Wind Power Equipment Research Articles

High-performance insulating materials with high breakdown strength and low permittivity for eco-friendly electrical equipment

High-performance insulating materials are essential for developing lightweight, compact, and green offshore wind power equipment. It has been shown that nanoporous structures can limit the development of electron avalanche, leading to a significant increase in the breakdown electric strength of dielectrics. Hence, we fabricated a polysiloxane nanoporous biopolymer insulating material (PNBIM) with the nanoporous structure that presents exceptionally high electrically insulating properties. Under a CO2 atmosphere, the breakdown electric strength of the PNBIM was remarkably improved, exhibiting 761.01 % enhancement relative to that of the pure gas gap. Moreover, the PNBIM demonstrates excellent dielectric performance, with a low dielectric constant (1.63) and dielectric loss (0.014), as well as high hydrophobicity and excellent thermal stability. The investigations revealed that the nanopores inside the material can effectively suppress gas discharge and improve the breakdown electric strength. This study widens our understanding of conventional insulating materials and provides a promising approach for exploring high-performance insulating materials provide valuable insights.

Research on Wind Turbine Fault Detection Based on CNN-LSTM

With the wide application of wind energy as a clean energy source, to cope with the challenge of increasing maintenance difficulty brought about by the development of large-scale wind power equipment, it is crucial to monitor the operating status of wind turbines in real time and accurately identify the specific location of faults. In this study, a CNN-LSTM-based wind motor fault detection model is constructed for four types of typical faults, namely gearbox faults, electrical faults, yaw faults, and pitch faults of wind motors, combining CNN’s advantages of excelling in feature extraction and LSTM’s advantages of dealing with long-time sequence data, to achieve the simultaneous detection of multiple fault types. The accuracy of the CNN-LSTM-based wind turbine fault detection model reaches 90.06%, and optimal results are achieved for the effective discovery of yaw system faults, pitch system faults, and gearbox faults, obtaining 94.09%, 96.46%, and 97.39%, respectively. The CNN-LSTM wind turbine fault detection model proposed in this study improves the fault detection effect, avoids the further deterioration of faults, provides direction for preventive maintenance, reduces downtime loss due to restorative maintenance, and is essential for the sustainable use of wind turbines and maintenance of wind turbine service life, which helps to improve the operation and maintenance level of wind farms.

Strain modal response and vibration damping optimization of tower for wind power equipment

The safety of wind power equipment under dynamic load is one of the key factors to ensure sustainable energy recovery. In order to effectively improve the reliability of tower structure, the damage identification of flange bolt fracture based on strain mode was studied, and the vibration control and optimization scheme was proposed and verified. The dynamic response of the tower to wind load was calculated using the theory of Davenport spectrum. Combined with computational fluid dynamics, the dynamic load change law of the tower was obtained. Based on ANSYS Workbench, the modal simulation and analysis of the tower were carried out. Under different bolt damage conditions, the distribution characteristics of the strain modal shape of the tower in the axial and radial directions were obtained. The vibration damper was applied to the inside of the tower, and the vibration and stress at different positions under wind load were compared and analyzed to verify the specific vibration reduction and optimization effect. The results show that the strain modal shape of the tower cylinder has a significant peak at the damage site, and the peak height is positively correlated with the damage degree, indicating that the strain modal shape is highly sensitive to the damage. In addition, the vibration and maximum stress of the flange and top position of the tower have been effectively reduced by the shock absorber. The average amplitude of tower top can be reduced by 22.5 %, and the peak stress at the bottom flange position can be reduced by about 38 %.

Machine-Learning Methods for Estimating Performance of Structural Concrete Members Reinforced with Fiber-Reinforced Polymers

AbstractIn recent years, fiber-reinforced polymers (FRP) in reinforced concrete (RC) members have gained significant attention due to their exceptional properties, including lightweight construction, high specific strength, and stiffness. These attributes have found application in structures, infrastructures, wind power equipment, and various advanced civil products. However, the production process and the extensive testing required for assessing their suitability incur significant time and cost. The emergence of Industry 4.0 has presented opportunities to address these drawbacks by leveraging machine learning (ML) methods. ML techniques have recently been used to forecast the properties and assess the importance of process parameters for efficient structural design and their broad applications. Given their wide range of applications, this work aims to perform a comprehensive analysis of ML algorithms used for predicting the mechanical properties of FRPs. The performance evaluation of various models was discussed, and a detailed analysis of their pros and cons was provided. Finally, the limitations that currently exist in these techniques were pinpointed, and suggestions were given to improve their prediction precision suitable for evaluating the mechanical properties of FRP components.

Artificial intelligence based abnormal detection system and method for wind power equipment

The existing traditional monitoring methods are not ideal for identifying abnormal states in remote real-time monitoring of power system equipment due to adverse weather conditions such as haze and snow. The traditional monitoring methods for wind power equipment mainly rely on manual inspections and regular maintenance, which have problems such as high labor costs, low monitoring efficiency, and low monitoring accuracy. In order to improve the effectiveness of anomaly recognition for power system equipment, this paper proposes a remote real-time monitoring anomaly recognition method for power system equipment based on artificial intelligence technology. Firstly, this article uses pan tilt cameras to capture real-time images of power system equipment and transmit them to a remote monitoring center. The remote monitoring center captures real-time images of the equipment in the video stream and performs real-time image preprocessing. Then, the variational mode decomposition method is used to extract the abnormal state features of power system equipment in the image, and this feature is input into the support vector machine as a training sample to achieve real-time recognition of abnormal state of power system equipment. This article was also conducting fault diagnosis for four sets of wind turbines in the experimental section. According to the experimental results, it can be concluded that the rotational speed of wind turbine 1 increased from around 12 rpm to around 30 rpm during the day. During the day, the temperature of wind turbine 2 increased from around 25 °C to around 40 °C, while wind turbine 3 remained in a stopped state and the current speed decreased to 0. Wind turbine 4 also decreased from around 10 rpm to 0 during the day. From this, it can be concluded that the wind power equipment anomaly detection system based on artificial intelligence can clearly determine the abnormal situation of wind turbines. The wind power equipment anomaly detection system based on artificial intelligence can timely and accurately identify the abnormal situation of WPE, and can provide a new wind power equipment anomaly detection method based on artificial intelligence for the wind power industry. This can provide more accurate and reliable support for the operation and maintenance of wind power equipment, and also provide practice and exploration for the application of artificial intelligence in the industrial field.

Short-term wind speed prediction based on improved Hilbert–Huang transform method coupled with NAR dynamic neural network model

Wind energy, as a renewable energy source, offers the advantage of clean and pollution-free power generation. Its abundant resources have positioned wind power as the fastest-growing and most widely adopted method of electricity generation. Wind speed stands as a key characteristic when studying wind energy resources. This study primarily focuses on predictive models for wind speed in wind energy generation. The intense intermittency, randomness, and uncontrollability of wind speeds in wind power generation present challenges, leading to high development costs and posing stability challenges to power systems. Consequently, scientifically forecasting wind speed variations becomes imperative to ensure the safety of wind power equipment, maintain grid integration of wind power, and ensure the secure and stable operation of power systems. This holds significant guiding value and significance for power production scheduling institutions. Due to the complexity of wind speed, scientifically predicting its fluctuations is crucial for ensuring the safety of wind power equipment, maintaining wind power integration systems, and ensuring the secure and stable operation of power systems. This research aims to enhance the accuracy and stability of wind speed prediction, thereby reducing the costs associated with wind power generation and promoting the sustainable development of renewable energy. This paper utilizes an improved Hilbert–Huang transform (HHT) using complementary ensemble empirical mode decomposition (CEEMD) to overcome issues in the traditional empirical mode decomposition (EMD) method, such as component mode mixing and white noise interference. Such an approach not only enhances the efficiency of wind speed data processing but also better accommodates strong stochastic and nonlinear characteristics. Furthermore, by employing mathematical analytical methods to compute weights for each component, a dynamic neural network model is constructed to optimize wind speed time series modeling, aiming for a more accurate prediction of wind speed fluctuations. Finally, the optimized HHT-NAR model is applied in wind speed forecasting within the Xinjiang region, demonstrating significant improvements in reducing root mean square errors and enhancing coefficient of determination. This model not only showcases theoretical innovation but also exhibits superior performance in practical applications, providing an effective predictive tool within the field of wind energy generation.

Cerium-doped SnS micron flowers with long life and high capacity for hybrid supercapacitors

Supercapacitors with long life and high power density can be used in power tools and wind power equipment. However, increasing energy density without sacrificing advantages remains a major challenge, which limits their practical application. Therefore, efforts have been made to manufacture supercapacitors with high capacitance and long life. Ce-doped SnS micron flowers grown on nickel foam (Ce@SnS/NF) have been synthesized using a facile solvothermal method. The effect of Ce doping on the SnS, where Ce doping confined the growth of Ce@SnS to form smaller nanosheets and provided more active sites, resulting in the enhancement of the capacity and cycle stability, has been explored. In a three-electrode system, the specific capacitance increased from 1237 F/g (171.8 mAh/g) to 2549 F/g (354.0 mAh/g) at 2 A/g, and the stability was 85.5 % after 10,000 cycles at 10 A/g. A hybrid supercapacitor (Ce@SnS/NF (+)//AC (–)) assembled from Ce@SnS/NF and activated carbon as cathode and anode, respectively. The device has an energy density of 48.83 Wh/kg at 824.91 W/kg and long cycle stability (91.6 % retention after 20,000 cycles at 5 A/g). Moreover, the supercapacitor can power a white light-emitting diode (LED) for 15 min, so the Ce-doped SnS cathode has vast application prospects in supercapacitors.

Modeling method for optimal scheduling of Park comprehensive energy source load storage and consumption considering customer portrait

Aiming at the problems of energy consumption optimization of park integrated energy source load storage system in power grid dispatching, a modeling method of park integrated energy consumption optimization dispatching considering user portrait is proposed. Firstly, an improved k-means portrait method is proposed for park users; Secondly, on the premise of considering the constraints of equipment operation in the park, cluster users in the park, and establish an energy consumption optimization scheduling model with the objective function of user satisfaction, net income of wind power consumption and equipment operation cost; Finally, SAPSO algorithm and fuzzy theory are used for decision-making. The example simulation shows that the proposed model is correct and effective, which is more precise and objective than the traditional model.

Retracted: Detection Algorithm of Wind Power Equipment Video Image Sequence Based on Artificial Intelligence

Retracted: Detection Algorithm of Wind Power Equipment Video Image Sequence Based on Artificial Intelligence

Evaluating rare-earth constraints on wind power development under China's carbon-neutral target

China proposed a target to achieve carbon neutrality before 2060. Wind power is crucial for mitigating climate change and achieving carbon neutrality. However, its development depends on the potential constraints of rare-earth elements. Therefore, first projecting the rare-earth demand for wind power equipment in the context of achieving carbon neutrality and identifying potential obstacles are necessary. However, the carbon-neutral pathway for China's power sector is unclear, let alone the corresponding rare-earth demand. Consequently, this study explores a potential cost-effective carbon-neutral pathway for China's power sector and quantifies the demand for rare-earth elements used for producing wind power equipment under different pathways, by integrating dynamic material flow analysis and a national energy technology model. The results showed that the rare-earth supply may be inadequate for wind power development in terms of achieving carbon neutrality in China, especially for dysprosium and terbium. To neutralise the carbon emissions of China's power sector, the cumulative rare-earth demand during 2021–2060 would be 222–434 kt, of which at most 1/3 could potentially be obtained by circular usage from end-of-life wind turbines. However, the existing low secondary recovery rate of rare-earth elements makes the available circular amounts very small. Shifting to a wind power market dominated by direct-drive turbines may increase the cumulative rare-earth demand by up to 34 %. Without material intensity reduction for the wind power technologies, an additional 38 % demand for rare-earth elements will occur, exacerbating the risk of shortage.

Optimal Dispatching of Energy Hub for New Energy Consumption

In recent years, with the continuous increase of wind power installed capacity, due to insufficient flexibility of the system, there has been a serious problem of wind power abandonment. In response to this phenomenon, this article improves the flexibility of the regional energy system by adding various flexible devices. Based on the concept of an energy hub, an energy hub model including wind power, CHP, heat pumps, heat storage, and other equipment is established. On this basis, considering system operating costs, wind abandonment penalty costs, and electricity purchase costs, a multi-objective regional energy system flexibility configuration model based on energy hubs was established to explore the feasibility of this model in improving wind power consumption. Finally, specific examples were combined in two different cases for analysis. The results indicate that the model has a very good effect on improving system flexibility and improving wind power consumption rate.

Analysis and forecast of the substitution potential of China's wind power-hydrogen production for fossil fuel hydrogen production

The predominant utilization of fossil fuels for hydrogen production in China leads to substantial carbon emissions, posing a challenge to China's objective of achieving carbon neutrality. With the rapid development of wind power in China, hydrogen can be produced through water electrolysis powered by wind-generated electricity. This process offers the potential to gradually replace the hydrogen production from fossil fuel (grey hydrogen), thus promoting the low-carbon transformation of China's hydrogen energy industry. However, further investigation is required to determine the potential displacement of grey hydrogen through the production of hydrogen from wind power in China in the future. To address this issue, this study first obtained wind speed data for the entire China region from the MERRA-2 database. Subsequently, based on the wind speed data for each specific area, the wind turbine model with the lowest power generation cost was selected from the eight available wind turbine models. On this basis, the mixed integer linear programming method is used to construct the cost analysis model of wind power-hydrogen production (WPHP). Based on the local wind power generation data, calculations are performed to determine the optimal installed capacity of the wind turbine, electrolyzer, and hydrogen storage tank within the WPHP system. Additionally, the optimal scheduling of the WPHP system is determined. These optimizations collectively result in the best WPHP cost. Based on this research, the optimal cost of WPHP for each geographical grid in China can be calculated and compared with the local grey hydrogen cost. The analysis reveals that in some areas of Xinjiang, Gansu, and Guizhou, exhibit low costs of wind power. This suggests that the substitution of grey hydrogen with WPHP can be achieved in these areas. Based on technological progress and changes in carbon trading prices, this paper predicts that WPHP in China could potentially replace 16.35–28.69 million tons of grey hydrogen by 2040. Moreover, by 2060, WPHP in China is projected to replace 76.72–92.01 million tons of grey hydrogen. According to the sensitivity analysis conducted, it has been determined that the efficiency of the electrolyzer and the price of the wind power equipment exert the greatest impact on the cost of hydrogen production.

Semi-supervised adversarial discriminative learning approach for intelligent fault diagnosis of wind turbine

Wind turbines play a crucial role in renewable energy generation systems and are frequently exposed to challenging operational environments. Monitoring and diagnosing potential faults during their operation is essential for improving reliability and reducing maintenance costs. Supervised learning using data-driven techniques, particularly deep learning, offers a viable approach for developing fault diagnosis models. However, a significant challenge in practical wind power equipment lies in the scarcity of annotated samples required to train these models effectively. This paper proposes a semi-supervised fault diagnosis approach specifically designed for wind turbines, aiming to address this challenge. Initially, a semi-supervised deep neural network is constructed using adversarial learning, where a limited set of annotated samples is used in conjunction with a vast amount of unannotated samples. The health status features present in the unannotated samples are leveraged to capture a generalized representation of the underlying features. Subsequently, a metric learning-guided discriminative features enhancement technique is employed to improve the separability of different manifolds, thereby enhancing the performance of the semi-supervised training process. By employing this methodology, it becomes possible to develop a fault diagnosis model with superior accuracy using only a limited amount of annotated samples. Comprehensive fault diagnosis experiments were conducted on a wind turbine fault dataset, revealing the efficacy and superiority of the presented methodology.

The Financing Principles of High Carbon Enterprises Green Development Based on Double Carbon Target in China—Take MYSE as an Example

The “double carbon target” puts forward new requirements for China’s energy structure adjustment. As a leader in offshore wind power equipment, MYSE is an active practitioner of the concept of “carbon neutrality”. The company has established a green growth path and is actively experimenting with various green financial instruments to meet its rapidly growing capital needs. This paper describes in detail the “double carbon goal” to promote the intelligent green development of MYSE, as well as the diversified financing strategy based on green development. Taking the company’s experience of issuing green notes, green bonds, listing on London Stock Exchange and obtaining the “Green economy mark” as examples, this paper analyzes the conditions and strategies of green financing for high-carbon enterprises, and deeply understands the mutual support and promotion relationship between green development and green finance of companies. Deeply understand the positive significance of innovative financing channels and the use of green financing tools for the company’s green development, and provide reference and inspiration for local high-carbon enterprises to actively use green financing tools.

Optimal Maintenance Schedule for a Wind Power Turbine with Aging Components

Wind power is one of the most important sources of renewable energy available today. A large part of the cost of wind energy is due to the cost of maintaining wind power equipment. When a wind turbine component fails to function, it might need to be replaced under circumstances that are less than ideal. This is known as corrective maintenance. To minimize unnecessary costs, a more active maintenance policy based on the life expectancy of the key components is preferred. Optimal scheduling of preventive maintenance activities requires advanced mathematical modeling. In this paper, an optimal preventive maintenance algorithm is designed using the renewal-reward theorem. In the multi-component setting, our approach involves a new idea of virtual maintenance that allows us to treat each replacement event as a renewal event even if some components are not replaced by new ones. The proposed optimization algorithm is applied to a four-component model of a wind turbine, and the optimal maintenance plans are computed for various initial conditions. The modeling results clearly show the benefit of PM planning compared to a pure CM strategy (about 30% lower maintenance cost).

Retracted: Fault Diagnosis Method for Wind Power Equipment Based on Hidden Markov Model

Retracted: Fault Diagnosis Method for Wind Power Equipment Based on Hidden Markov Model

Bearing Fault Diagnosis Using ACWGAN-GP Enhanced by Principal Component Analysis

Rolling bearings are one of the most widely used parts in all kinds of rotating machinery (including wind power equipment) and also one of the most easily damaged parts, which makes fault diagnosis of rolling bearings a promising research field. To this end, recent studies mainly focus on fault diagnosis cooperating with deep learning. However, in practical engineering, it is very challenging to collect massive fault data, resulting in low accuracy of bearing fault classification. To solve the problem, an auxiliary classifier optimized by a principal component analysis method is proposed to generate an adversarial network model in which Wasserstein distance and gradient penalty are used to improve the stability of the network training process in case of over-fitting and gradient disappearance during model training. Specifically, we implement the model system using two main components. First, the one-dimensional time domain signal is transformed into a two-dimensional grayscale image and the principal component analysis is employed to reduce the dimension of the original data; this is instead of random noise as the input of the generator thereby preserving the characteristics of the original data to a certain extent. Second, in a generative adversarial network, the label information of the fault data is inserted into the generator to achieve supervised learning, thereby improving the data generation performance and reducing the training time cost. The experimental results show that our model could produce high-quality samples that are similar to real samples and that it could significantly improve the classification accuracy of fault diagnosis in the case of insufficient fault samples.

Reconstruction of Unsteady Wind Field Based on CFD and Reduced-Order Model

Short-term wind power forecasting is crucial for updating the wind power trading strategy, equipment protection and control regulation. To solve the difficulty surrounding the instability of the statistical model and the time-consuming nature of the physical model in short-term wind power forecasting, two innovative wind field reconstruction methods combining CFD and a reduced-order model were developed. In this study, POD and Tucker decomposition were employed to obtain the spatial–temporal information correlation of 2D and 3D wind fields, and their inverse processes were combined with sparse sensing to reconstruct multi-dimensional unsteady wind fields. Simulation and detailed discussion were performed to verify the practicability of the proposed algorithms. The simulation results indicate that the wind speed distributions could be reconstructed with reasonably high accuracy (where the absolute velocity relative error was less than 0.8%) using 20 sensors (which only accounted for 0.04% of the total data in the 3D wind field) based on the proposed algorithms. The factors influencing the results of reconstruction were systematically analyzed, including all-time steps, the number of basis vectors and 4-mode dimensions, the diversity of CFD databases, and the reconstruction time. The results indicated that the reconstruction time could be shortened to the time interval of data acquisition to synchronize data acquisition with wind field reconstruction, which is of great significance in the reconstruction of unsteady wind fields. Although there are still many studies to be carried out to achieve short-term predictions, both unsteady reconstruction methods proposed in this paper enable a new direction for short-term wind field prediction.

Current Harmonic Control Method of Wind Turbine Based on PIR Controller

Wind power equipment technology is the basis for the development of wind power generation. Aiming at the problem that the traditional PI controller is difficult to control the AC component of the current in the DQ axis, resulting in a large number of current harmonics in the process of power generation, which seriously affects the efficiency of power generation. This paper proposes a current harmonic control method for wind turbines based on a PIR controller. The mathematical model of wind power generation is constructed based on the synchronous rotating coordinate system. The current harmonic components are calculated according to the DQ rotor speed, shaft component, and other parameters. The PIR current harmonic controller is established by improving the ordinary PI controller to track and control the AC current signal. And finally, a current harmonic control target value is obtained according to different main control targets, and a specific harmonic component is eliminated. The experimental results show that the harmonic content is 3.12%, 2.06%, 1.17%, and 1. 02%, respectively, when the harmonic current order is 5, 7, 11, and 13. It is better than the comparison method. Therefore, this method can greatly reduce the harmonic content of the current in the process of different current orders, reduce the harmonic loss, and improve the generating efficiency of the unit.

Design of wind power equipment monitoring system based on Huawei AI chip

With the gradual development of wind power, developing a wind power equipment monitoring system for wind farms is of great significance for ensuring the normal operation of wind farms and subsequent data analysis and processing. This paper develops a wind farm equipment monitoring system based on Huawei AI chips. The frame structure, R&D process, system construction process, and corresponding electronic circuits of the power vision edge computing system design based on Huawei AI chips are introduced in detail. The system can realize the intelligent processing of the data of the main electrical equipment of the wind farm and has functions such as the perception and acquisition of wind power resource data, the monitoring of the operating status of wind power equipment, and the advanced functions and intelligent processing of data.