliasing, while PSO determines globally optimal ESS capacities under peak-shaving constraints. Case-study simulations showed a 25.86% reduction in the storage investment cost compared to EMD-based baselines, maintenance of the state of charge (SOC) within 0.3–0.6, and significantly enhanced overall energy management efficiency. The proposed framework thus offers a cost-effective and robust solution for energy storage at renewable energy plants. Full article

The ongoing impacts of climate change, driven by both anthropogenic and global warming, significantly influence wind characteristics, resulting in increased wind speeds. Consequently, buildings that currently satisfy safety and serviceability standards may face challenges in the future. Despite extensive studies on wind-induced responses of tall buildings, there is a notable lack of comparative analyses assessing their performance under both historical and projected future wind conditions influenced by climate change. This study investigates the wind-induced performance of a 151 m tall building located in Suzhou, China, employing time history generation based on power spectral density functions. The analysis evaluates the acceleration responses of the building under both historical and projected future wind scenarios across different return periods and compares the responses to identify the potential changes in the building’s performance due to changing wind conditions. The structural acceleration responses are projected to rise significantly under future wind conditions. Furthermore, this study uses a time-domain Monte Carlo simulation framework to conduct a fragility analysis of the case study building, assessing the comfort of human occupants and the likelihood of exceeding performance thresholds under various wind scenarios. The fragility curve for the case study building is plotted for human occupant comfort as a function of mean wind speed. A substantial increase in the building’s fragility concerning occupant comfort is observed. The future wind climate will significantly impact the performance of tall buildings, necessitating proactive measures to address increased wind-induced effects and ensure long-term safety and habitability. Full article

Transformers, serving as critical components in power systems, are predominantly affected by winding faults that compromise their operational safety and reliability. Frequency Response Analysis (FRA) has emerged as the prevailing methodology for the status assessment of transformer windings in contemporary power engineering practice. To mitigate the accuracy limitations of single-classifier approaches in winding status assessment, this paper proposes a differentiated M-training classification algorithm based on White Shark Optimization (WSO). The principal contributions are threefold: First, building upon the fundamental principles of the M-training algorithm, we establish a classification model incorporating diversified classifiers. For each base classifier, a parameter optimization method leveraging WSO is developed to enhance diagnostic precision. Second, an experimental platform for transformer fault simulation is constructed, capable of replicating various fault types with programmable severity levels. Through controlled experiments, frequency response curves and associated characteristic parameters are systematically acquired under diverse winding statuses. Finally, the model undergoes comprehensive training and validation using experimental datasets, and the model is verified and analyzed by the actual transformer test results. The experimental findings demonstrate that implementing WSO for base classifier optimization enhances the M-training algorithm’s diagnostic precision by 8.92% in fault-type identification and 8.17% in severity-level recognition. The proposed differentiated M-training architecture achieves classification accuracies of 98.33% for fault-type discrimination and 97.17% for severity quantification, representing statistically significant improvements over standalone classifiers. Full article

Power system stability (PSS) refers to the capacity of an electrical system to maintain a consistent equilibrium between the generation and consumption of electric power. In this paper, the PSS is evaluated for a “hybrid power plant” (HPP) which combines thermal, wind, solar photovoltaic (PV), and hydropower generation in Niigata City. A new method for estimating its PV power generation is also introduced based on NHK (the Japan Broadcasting Corporation)’s cloud distribution forecasts (CDFs) and land ratio settings. Our objective is to achieve frequency stability (FS) while reducing CO₂ emissions in the power generation sector. So, the PSS is evaluated according to the results in terms of the FS variable. Six-minute autoregressive wind speed prediction (6ARW) support is used for wind power (WP). One-hour GPV wind farm (1HWF) power is computed from the Grid Point Value (GPV) wind speed prediction data. The PV power is predicted using autoregressive modelling and the CDFs. In accordance with the daily power curve and the prediction time, we can support thermal power generation planning. Actual data on wind and solar are measured every 10 min and 1 min, respectively, and the hydropower is controlled. The simulation results for the electricity frequency fluctuations are within ±0.2 Hz of the requirements of Tohoku Electric Power Network Co,. Inc. for testing and evaluation days. Therefore, the proposed system supplies electricity optimally and stably while contributing to reductions in CO₂ emissions. Full article

Dry-type air-core reactors (DAR) are critical components in power systems but are prone to inter-turn short circuit faults which interrupt the symmetry of the winding structure. Inspired by the online detection of transformer winding deformation, the V-I method has been adapted to diagnose short circuit faults in reactors. However, the diagnostic criteria and thresholds of V-I method remain unclear. This paper presents a novel method for determining the threshold for detecting inter-turn short circuit faults in DAR, integrating V-I analysis with machine learning techniques. Specifically, Gradient Boosting Regression (GBR) is used to compute a standard diagnostic criterion value, and curve fitting is also used to define the threshold for identifying inter-turn short circuit faults. The experimental results demonstrate that this method effectively identifies fault conditions in DAR. Full article

Efficient ice protection systems are essential to ensure the operability and reliability of aircraft. In recent years, electromechanical resonant ice protection systems have emerged as a promising low-power alternative to current solutions. These systems can operate in two primary resonant modes: flexural and extensional. While extensional modes enable effective de-icing over large surface areas, their performance can be compromised by interference from flexural modes, particularly in thin, ice-covered substrates where natural mode coupling occurs. This study presents a strategy based on material selection for making the Young’s modulus-to-density ratio uniform. The final objective of this paper is to establish the design rules for a composite leading edge de-icing system. For this purpose, an incremental approach will be used on profiles with different radii of curvature: plate or beam (infinite radius), circular profile (constant radius), NACA profile (variable radius). For beam and plate structures, the paper shows that this coupling can be mitigated by selecting materials with a Young’s modulus-to-density ratio comparable to that of ice. For curved structures, the curvature-induced effect is another source of parasitic flexion, which cannot be controlled solely by material selection and requires careful thickness optimization. This study presents analytical and numerical approaches to investigate the origin of this effect and a design methodology to minimize parasitic flexion in curved structures. The methodology is applied to the design optimization of a glass fiber NACA 0024 airfoil leading edge, the performance of which is subsequently evaluated through icing wind tunnel testing. Full article