Wind Power Research Articles

A techno-economic study on the utilisation of airborne wind energy for reverse osmosis seawater desalination.

Airborne wind energy is an emerging technology that can harness stronger and more consistent winds in higher altitudes using less mechanical and civil infrastructures than conventional wind energy systems. This article outlines a techno-economic study on using this technology for reverse osmosis seawater desalination in which a semi-permeable membrane process is used to remove salts and contaminants from water. To understand the techno-economic feasibility of such a system, this research work studies a 2MW airborne wind-driven reverse osmosis plant. Different energy recovery devices are also studied to find their impact on improving the desalination plant's techno-economic performance. Results show the techno-economic practicality of an airborne wind-driven reverse osmosis plant with a competitive levelised-cost-of-water compared to similar-sized wind and solar energy-driven seawater desalination systems.

Research on power plant security issues monitoring and fault detection using attention based LSTM model

OverviewFor Photo Voltaic (PV) arrays and Wind systems to operate as efficiently and effectively as possible, fault detection is essential. It is possible to improve the safety of renewable energy systems and guarantee that the service will continue uninterrupted if problem detection and diagnostics are performed in a timely and accurate manner. In general, wind power is one of the three major renewable energy sources, along with solar power and hydropower. Wind power is well distributed around the world, making it suitable to be exploited in human activities for the general welfare of society.ObjectivesA prototype security situational awareness system applicable to the power data communication network service and traffic model should be developed. This will help to successfully enhance the security and service quality of the power data communication network, effectively cope with network security threats in the new environment, and ensure the security of the power plant network access. The traffic of the main network of the existing data communication network will be combined and analyzed, and NQA traffic management algorithms will be studied and proposed. These actions will improve the SLA hierarchical service capability, the service quality of the core services carried by the backbone network, and strengthen the security capability of the new energy power plant communication network access system.MethodologyFor the purpose of this investigation, an attention-based long short-term memory (Att-LSTM) model was used for the categorization of time series actual data. The approach that has been developed is able to identify defects in photovoltaic arrays and inverters, which offers a dependable option for improving the efficiency and dependability of solar energy systems. For the purpose of evaluating the proposed method, a real-world solar energy dataset is used.ResultsThe findings acquired from this evaluation are compared to the results received from existing detection approaches such as Cryptography, Intrusion Detection System (IDS) methods, and Network Defense Schemes. The results obtained demonstrate that the suggested method surpasses current fault detection techniques, providing greater accuracy and better performance.

Research on Physically Constrained VMD-CNN-BiLSTM Wind Power Prediction

Accurate forecasting of wind power is crucial for addressing energy demands, promoting sustainable energy practices, and mitigating environmental challenges. In order to improve the prediction accuracy of wind power, a VMD-CNN-BiLSTM hybrid model with physical constraints is proposed in this paper. Initially, the isolation forest algorithm identifies samples that deviate from actual power outputs, and the LightGBM algorithm is used to reconstruct the abnormal samples. Then, leveraging the variational mode decomposition (VMD) approach, the reconstructed data are decomposed into 13 sub-signals. Each sub-signal is trained using a CNN-BiLSTM model, yielding individual prediction results. Finally, the XGBoost algorithm is introduced to add the physical penalty term to the loss function. The predicted value of each sub-signal is taken as the input to get the predicted result of wind power. The hybrid model is applied to the 12 h forecast of a wind farm in Zhangjiakou City, Hebei province. Compared with other hybrid forecasting models, this model has the highest score on five performance indicators and can provide reference for wind farm generation planning, safe grid connection, real-time power dispatching, and practical application of sustainable energy.

Optimal allocation of 30 GW offshore wind power in the Norwegian economic zone

Abstract. The Norwegian Government aims to install offshore wind power with a total capacity of 30 GW by 2040, and the Norwegian Water Resources and Energy Directorate has suggested 20 candidate regions. We show that the potential for reducing overall power production variance across these regions is high using modern portfolio theory and the hourly and spatially rich reanalysis NORA3 Wind Power data set (NORA3-WP). The geographical diversification effect is demonstrated under various relevant scenarios, including a sequential build-out scenario with a fully connected Norwegian power grid assumption. By considering 20 alternative regions selected using a recently developed suitability score, we further illustrate that the diversification effect is robust to location changes.

A Review of Synergies Between Advanced Grid Integration Strategies and Carbon Market for Wind Energy Development

The integration of wind energy into power systems is essential for achieving global decarbonization goals but poses significant challenges, including transmission losses, grid instability, and risks of wind farm disconnection during contingencies. This review focuses on advanced grid stability technologies, optimization strategies, and carbon trading mechanisms, proposing a synergistic framework to address these issues. By enhancing transmission efficiency and maintaining grid stability, these solutions reduce energy losses, contribute to carbon reduction, and create economic incentives through carbon credits. Moreover, optimization models enable wind farms to remain operational during severe faults, ensuring their active participation in carbon markets. This review connects recent technical advancements with economic and policy frameworks, offering a comprehensive pathway to achieving sustainable and stable power systems while maximizing the economic potential of wind energy.

Exploring offshore wind’s potential to enhance energy security in nations with limited land and fuel resources

Abstract Offshore wind (OSW) power is critical to addressing energy security issues in nations with limited land and fuel resources. This study aims to assess the quality of OSW resources with high temporal and spatial resolution and to elucidate the economically feasible deployment of OSW using advanced power system models with Japan as a case study. First, comprehensive evaluations of OSW resources were performed by integrating a GIS-based resource assessment with simulated data for hourly resource availability and renewable power plant operation. Then, using the “SWITCH-Japan” model developed in our previous study, four key policy scenarios (“pathways”) were analyzed. Each scenario incorporated three technology cost sensitivities and was assessed on multiple criteria including affordability, energy security, and land-use change. Finally, the potential for hydrogen production in other sectors was explored. We found that the Least-Cost scenario, which accelerates renewable energy growth, reduces average system costs by 40% and increases energy self-sufficiency by 85% compared to the Business-As-Usual scenario. While it is a highly valuable resource, OSW nonetheless necessitates significant infrastructure development and potentially faces both stricter regulations and local opposition. In recognition of this, the Limited Onshore Resources scenario reduces direct land use by half but finds only a slight increase in overall costs. While the balance of OSW potential is utilized for power systems, the remainder can materially enhance energy security for entire economies. Ultimately, OSW energy presents a strategic opportunity for nations to achieve energy self-reliance and reduce import dependence, emphasizing the need for timely infrastructure development.

Review of Design Schemes and AI Optimization Algorithms for High-Efficiency Offshore Wind Farm Collection Systems

The offshore wind power sector has witnessed exponential growth over the past decade, with large-scale offshore wind farms grappling with the challenge of elevated construction and maintenance expenses. Given that the collector system constitutes a substantial part of the investment cost in wind farms, the design and optimization of this system are pivotal to enhancing the economic viability of offshore wind farms. A thorough examination of collector system design and optimization methodologies is essential to elucidate the critical aspects of collector system design and to assess the comparative merits and drawbacks of various optimization techniques, thereby facilitating the development of collector systems that offer superior economic performance and heightened reliability. This paper conducts a review of the evolving trends in collector system research, with a particular emphasis on topology optimization models and algorithms. It juxtaposes the economic and reliability aspects of collector systems with varying topologies and voltage levels. Building on this foundation, the paper delves into the optimization objectives and variables within optimization models. Furthermore, it provides a comprehensive overview and synthesis of AI-driven optimization algorithms employed to address the optimization challenges inherent in offshore wind farm collector systems. The paper concludes by summarizing the existing research limitations pertaining to offshore wind farm collector systems and proposes innovative directions for future investigative endeavors. The overarching goal of this paper is to enhance the comprehension of offshore wind farm collector system design and optimization through a systematic analysis, thereby fostering the continued advancement of offshore wind power technology.

Progress in Icephobic Coatings for Wind Turbine Protection: Merging Chemical Innovation with Practical Implementation

Ice accumulation on wind turbine blades poses a significant challenge to turbine performance and safety, and these issues have led to extensive research on developing effective anti-icing methods. Polymer-based icephobic coatings have emerged as promising solutions, given their passive nature and low energy requirements. However, developing effective icephobic coatings is a complex task. In addition to anti-icing properties, factors such as mechanical strength, durability, and resistance to UV, weathering, and rain erosion must be carefully considered to ensure these coatings withstand the harsh conditions faced by wind turbines. The main challenge in coating engineering is mastering the chemistry behind these coatings, as it determines their performance. This review provides a comprehensive analysis of the suitability of current icephobic coatings for wind turbine applications, emphasizing their alignment with present industrial standards and the underlying coating chemistry. Unlike previous works, which primarily focus on the mechanical aspects of icephobicity, this review highlights the critical yet underexplored role of chemical composition and explores recent advancements in polymer-based icephobic coatings. Additionally, earlier studies largely neglect the specific standards required for industrial applications on wind turbines. By demonstrating that no existing coating fully meets all necessary criteria, this work underscores both the urgency of developing icephobic coatings with improved durability and the pressing need to establish robust, application-specific standards for wind turbines. The review also combines insights from cutting-edge research on icephobic coatings that are coupled with active de-icing methods, known as the hybrid approach. By organizing and summarizing these innovations, the review aims to accelerate the development of reliable and efficient wind energy systems to pave the way for a cleaner and more sustainable future.

A Lightweight Framework for Rapid Response to Short-Term Forecasting of Wind Farms Using Dual Scale Modeling and Normalized Feature Learning

Accurate wind power forecasting is crucial for optimizing grid scheduling and improving wind power utilization. However, real-world wind power time series exhibit dynamic statistical properties, such as changing mean and variance over time, which make it difficult for models to apply observed patterns from the past to the future. Additionally, the execution speed and high computational resource demands of complex prediction models make them difficult to deploy on edge computing nodes such as wind farms. To address these issues, this paper explores the potential of linear models for wind power forecasting and constructs NFLM, a linear, lightweight, short-term wind power forecasting model that is more adapted to the characteristics of wind power data. The model captures both short-term and long-term sequence variations through continuous and interval sampling. To mitigate the interference of dynamic features, we propose a normalization feature learning block (NFLBlock) as the core component of NFLM for processing sequences. This module normalizes input data and uses a stacked multilayer perceptron to extract cross-temporal and cross-dimensional dependencies. Experiments with data from two real wind farms in Guangxi, China, showed that compared with other advanced wind power forecasting methods, the MSE of NFLM in the 24-step ahead forecasting of the two wind farms is respectively reduced by 23.88% and 21.03%, and the floating-point operations (FLOPs) and parameter count only require 36.366 M and 0.59 M, respectively. The results show that NFLM can achieve good prediction accuracy with fewer computing resources.

Hybrid Variable Renewable Power Plants: A Case Study of ROR Hydro Arbitrage

Wind and solar energy sources, while sustainable, are inherently variable in their power generation, posing challenges to grid stability due to their non-dispatchable nature. To address this issue, this study explores the synergistic optimization of wind and solar photovoltaic resources to mitigate power output variability, reducing the strain on local grids and lessening the reliance on balancing power in high-penetration renewable energy systems. This critical role of providing stability can be effectively fulfilled by run-of-river hydropower plants, which can complement fluctuations without compromising their standard operational capabilities. In this research, we employ a straightforward energy balance model to analyze the feasibility of a 100 MW virtual hybrid power plant, focusing on the northern region of Portugal as a case study. Leveraging actual consumption and conceptual production data, our investigation identifies a specific run-of-river plant that aligns with the proposed strategy, demonstrating the practical applicability of this approach.

Self‐Powered System by an Aerodynamic‐Complementary Triboelectric‐Electromagnetic Hybridized Generator with Triple‐Mode Switching Power Management Topology for Wide‐Range Wind Energy Collection and Climate Monitoring

AbstractRotary wind energy harvester has always been the focus of attention in the field of self‐power technology. However, a conflict between start‐up and saturation rotation speed of wind energy harvester hinders the adaptive energy collection from low to strong wind speeds in different wind speed ranges. Herein, a self‐powered system by an aerodynamic‐complementary triboelectric‐electromagnetic hybridized generator (AC‐TEHG) equipped with a triple‐mode switching power management topology (TmSPMT) is proposed to achieve self‐adaptive power supply mode switching in response to different wind speed ranges. Specifically, AC‐TEHG integrates Savonius and wind cup miniaturized turbine to achieve layered energy collection over wide‐range wind speed regions (1.4–16.3 m s−1), where the triboelectric nanogenerator (TENG) and electromagnetic generator (EMG) units have the excellent electrical output with Voc, Isc, and instantaneous peak power reaching 664 V/10.83 V, 35.96 µA/19.84 mA and 8.01 mW/62.45 mW, respectively. AC‐TEHG equipped with TmSPMT can effectively respond to different wind speed ranges of windlessness, low, medium, and high wind speeds for steadily powering commercial electronics. Finally, a wireless self‐powered climate monitoring system is developed to indicate that AC‐TEHG equipped with TmSPMT is a sustainable solution to efficiently power Internet of Things sensors in regions with variable wind speeds.

Influence of Extreme Climate events on wind and Solar Energy over the Gobi Desert Region of China in the future

Influence of Extreme Climate events on wind and Solar Energy over the Gobi Desert Region of China in the future

Controlling the energies of the single-rotor large wind turbine system using a new controller

In wind energy generation systems, ensuring high energy quality is critical but is often compromised due to the limited performance and durability of conventional regulators. To address this, this work presents a novel controller for managing the machine-side inverter of a single-rotor large wind turbine system using an induction machine-type generator. The proposed controller is designed using proportional, integral, and derivative error-based mechanisms, which fundamentally differ from traditional proportional-integral (PI) regulators. Key features of the proposed regulator include its simplicity, cost-effectiveness, ease of implementation, reduced number of gains, and rapid dynamic response.This regulator enhances the direct power control (DPC) approach, as it integrates two tailored controllers alongside a pulse width modulation strategy to manage the machine inverter. The DPC strategy incorporating the proposed controller was implemented and tested using MATLAB, with various simulations to evaluate its performance and effectiveness. The proposed regulator demonstrated a significant improvement over the PI regulator, with reductions in active power ripples of 69%, 61.70%, and 59.14% across different tests. Additionally, the steady-state error of reactive power was reduced by 54.84%, 85.23%, and 62.68%, and the total harmonic distortion of current decreased by 48.12%, 50.55%, and 56.05%. These results underscore the high efficiency, robustness, and effectiveness of the proposed controller in improving system performance compared to conventional PI regulators. The controller’s outstanding performance makes it a promising solution for broader industrial applications.

Assessment of Potential Complementarity of Pumped Hydropower Storage to Solar and Wind Energy

ABSTRACTWind and solar energy are among the most important clean energy sources globally but are significantly affected by climate variations, resulting in substantial intermittency and mismatches between supply and demand in both temporal and spatial dimensions. Pumped hydropower storage (PHS) is introduced to mitigate these discrepancies by storing excess energy during periods of low demand and releasing it during high‐demand periods. In this study, we comprehensively evaluate the potential complementarity of PHS to solar and wind energy in China. First, by calculating the ratio between energy demand and energy supply by various wind–solar power combinations, the energy reliability over China in multiple scenarios is assessed. We then identify potential sites for PHS to estimate the energy storage capacities and compare the energy reliability with and without PHS installation. Our results demonstrate that the reliability improved by an average of 20.38% over China, with the highest increase reaching 99.91% in Tibet, where exist abundant PHS resources. We also compare grid reliability before and after the 2025 plan in China, finding that PHS significantly enhances energy storage to meet energy demand. Overall, our findings highlight the potential of PHS in addressing the intermittency and volatility of wind and solar energy, providing crucial insights for future energy planning and policymaking.

Reusing and recycling of composite wind turbine blades. A review of current practices and prospects. Part 5**. New industrial projects. Wind energy in China

Reusing and recycling of composite wind turbine blades. A review of current practices and prospects. Part 5**. New industrial projects. Wind energy in China

A Coordinated Frequency Regulation Strategy Integrating Power Generation, Energy Storage, and DC Transmission for Offshore Wind Power MMC-HVDC Transmission Systems

With the increasing proportion of renewable energy in power grids, the inertia level and frequency regulation capability of modern power systems have declined. In response, this paper proposes a coordinated frequency regulation strategy integrating power generation, energy storage, and DC transmission for offshore wind power MMC-HVDC transmission systems, aimed at improving the frequency stability of onshore power grids. First, considering the inability of the receiving-end MMC-HVDC converter station under constant DC voltage control to directly respond to AC system frequency variations, a frequency regulation method is developed based on constant DC voltage control. The approach employs DC voltage as a transmission signal to coordinate the responses of wind turbines and energy storage systems. Subsequently, based on the energy storage configuration of the onshore renewable energy aggregation station, a secondary frequency regulation strategy is proposed. This strategy integrates offshore wind power, MMC-HVDC transmission system, and energy storage systems, balancing AC frequency regulation and the recovery of the state of charge (SOC) of the energy storage system. Finally, the proposed method is tested on a modified IEEE 39-bus system, the results demonstrate that the minimum frequency value can be in-creased by 37.5%, the system frequency can be restored to the initial state after secondary FM, and the results demonstrate its effectiveness.

Prediction for Coastal Wind Speed Based on Improved Variational Mode Decomposition and Recurrent Neural Network

Accurate and comprehensive wind speed forecasting is crucial for improving efficiency in offshore wind power operation systems in coastal regions. However, raw wind speed data often suffer from noise and missing values, which can undermine the prediction performance. This study proposes a systematic framework, termed VMD-RUN-Seq2Seq-Attention, for noise reduction, outlier detection, and wind speed prediction by integrating Variational Mode Decomposition (VMD), the Runge–Kutta optimization algorithm (RUN), and a Sequence-to-Sequence model with an Attention mechanism (Seq2Seq-Attention). Using wind speed data from the Shidao, Xiaomaidao, and Lianyungang stations as case studies, a fitness function based on the Pearson correlation coefficient was developed to optimize the VMD mode count and penalty factor. A comparative analysis of different Intrinsic Mode Function (IMF) selection ratios revealed that selecting a 50% IMF ratio effectively retains the intrinsic information of the raw data while minimizing noise. For outlier detection, statistical methods were employed, followed by a comparative evaluation of three models—LSTM, LSTM-KAN, and Seq2Seq-Attention—for multi-step wind speed forecasting over horizons ranging from 1 to 12 h. The results consistently showed that the Seq2Seq-Attention model achieved superior predictive accuracy across all forecast horizons, with the correlation coefficient of its prediction results greater than 0.9 in all cases. The proposed VMD-RUN-Seq2Seq-Attention framework outperformed other methods in the denoising, data cleansing, and reconstruction of the original wind speed dataset, with a maximum improvement of 21% in accuracy, producing highly accurate and reliable results. This approach offers a robust methodology for improving data quality and enhancing wind speed forecasting accuracy in coastal environments.

Future Energy Technology for Nonroad Mobile Machines

Greenhouse gases emissions reduction in the energy and transportation systems is extremely important. Nonroad mobile machines (NRMMs) are a key factor of production in many industrial and transportation systems with high‐energy intensity. NRMM cover a wide range of application sectors and operate often in harsh environments. This study presents a literature review for NRMM on agriculture and forestry, mining and earth‐moving, construction, and ports. It provides an overview of future energy technology and energy‐related business factors for NRMM, considering different geographical areas, various energy sources, energy delivery solutions, and different types of powertrains. The best solutions for the case combinations and projected market environments are derived for several case regions. This study also contains a detailed example of an off‐grid mining with renewable energy supply. The analysis of the off‐grid mining cases clearly reveals the differences between the Nordic conditions and southern conditions. The importance of the wind power as a source for the renewable energy is emphasized in Nordic conditions, but the solar power can augment it during the summer months. Also, the seasonal storage becomes important in the case of Nordic conditions.

A Simple Model of Turbine Control Under Stochastic Fluctuations of Internal Parameters

This article considers a model of a wind power generation system. It is assumed that the wind torque is transmitted to the generator via a gear. At the same time, the gear itself can have backlash with stochastic parameters. This kind of nonlinearity simulates an inevitable aging and wear of the mechanical parts of wind power generation systems over time. The purpose of the study was to identify a control system that would allow for establishing and maintaining the stability of the desired characteristics. The control system is formalized in the form of a second-order linear system. Numerical experiments demonstrated that the suggested control system is robust to stochastic perturbations resulting from both external and internal factors.

Concept Drift Early Fault Detection in Wind Turbine Based on Distance Metric: A Systematic Literature Review

The Supervisory Control and Data Acquisition (SCADA) system in wind turbines generates substantial data that remains underutilized in terms of wind farm operation and maintenance (O&M). Numerous fault detection methods leveraging SCADA data are being extensively researched to reduce O&M costs. The detection methods are revolutionizing wind farm O&M strategies, shifting from scheduled passive detection to predictive active detection, with the potential to significantly reduce spare parts and labor costs. This paper presents a systematic review of wind turbine fault detection methods based on concept drift and distance metrics, employing the PRISMA methodology. The selected literature is analyzed from three perspectives: fault components, modeling methods, and data sources. Additionally, this review addresses research questions related to current trends, concept drift applications, and distance metric utilization in wind turbine fault detection. Lastly, it provides valuable insights for researchers and industry practitioners in wind energy engineering to explore future research and development in fault detection techniques for enhancing the reliability and efficiency of wind turbine operations.