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Wind power generation is different from traditional power generation in which wind power output is highly stochastic and spatio-temporally dependent. In the optimal scheduling problem of wind power grid-connected power system, ensuring the optimal execution of power scheduling in different wind power scenarios is the key of the decision-making problem. Therefore, high quality wind power scenario generation is of great importance. The spatiotemporal correlation of the output power of wind power plants are characterized based on Gaussian stochastic process and spatiotemporal covariance function, and the joint probability distribution is established by the Pair Copula model, and specific scenarios are implemented by the method of empirical probability inverse transformation. A variety of scene metrics of the generated scene are generated, which verifies the superiority of the generated scene. Finally, based on the modified IEEE 6-bus system, a mixed integer programming model for the unit output of the power system is established to solve the problems in different scenarios and verify the economic advantages of the scenario generation method in the dispatching problem of wind power grid connection.

A novelty dual-stator brushless doubly-fed generator (DSBDFG) with magneticbarrier rotor structure is put forward for application in wind power. Compared with a doublyfed induction generator, the DSBDFG has virtues of high reliability and low maintenance costs because of elimination of brush and sliprings components. Therefore, the proposed structure has tremendous potential as a wind power generator to apply in wind power. According to the operating principle of electric machine, the DSBDFG is studied in wind power application. At first, the topology, the winding connecting, the rotor structure, the power flow chart of different operating models and the variable speed capability of electric machine are discussed and analyzed. Then, a 50 kW DSBDFG is designed. Based on the principal dimension of the design electric machine, the electromagnetic characteristics of the DSBDFG with different running modes are analyzed and calculated to adopt the numerical method. From the result, it meets the requests of electromagnetic consistency and winding connecting in the design electric machine. Meanwhile, it confirms the proposed DSBDFG has the strong ability of speed regulation.

Wind power generation has increased in China to achieve the target of decreasing CO2 emissions by 2050, but there are high levels of wind curtailment due to the mismatch between electricity supply and demand. This paper proposes a single-stage air source heat pump coupled with thermal storage for building heating purposes. The main objective is to find the proper system designs and operating strategy, which can help to avoid peak demand periods while obtaining minimized running costs and reduced wind energy curtailment. Dynamic simulations were performed using TRNSYS to investigate its use in a typical office building based on an actual electricity tariff, wind power, and meteorological data. The proper system designs, including the tank size and thermal storage temperature, were determined to maximize the system’s performance. It was found that a proper combination of the two parameters exists for a specific application. Further, results showed that the use of auxiliary electric heating is necessary for single-stage air source heat pumps to participate in a wind curtailment reduction. The operating strategy of the system was also studied. Results indicate that by implementing a proper operating strategy, non-renewable power consumption can be reduced by 11% for the studied building, with a total wind power utilization of 3348 kWh during the heating season while still satisfying the heating demands of users. These findings can contribute to the green and low-carbon development of the building industry and further enhance the grid’s accommodation capacity for renewable energy sources.

Recently, there has been a push by countries to diversify their energy mix considering various factors. In this regard, there have been several studies conducted to assess the potential for using sources such as wind and solar to generate supplemental energy to the already present energy generation setup. In this regard, this study explores the potential of wind for the Commonwealth of Kentucky. To perform this study, wind data were sourced for eight locations across Kentucky from the publicly accessible wind speed information present at Weather Underground for the years 2020–2021 (two years). An analysis was performed concerning the seasonal, monthly, and hourly variation in the wind speed so as to identify the expected times of sufficient wind energy generation. Moreover, a comparison of the collected data was performed with data from a home-based weather station as well as a deployed wind turbine to validate the variation pattern of the publicly sourced data. Finally, in order to investigate the variation patterns of wind and solar energy sources, a comparative analysis was also performed using data from a solar power generation plant in Kentucky. A seasonal and monthly complementarity was observed between the wind and solar energy. However, when considering daily patterns, the wind was found to follow solar generation with an offset. While further research is required, this analysis indicates that it is possible to deploy wind energy power generation projects in the Commonwealth of Kentucky. The seasonal complementary behavior of wind and solar energy can be used along with battery storage in conjunction with natural gas to provide a diversified electricity generation portfolio.

Wind power is an important energy source that can be used to supply clean energy and meet current energy needs. Despite its advantages in terms of zero emissions, its main drawback is its intermittency. Deterministic approaches to forecast wind power generation based on the annual average wind speed are usually used; however, statistical treatments are more appropriate. In this paper, an intelligent statistical methodology to forecast annual wind power is proposed. The seasonality of wind is determined via a clustering analysis of monthly wind speed probabilistic distribution functions (PDFs) throughout n years. Subsequently, a methodology to build the wind resource typical year (WRTY) for the n + 1 year is introduced to characterize the resource into the so-called statistical seasons (SSs). Then, the wind energy produced at each SS is calculated using its PDFs. Finally, the forecasted annual energy for the n + 1 year is given as the sum of the produced energies in the SSs. A wind farm in Mexico is chosen as a case study. The SSs, WRTY, and seasonal and annual generated energies are estimated and validated. Additionally, the forecasted annual wind energy for the n + 1 year is calculated deterministically from the n year. The results are compared with the measured data, and the former are more accurate. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Shanghai Jiao Tong University (1006-2467) is the property of Journal of Shanghai Jiao Tong University Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, based on the lift-type wind turbine, an adaptive double-drive lift–drag composite vertical-axis wind turbine is designed to improve the wind energy utilization rate. A drag blade was employed to rapidly accelerate the wind turbine, and the width of the blade was adaptively adjusted with the speed of the wind turbine to realize lift–drag conversion. The aerodynamic performance analysis using Fluent showed that the best performance is achieved with a blade curvature of 30° and a drag-type blade width ratio of 2/3. Physical experiments proved that a lift–drag composite vertical-axis wind turbine driven by dual blades can start when the incoming wind speed is 1.6 m/s, which is 23.8% lower than the existing lift-type wind turbine's starting wind speed of 2.1 m/s. At the same time, when the wind speed reaches 8.8 m/s, the guide rail adaptive drag-type blades all contract and transform into lift-type wind turbine blades. The results show that the comprehensive wind energy utilization rate of the adaptive dual-drive lift–drag composite vertical-axis wind turbine was 5.98% higher than that of ordinary lift-type wind turbines and can be applied to wind power generation in high-wind-speed wind farms. [ABSTRACT FROM AUTHOR]

This study introduces a novel hybrid forecasting model for wind power generation. It integrates Artificial Neural Networks, data clustering, and Particle Swarm Optimization algorithms. The methodology employs a systematic framework: initial clustering of weather data via the k-means algorithm, followed by Pearson's analysis to pinpoint pivotal elements in each cluster. Subsequently, a Multi-Layer Perceptron Artificial Neural Network undergoes training with a Particle Swarm Optimization algorithm, enhancing convergence and minimizing prediction discrepancies. An important focus of this study is to streamline wind forecasting. By judiciously utilizing only sixteen observation points near a wind farm plant, in contrast to the complex global numerical weather prediction systems employed by the European Center Medium Weather Forecast, which rely on thousands of data points, this approach not only enhances forecast accuracy but also significantly simplifies the modeling process. Validation is performed using data from the Italian National Meteorological Centre. Comparative assessments against both a persistence model and actual wind farm data from Southern Italy substantiate the superior performance of the proposed hybrid model. Specifically, the clustered Particle Swarm Optimization-Artificial Neural Network-Wind Forecasting Method demonstrates a noteworthy improvement, with a reduction in mean absolute percentage error of up to 59.47% and a decrease in root mean square error of up to 52.27% when compared to the persistence model. [ABSTRACT FROM AUTHOR]

Summary: Numerous wind turbines form large‐scale wind farms, which are complex nonlinear systems with uncertain parameters. The issue of maximum wind energy capture and coordinated control has always been a research hotspot. In this article, under the condition of limited input and uncertain parameters, the preset controller and the adaptive cooperation control are designed to realize the maximum wind energy capture for every wind turbine and the adaptive cooperation control of multiple wind turbines. The research gap lies in that the Hamilton model of wind power generation system is established with uncertain parameters, and the preset controller (method) is designed to capture the maximum wind energy. Under the hypothesis that the uncertain part can be expressed as a linear form about unknown parameter, and using the saturation function processing method in the diagonal matrix, an adaptive feedback controller with limited input is designed to realize the adaptive cooperation control of multiple wind turbines. The simulation results show that under the conditions such as variable wind speed, limited input and uncertain parameters, the wind turbine remain normal operation at the desired angular velocity. It can be concluded that not only the maximum wind energy capture is realized under the condition of variable wind speed, in which the wind turbine can operate on the optimal power curve to improve the utilization of wind energy, but also the adaptive cooperation control of multiple wind turbines can be achieved with limited input and parameter perturbation. [ABSTRACT FROM AUTHOR]

In this study, the focus is on the magnetic path formation and its effects on the performance of a counter-rotating dual-rotor permanent-magnet flux-switching machine (CR-DRPMFSM) for direct-drive counter-rotating wind power generation, based on different stator slot and rotor pole combinations. To fully exploit rotor-shaft bore and improve fault-tolerant design, as well as increase torque density, dual-rotor topologies with the capability for dual electrical and dual mechanical ports are investigated. Moreover, the direct-drive counter-rotating wind power generation technique offers a brushless topology, thus reducing maintenance cost and improving energy conversion efficiency compared to single-blade wind turbine systems. Using finite element analysis (FEA), the inherent magnetic coupling of the series and parallel paths shows varied impacts on the electromagnetic performance of four different CR-DRPMFSMs based on the slot/pole combinations (MI to MIV) considered in this study. The key electromagnetic performance indices, such as torque, cogging torque, torque ripple, power factor, and efficiency, show proportionate variation to the coupling level. A comparative analysis shows that MI exhibits higher average torque, lower torque ripples, and high efficiency, reaching 90% with a power factor of 0.6. As an optimal design, an MI test prototype is developed. The experimental test prototype validates the FEA results under no-load and on-load conditions. [ABSTRACT FROM AUTHOR]

Copyright of Experimental Technology & Management is the property of Experimental Technology & Management Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Given that wind farms are required to contribute to ancillary services, this paper proposes a preventive control strategy to improve the static voltage stability margin (VSM) of the power system by controlling the power factor from a previously selected set of wind farms. The selected group of wind farms is determined by calculating a PF sensitivity index that indicates the wind farms that most positively affect the VSM. The optimal PF regulation of this group of wind farms is then obtained by applying an optimal power flow (OPF) to maximize the distance from the current operating point to the maximum loading point (MLP). The dispatch of active and reactive powers of conventional fossil fuel and hydroelectric power plants is also considered control variables in the proposed optimization problem. The method is validated using a real test system, based on Northeast subsystem of the Brazilian interconnected power system with a high share of wind power considering different scenarios. The results show the applicability of the proposed voltage preventive control strategy, indicating the sets of effective actions in terms of PF regulation of wind farms, as well as the optimal dispatch of active and reactive powers of conventional power plants that must be taken to improve the static VSM of the system. [ABSTRACT FROM AUTHOR]

Renewable energy has significant output fluctuation. In areas with a small thermal power generation capacity, some wind power plants need to install storage batteries. Additionally, fluctuations in wind power generation output on remote islands are putting pressure on the control capacity of thermal power generation. This paper proposes a new wind speed prediction method using a wind generation power system with redox flow batteries. A fixed order of the autoregressive (AR) coefficient predicts the wind speed 60 minutes ahead, the AR model's predicted wind speed is added to the average value of the difference between the actual wind speed and the AR model's predicted wind speed, and the prediction wind speed curve is passed through a low-pass filter. As a result, the predicted power output of the power system was determined with high prediction accuracy by using the measured wind speed data, and frequency fluctuations were suppressed within an acceptable range. [ABSTRACT FROM AUTHOR]

The blades of some airborne equipment are prone to icing under supercooled cloud conditions. In this paper, we propose an anti-deicing spray method to prevent blades from icing at low temperatures. Using computational fluid dynamics modeling and orthogonal experimental methods, we investigated the effects of the blade angle of attack, inlet wind speed, and nozzle mass flow rate on the thickness and coverage of the liquid layer of spray material and examined the use of deflectors in this study. We found the magnitude and change rule of the influence of the previously mentioned parameters on the liquid film thickness and coverage of sprayed material to be the nozzle mass flow rate is greater than the blade angle of attack and greater than the inlet wind speed. Under the optimal combination of conditions of α = 30°, u0 = 6 m/s, and Q = 0.003 kg/s, the liquid film thickness was maximized, and the liquid film thickness was 0.037 mm; under the optimal combination of conditions of α = 60°, u0 = 6 m/s, and Q = 0.003 kg/s, the liquid film coverage was maximized, and the liquid film coverage was 99.81%. The anti-deicer spraying method proposed herein for use on blades is effective when considered from a number of perspectives. It provides an innovative and feasible solution to the wind turbine blade freezing problem. However, the method must be explored and modified to maximize its chances of general application, and other factors must also be considered to fully optimize the sprinkler de-icing technique to improve the performance and reliability of blades. [ABSTRACT FROM AUTHOR]

Copyright of Electric Machines & Control / Dianji Yu Kongzhi Xuebao is the property of Electric Machines & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper investigates, for the first time, the accuracy of normalized power curves (NPCs), often used to incorporate uncertainties related to wind and solar power generation, when integrating renewable distributed generation (RDG), in the radial distribution system (RDS). In this regard, the present study proposes a comprehensive, simple, and more accurate model, for estimating the expected hourly solar and wind power generation, by adopting a purely probabilistic approach. Actually, in the case of solar RDG, the proposed model allows the calculation of the expected power, without going through a specific probability density function (PDF). The validation of this model is performed through a case study comparing between the classical and the proposed model. The results show that the proposed model generates seasonal NPCs in a less complex and more relevant way compared to the discrete classical model. Furthermore, the margin of error of the classical model for estimating the expected supplied energy is about 12.6% for the photovoltaic (PV) system, and 9% for the wind turbine (WT) system. This introduces an offset of about 10% when calculating the total active losses of the RDS after two RDGs integration. [ABSTRACT FROM AUTHOR]

In this paper, the increase in the quality of the rotor speed of wind turbines and the decrease in mechanical loads on the turbines are investigated. Adjusting the angle of the blade to the nominal wind speed, the rotor speed of the wind turbine is maintained at its nominal value. Using control methods (such as proportional integral (PI), genetic algorithms (GAs), and particle swarm optimisation (PSO)), different results can be recovered. In addition, individual control of the blade tilt angle allows us to reduce the mechanical loads on the turbine with the control methods. The wind turbine was modelled in Matlab/Simulink. The simulation results show that individual control of the blade tilt angle ensures the quality of the rotor speed of the wind turbine and reduces the balanced periodic loads on the wind turbine. In the first part, we study the wind turbine in a global way, as well as the method used to calculate them. Then, we discuss the FAST system, which was used to model the wind turbine, as well as the design of individual pitch angle control. As a result, it is possible to reduce the fatigue of the mechanical wind turbine parts. According to the study, the mechanical load for all three blades was reduced by an average of 44 % compared to the PI and PSO methods and by 1 % compared to the PI and GA methods. The control of the pitch angle in wind energy systems is performed with different control methods. The study analysis of the mechanical loads found that they are largely balanced. Winds that blow perpendicular to the turbine blades on the x-axis provide these loads.

A new type of generator, a transgenerator, is introduced, which integrates the wind turbine and flywheel into one system, aiming to make flywheel-distributed energy storage (FDES) more modular and scalable than the conventional FDES. The transgenerator is a three-member dual-mechanical-port (DMP) machine with two rotating members (inner and outer rotors) and one stationary member (stator). The transgenerator–flywheel system is introduced with its configuration, transgenerator overview, flywheel operation principle and power management strategies, and control system. Simulations are performed in MATLAB 2023b/Simulink to verify the system viability, including control system verification and flywheel storage performance evaluation. The results show that the inner and outer rotors can be controlled independently with an accurate and fast control response, and the grid-side control works properly. The flywheel performs well, with considerable charging power and storage capacity.

Taking small and medium-sized switched reluctance wind turbines as the research object, aiming at the requirements of the rapidity and accuracy of the maximum power point tracking control under different wind speed conditions during the generation process, a maximum power point tracking control strategy based on adaptive weighted grey Wolf optimization PID algorithm was proposed. When the external wind speed changes, the optimal speed of the wind turbine is calculated according to the real-time wind speed. The difference between the actual speed and the weighted adaptive gray wolf PID control algorithm is used as the input of the closed-loop PID control parameter of the speed, so as to output the optimal duty ratio of the voltage pulse width modulation and realize the maximum power point tracking control under the variable wind speed. The simulation results show that compared with the traditional PID control, the adaptive weighted grey wolf optimization PID algorithm can quickly and accurately achieve the maximum power point tracking control under the condition of wind speed change. [ABSTRACT FROM AUTHOR]

The design, operational planning, and integration of wind power plants with other renewables and the grid face challenges attributed to the intermittent nature of wind power generation. Addressing this issue necessitates the development of a smart wind power (and in particular wind speed) forecasting approach. This is a complex task due to substantial fluctuations in wind speed. To overcome the inherent stochastic nature of wind speed and mitigate related challenges, traditionally, numerical weather prediction (NWP) models are employed for wind speed forecasting. However, the applicability of NWP models is limited to short-term forecasting due to their computational constraints. In this study, a hybrid AI-based approach is proposed to improve forecast accuracy over a 48 h horizon for the city of Montreal. The results demonstrate that by integrating the probability distribution of wind speed with a deep learning model, the forecasted values align closely with the observed values in terms of seasonality and trend, exhibiting enhanced accuracy. Evaluation metrics reveal a substantial reduction in the root mean squared error (13–31%) across three prediction horizons (summer, fall, and winter) compared to a single long, short-term memory model. Furthermore, integrating the improved model with the numerical weather prediction model yields increased accuracy and decreased error compared to the LSTM–Weibull model. [ABSTRACT FROM AUTHOR]

To alleviate the environmental pollution caused by waste wind turbine blades and provide new ideas for recycling, the thermochemical characteristics of four basic components of composite materials commonly used in wind turbine blades were studied in this paper. Thermogravimetric experiments in different atmospheres and heating rates, and thermogravimetric‐infrared combined experiments were carried out. The results show that the reaction of epoxy resin and thermoplastic polyurethane was easy and deep in N2 and CO2. Except for glass fiber, the other three components showed different combustion characteristics in air. The maximum reaction rates and activation energy of resin‐based components in N2 increased with increasing heating rate. The pyrolysis products of the four basic components all contained CO2 and CO, and some aromatic substances could be generated during the thermal transformation of epoxy resin and thermoplastic polyurethane. It can be concluded that the thermal transformation properties of the basic components of the composite material of wind turbine blades can be used to treat the waste blades, making the resin as the matrix material undergo a thermal transformation reaction and recover the fiber. This is to effectively guarantee the green and sustainable development of wind power generation. [ABSTRACT FROM AUTHOR]

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Wind power generation is considered an effective way for ships to harness wind energy, and the aerodynamic characteristics of wind turbines determine wind energy utilization and efficiency. However, traditional vertical axis wind turbines have intermediate shafts and support rods, which result in large negative effects in the research of the wind turbine aerodynamic characteristics. To address this issue, a Straight-Bladed Vertical Axis Wind Turbine (SB-VAWT) without intermediate support axes is proposed. The turbine can flexibly change the number of blades, rotor diameter, and installation position of blades. The static aerodynamic performance of the wind turbine with different combinations was tested in a wind tunnel laboratory at 10 m/s. The results show that the radius of the wind turbine has a greater effect on the drag coefficient for the same number of blades, with an inverse relationship between the drag coefficient and radius, and a positive association between lift coefficient, static torque coefficient, and radius. The drag coefficient is proportional to the number of blades at the same radius, while the static torque coefficient is inversely proportional to the number of blades. According to the results, placing the initial location in the azimuth range between 30° and 50° can obtain the maximum initial starting torque. Moreover, a wind turbine with a radius of 16 cm can achieve a higher average torque. Changes in the number of blades can significantly impact turbine properties, resulting in wind turbines with distinct features.

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The prediction of wind power output is part of the basic work of power grid dispatching and energy distribution. At present, the output power prediction is mainly obtained by fitting and regressing the historical data. The medium- and long-term power prediction results exhibit large deviations due to the uncertainty of wind power generation. In order to meet the demand for accessing large-scale wind power into the electricity grid and to further improve the accuracy of short-term wind power prediction, it is necessary to develop models for accurate and precise short-term wind power prediction based on advanced algorithms for studying the output power of a wind power generation system. This paper summarizes the contribution of the current advanced wind power forecasting technology and delineates the key advantages and disadvantages of various wind power forecasting models. These models have different forecasting capabilities, update the weights of each model in real time, improve the comprehensive forecasting capability of the model, and have good application prospects in wind power generation forecasting. Furthermore, the case studies and examples in the literature for accurately predicting ultra-short-term and short-term wind power generation with uncertainty and randomness are reviewed and analyzed. Finally, we present prospects for future studies that can serve as useful directions for other researchers planning to conduct similar experiments and investigations. [ABSTRACT FROM AUTHOR]

The accuracy of wind power generation predicted by computational fluid dynamics (CFD) simulations combined with meteorological wind data was validated based on comparisons with directly measured data for a small vertical axis wind turbine system installed on a university campus. The CFD simulations were performed in accordance with established guidelines and frameworks for the prediction of urban wind environments. At the rooftop location, where small wind turbines are typically installed, the deviations in wind velocity from the measurements are quite large. However, in the present study, the prediction accuracy for the wind turbine site, which was 4 m above the ground, was acceptable. The total power generation estimated using the assumed power curve based on the rated output of the turbine was 56% larger than that directly measured by the power generator. However, using the power curves obtained from the measurements, the total power generation could be predicted with a high degree of accuracy and with an error of approximately 3%. It is suggested that not only the accuracy of the wind velocity but also that of the power curve is very important because they are directly related to that of the predicted power generation. [ABSTRACT FROM AUTHOR]

In this paper, the increase in the quality of the rotor speed of wind turbines and the decrease in mechanical loads on the turbines are investigated. Adjusting the angle of the blade to the nominal wind speed, the rotor speed of the wind turbine is maintained at its nominal value. Using control methods (such as proportional integral (PI), genetic algorithms (GAs), and particle swarm optimisation (PSO)), different results can be recovered. In addition, individual control of the blade tilt angle allows us to reduce the mechanical loads on the turbine with the control methods. The wind turbine was modelled in Matlab/Simulink. The simulation results show that individual control of the blade tilt angle ensures the quality of the rotor speed of the wind turbine and reduces the balanced periodic loads on the wind turbine. In the first part, we study the wind turbine in a global way, as well as the method used to calculate them. Then, we discuss the FAST system, which was used to model the wind turbine, as well as the design of individual pitch angle control. As a result, it is possible to reduce the fatigue of the mechanical wind turbine parts. According to the study, the mechanical load for all three blades was reduced by an average of 44 % compared to the PI and PSO methods and by 1 % compared to the PI and GA methods. The control of the pitch angle in wind energy systems is performed with different control methods. The study analysis of the mechanical loads found that they are largely balanced. Winds that blow perpendicular to the turbine blades on the x-axis provide these loads. [ABSTRACT FROM AUTHOR]

Copyright of Large Electric Machine & Hydraulic Turbine is the property of Large Electric Machine & Hydraulic Turbine Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Engineering for Thermal Energy & Power / Reneng Dongli Gongcheng is the property of Journal of Engineering for Thermal Energy & Power and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract Wind power generation does not emit greenhouse gases or pollutants, but there are some carbon emissions from the manufacturing, transportation, operation, and waste disposal of wind turbines. Directly driven permanent magnet and doubly fed asynchronous wind turbines currently have the largest market share in China, but few Chinese studies have compared their differences in carbon reduction potential. This paper uses life cycle assessment (LCA) to quantitatively analyze the full life cycle carbon emissions of the two wind turbines to determine which type of wind turbine has greater carbon reduction potential, obtaining the following results. (1) The full life cycle greenhouse gas emissions of 2.5 MW directly driven permanent magnet and doubly fed asynchronous wind turbines are 8.48 and 10.43 g CO2/kWh, respectively. The direct‐driven permanent magnet wind turbine is superior in terms of carbon reduction. (2) The stage with the greatest impact and the greatest difference between the two wind turbines in the full life cycle is the production stage, during which the carbon emissions of the directly driven permanent magnet and doubly fed asynchronous wind turbines are 1.045 × 106 and 1.210 × 106 kg, respectively. (3) According to sensitivity analysis, proper waste disposal and transportation can reduce carbon emissions from wind turbines. These research findings can be used to help achieve carbon peaking and neutrality goals, as well as the technological development of wind power enterprises.

Abstract To solve the problem of large output power fluctuations in wind turbines and improve grid adaptability, a hydraulic energy storage system is introduced in traditional hydraulic wind turbines. Based on the working principle of energy storage hydraulic wind turbines, an energy storage hydraulic wind turbine state space model is established, and the feedback linearization method is introduced to solve the multiplication nonlinear problem in the modeling process. The output power is taken as the control output, and the torque compensation controller is established with the feedback linearization method. The displacement of the variable displacement pump motor is controlled to realize hydraulic energy storage system energy charging and discharging, and the wind turbine output power smoothing control is realized with the fluctuating wind speed. The power smoothing control strategy is verified with the 24 kW energy storage hydraulic wind turbines semi‐physical simulation experimental platform. The proposed control strategy lays the groundwork for the wide application of the energy storage hydraulic wind turbines.

Abstract Flexible combined cooling, heating, and power (CCHP) systems are effective in integrating wind sources. As an attractive, clean, and large‐scale energy storage technique, the advanced adiabatic compressed air energy storage (AA‐CAES) can store and generate both electricity and heating, and also provide cooling during expansion under certain conditions. Although AA‐CAES has immense potential in multi‐energy supply systems, CCHP dispatch with AA‐CAES and wind power generation (WPG) is yet to be systematically studied. In this study, the economic dispatch of an AA‐CAES system equipped with WPG is addressed. The AA‐CAES system is comprehensively modelled by considering its thermal characteristics, air‐temperature changes due to heating exchange, air storage constraint, and other factors, particularly the heat supply to the air for expansion, which is a key factor that influences the cooling supply. Subsequently, the cooling, heating, and power of the AA‐CAES system are dispatched to minimise the operating cost under different supply modes. In conclusion, the proposed method is demonstrated using an integrated energy system in an industrial park, and the operation cost of the AA‐CAES system is minimised. The numerical results demonstrate that the participation of AA‐CAES in CCHP dispatch can curtail WPG and reduce operation costs. The economics of the different supply modes of AA‐CAES are also discussed.

This paper introduces a 6-phase switched flux permanent magnet (SFPM) generator with high permanent magnet (PM) utilization, designed for a 2 kW wind power generation application. Two different SFPM generator variants were designed and analyzed by applying E-core and C-core techniques to a state-of-the-art (SOTA) benchmark SFPM machine structure. Key design variables, namely, the rotor pole number, split ratio, rotor pole width, and the number of turns per coil were optimized. The electromagnetic performance, specifically as a wind generator considering objectives of achieving high PM utilization, minimizing voltage waveform distortion, and reducing cogging torque, was evaluated using finite element analysis. The results obtained from the analysis demonstrated that both the E-core and C-core SFPM generators exhibited significantly higher flux-linkage per unit PM volume compared to the SOTA benchmark generator. In particular, the optimized C-core SFPM generator demonstrated nearly double the magnet utilization, half the cogging torque, and higher efficiency when compared to the benchmark SFPM generator. Notably, the C-core SFPM generator achieved a power per PM volume of 24,979 kW/m3, positioning it as the second-highest performing PM generator among all SOTA generators. Lastly, a prototype of the 6-phase 12-stator/25-rotor C-core SFPM generator was manufactured and tested to validate the analytical design.

This study proposes an available transfer capability (ATC) assessment approach in an intraday market that would enable wind energy participants to raise their level of integration without exposing them to high risk. The presented methodology allows the transmission system operator (TSO) to assess ATC near to the real state while considering voltage stability concerns as well as the uncertainties of the forecasted load and wind power. The proposed formulation is designed so that the existing VAR sources are appropriately exploited during the ATC assessment to maximize its anticipated value and boost the transaction between various zones. To alleviate the complexity of analyzing N-1 contingencies in the ATC estimation, a linear sensitivity technique is applied for ranking the most severe contingencies to be examined carefully. A two-level hybrid algorithm using the primal-dual interior point method (PDIPM) and an improved grey wolf optimizer (IGWO) is suggested for solving the problem. The performance of the proposed approach has been evaluated by its application on IEEE 30- reliability test system (RTS). The outcomes confirm the viability of the proposed approach for optimizing ATC value for various transactions through the best use of installed VAR devices, considering different locations of wind farms. The obtained findings also demonstrate the effectiveness of the suggested method for determining the most severe contingency associated with each scenario under consideration.

Ever increasing integration of renewable resources into contemporary power systems has imposed a unique set of challenges for system protection. Effectiveness of techniques based on conventional system protection theory has long been questionable in protecting power systems with higher penetration of inverter-based resources (IBRs). Fault direction identification is one of the commonly affected protection functions in the presence of IBRs. This paper presents a novel algorithm to correctly identify fault direction using negative sequence admittance measurements. The proposed technique is robust and reliable and yields better results in comparison to conversional techniques in presence of IBRs. In addition, its operation is simple and can be conveniently incorporated into a modern digital relay.

The performance of wind turbines directly determines the profitability of wind farms. However, the complex environmental conditions and influences of various uncertain factors make it difficult to accurately assess and monitor the actual power generation performance of wind turbines. A data-driven approach is proposed to intelligently monitor the power generation performance evolution of wind turbines based on operational data. Considering the inherent nonlinearity and structural complexity of wind turbine systems, a data-derived characteristic construction and dimensionality reduction method based on KPCA is adopted as a prerequisite. Additionally, an AdaBoost-enhanced regressor is applied to wind power prediction with adequate inputs, and day-oriented deviation indicators are further constructed for quantifying performance fluctuations. The final validation phase includes two application cases: In the first case, the results show that the proposed method is sensitive enough to capture the early characteristics of blade damage faults. In the second case, an uncertainty error within ±0.5% demonstrates that the proposed method has high-level accuracy in the quantitative assessment of the power performance and good practical effectiveness in real engineering applications.

Earlier, the production of electricity was primarily reliant on non-renewable sources such as coal and diesel. However, these sources have limited availability and will eventually be depleted. Therefore, it is crucial to shift our focus towards renewable sources for electricity generation. Wind energy is considered one of the cleanest and most sustainable forms of renewable energy. The cost and maintenance associated with generating electricity from wind energy are significantly lower compared to other sources. However, the irregular flow of wind energy makes it challenging to directly convert it into electrical form. To address this issue, Wind Energy Conversion Systems (WECS) are required. WECS can be utilized in both gridconnected and stand-alone systems to meet their respective load demands. One of the main concerns with WECS is the mechanical safety and the output power of the system. The inconsistent supply of wind can lead to wear and tear of the turbine blades. To prevent this, a pitch control system is implemented to regulate the blades’ angle. Additionally, the rotational speed of the rotor is affected by improper wind supply, which ultimately impacts the output power. To maximize the power output from the rotor, an MPPT (Maximum Power Point Tracking) control scheme is employed. In this study, a DC load system representing a telecom base station is used as the base system to analyze the control scheme under various wind profiles using MATLAB/SIMULINK.

This work proposes a novel Linear Regression algorithm compared its performance with the K-Nearest Neighbor (KNN) algorithm for improving the accuracy of wind power generation prediction.In this study, two groups were created for the purpose of comparing the effectiveness of the KNN model (group 1) and the Linear Regression model (group 2) in predicting wind energy output. Each group consisted of 10 samples, resulting in a total of 20 samples used for the analysis. The data in this study were collected from an actual wind turbine and include the following factors: wind speed, altitude, humidity, air density, wind direction, and output power. The information was gathered at 10-minute intervals over the course of a year. The dataset was preprocessed, and the mean value of the corresponding variable was used to impute the missing values. Seventy percent of the data was used for training and thirty percent for testing. The training set was used to train the models, whilst the testing set was used to assess the effectiveness of the models. Python’s scikit-learn module was made use for the development of the Linear Regression technique. Based on statistical power (G-power) = 0.8, α = 0.05, CI of 95% confidence interval was also calculated. The observations indicate that the Linear Regression algorithm is more accurate than the KNN technique. The linear regression model achieved an accuracy of 82.15%, whereas the KNN model had a lower accuracy of 79.55% for predicting wind energy output. Additionally, the statistically significance values of the research was determined to be at a p-value of 0.001 (p

This study proposes the Higher Rotational Speed Maintaining (HRSM) control algorithm that is suitable for large wind speed fluctuations. One of the challenges faced by wind generators is operating with significant wind speed oscillations. A wind generator with a large moment of inertia increases the time constant in rotational speed control and delays the tracking of the optimal operating point. This delay could reduce generated power by up to 40% or more. There are numerous regions in Japan’s inland areas where average wind speeds exceed 6.5 m/s. However, frequent fluctuations in wind speeds make wind power generation challenging. Therefore, this paper proposes a control algorithm suitable for wind turbines operating under unstable wind conditions. Higher rotational speeds are required to convert sudden high wind speeds into higher power output, especially when wind speed oscillations are large. Hence, the proposed algorithm focuses on maintaining the rotational speed during periods of unstable wind conditions to improve generation efficiency. The effectiveness of the proposed control method for wind turbines is verified through numerical simulation results. The novel control algorithm aims to promote the spread of wind turbines in areas with unstable wind conditions by overcoming these disadvantages.

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In this paper, Machine Learning (ML) based techniques known as Support Vector Regression (SVR) and Gradient Boosting Regression Trees (GBRT) are utilized for improving the forecast accuracy of short-term wind power production. In addition, data analysis framework is also prepared to visualize the data acquired from the Supervisory Control and Data Acquisition (SCADA) system. Dataset analysis has been carried out in Polar and Cartesian coordinates to identify the produced power and wind relationship. Based upon different statistical indices, the performance of the developed model has been evaluated. In addition, the comparative analysis reveals that GBRT model performance is superior than that of the SVR model with Mean-Square Error (MSE) value of 0.026, Root Mean-Square Error (RMSE) 0.301, Mean Absolute Percentage Error (MAPE) 0.063, Mean Absolute Error (MAE) 0.004 and Coefficient of determination (R2) 0.969. The results obtained illustrate that the proposed model would be applicable in real-life scenarios and help the management team to handle the produced power from wind turbines. [ABSTRACT FROM AUTHOR]

Flux-switching permanent magnet (FSPM) machines have attracted significant research attention in the field of wind power generation. In this study, the utilization of a magnetic flux barrier to improve the performance of the nine-phase FSPM generator designed for low-speed wind power applications is conducted. The proposed approach involves introducing magnetic flux barriers of different topologies to the conventional FSPM generator and analyzing their performance using 2D finite element simulations. Results suggested that |-shaped magnetic flux barriers exhibited the highest performance among other topologies, making them the appropriate choice for this generator. The geometry of the |-shaped flux barriers was further optimized using response surface methodology to maximize the generator's performance. The proposed generator exhibits a significant decrease in cogging torque, achieving a remarkable reduction of up to 23.7%, while maintaining electromotive force. Moreover, it shows a significant decrease in permanent magnet eddy-current loss, with a noteworthy reduction of up to 51%. Additionally, significant improvements were demonstrated in terms of electromagnetic torque, torque ripple, output power, and efficiency. Details on the physical reasoning behind these improvements have been provided. Overall, the proposed FSPM generator with inserted flux barriers has the potential to meet the demands of low-speed wind power generation effectively. [ABSTRACT FROM AUTHOR]

In response to the problem of reduced system inertia and insufficient frequency regulation capacity due to large-scale wind power connection to the grid, an integrated control strategy scheme for wind turbine inertia support and primary frequency regulation is proposed. In this scheme, the load reduction control of wind turbine is adjusted based on the characteristics of wind turbine slurry distance angle, and the reserve capacity required for wind turbine frequency modulation is reserved, which further solves the combination problem between the frequency change of the power grid and the virtual moment of inertia in the load reduction mode. In addition, the wind turbine speed protection is considered to set the static adjustment coefficient, and combined with the inertia support control, to achieve the wind turbine integrated frequency control strategy. The co-simulation model including wind farm is built in MATLAB/Simulink platform. The results show that the wind turbine can quickly provide virtual rotational inertia support to the grid, reduce the rate of change of the initial grid frequency perturbation, and regulate the output power according to the adjusted static regulation differential coefficients to improve the frequency stability of the wind power connected to the grid, which verifies the rationality of the proposed control strategy. [ABSTRACT FROM AUTHOR]

Variability and uncertainty in wind resources pose significant challenges to the expansion planning of wind farms and associated flexible resources. In addition, the spatial smoothing effect, indicating the impact of wind farm scale on aggregated wind power prediction errors, further aggravates the challenge. This paper proposes a chance‐constrained co‐expansion planning method considering the spatial smoothing effect, where the expansion of wind farm capacity, batter energy storage capacity, and power transmission lines are co‐optimized. Specifically, a decision‐dependent uncertainty (DDU) model is established capturing the dependency of wind power uncertainties on wind farm expansion decisions under the spatial smoothing effect. Unlike traditional optimization diagram where decisions are made under only decision‐independent uncertainty (DIU) with fixe properties, properties of decision‐dependent uncertain parameters would be inversely altered by decisions. To effectively tackle the coupling relation between decisions and DDU, DDU‐based chance constraints are formulated in an analytical manner, where the decisions and decision‐dependent uncertain parameters are expressed in a closed form. Eventually, with piecewise linearization of the DDU model and the polynomial approximation of cumulative distribution function of uncertain parameters, the proposed chance‐constrained optimization model with DDU is converted into a mixed‐integer second‐order cone program (MISOCP). Case studies verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]