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Abstract In the context of global energy transformation and sustainable development, integrating and utilizing renewable energy effectively have become the key to the power system advancement. However, the integration of wind and photovoltaic power generation equipment also leads to power fluctuations in the distribution network. The research focuses on the multifaceted challenges of optimizing the operation of distribution networks. It explores the operation and control methods of active distribution networks based on energy storage and reactive power compensation equipment. The stable operation of the distribution network is analyzed under the conditions of wind and photovoltaic integration, with a particular focus on precise regulation to address the limitations of existing methods. Afterwards, the study proposes an improvement plan that combines on load tap changer transformers and reactive power compensation equipment to solve complex power balance problems through second-order cone programming relaxation method. The results of numerical analysis show that the constructed mathematical model maintains a stable voltage of 1 to 1.1 pu at distribution network nodes within 24 h. Especially during peak hours from 15:00 to 24:00, it remains normal without any abnormal fluctuations when the control equipment is not added. These results confirm that precise regulation of multiple devices ensures voltage stability and avoids low or high voltage issues.

In order to improve the anti-interference ability and control accuracy of wind power brake, the improved active disturbance rejection control and Kalman filter algorithm are proposed based on the traditional three-closed-loop system. The traditional "large bandwidth, small error" is improved to "small bandwidth, small error" by introducing the speed tracking condition in the extended state observer to improve the observation efficiency of the observer. A new exponential reaching law sliding mode control is introduced into the error feedback control rate to enhance the control effect. The Kalman filter algorithm is introduced into the current loop to reduce the current noise. The mathematical model of the electric braking system is established by MATLAB/Simulink, and compared with the traditional active disturbance rejection control strategy and PID control strategy. The results showed that the improved control strategy can improve the response speed and reduce the starting torque and current noise. The brake showed good control effect and anti-interference ability under different operating conditions, which enhanced the system's stability and dynamic performance. [ABSTRACT FROM AUTHOR]

In the context of global energy transformation and sustainable development, integrating and utilizing renewable energy effectively have become the key to the power system advancement. However, the integration of wind and photovoltaic power generation equipment also leads to power fluctuations in the distribution network. The research focuses on the multifaceted challenges of optimizing the operation of distribution networks. It explores the operation and control methods of active distribution networks based on energy storage and reactive power compensation equipment. The stable operation of the distribution network is analyzed under the conditions of wind and photovoltaic integration, with a particular focus on precise regulation to address the limitations of existing methods. Afterwards, the study proposes an improvement plan that combines on load tap changer transformers and reactive power compensation equipment to solve complex power balance problems through second-order cone programming relaxation method. The results of numerical analysis show that the constructed mathematical model maintains a stable voltage of 1 to 1.1 pu at distribution network nodes within 24 h. Especially during peak hours from 15:00 to 24:00, it remains normal without any abnormal fluctuations when the control equipment is not added. These results confirm that precise regulation of multiple devices ensures voltage stability and avoids low or high voltage issues. [ABSTRACT FROM AUTHOR]

Copyright of Power Generation Technology is the property of Power Generation Technology 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.)

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. [ABSTRACT FROM AUTHOR]

Wind power generation has been introduced to reduce carbon emissions; however, recycling or recovering the waste of wind blades, which contain fibre-reinforced plastic, is difficult. Converting the recovered materials for secondary use is also difficult owing to the decreased strength and low material value. Many countries, including Japan, have not considered the future energy and CO2 emission scenarios, particularly CO2 emissions from wind blade waste. Based on these scenarios, Japan has planned to introduce large amounts of onshore/offshore wind power generation through 2050. Therefore, we aimed to evaluate quantitatively the total amount of waste and the global warming potential (GWP) from multiple blade waste treatment processes. Based on the average lifetime of blades (20–25 years), we found that the GWP of wind blade waste treatment in Japan may reach a maximum of 197.3–232.4 MtCO2eq by 2060–2065. Based on this lifetime, the wind blade treatment in 2050 accounted for 63.9–80.1% of the total greenhouse gas emissions in 2050. We also showed that the rise in CO2 emissions from the wind blade wastes would make up 82.5–93.6% of the potential reduction in the GWP, which is achievable by shifting from thermal to wind power generation. [ABSTRACT FROM AUTHOR]

The large size of wind turbines and wind farm clustering aggravate the effect of wake on output power, resulting in a reduction in the economic benefits of wind farms. This paper took the actual operating turbines of an onshore wind farm in China as the research object and analyzed the influence of wake on energy efficiency loss by combining SCADA data. The research established a complete loss assessment method and proposed the corresponding evaluation criteria. The results showed that typical wind turbines seriously affected by wake accounted for 32.8% of the wind farm. The actual output power was only 84.2% of the theoretical output power at the lowest month, and the wake loss of the wind farm is serious. The economic efficiency of the wind farm is lower in the summer months (June–August). The study can provide a theoretical basis for the arrangement of wind farms and the development of an operation control strategy. [ABSTRACT FROM AUTHOR]

Power supply from renewable energy is an important part of modern power grids. Robust methods for predicting production are required to balance production and demand to avoid losses. This study proposed an approach that incorporates signal decomposition techniques with Long Short-Term Memory (LSTM) neural networks tuned via a modified metaheuristic algorithm used for wind power generation forecasting. LSTM networks perform notably well when addressing time-series prediction, and further hyperparameter tuning by a modified version of the reptile search algorithm (RSA) can help improve performance. The modified RSA was first evaluated against standard CEC2019 benchmark instances before being applied to the practical challenge. The proposed tuned LSTM model has been tested against two wind production datasets with hourly resolutions. The predictions were executed without and with decomposition for one, two, and three steps ahead. Simulation outcomes have been compared to LSTM networks tuned by other cutting-edge metaheuristics. It was observed that the introduced methodology notably exceed other contenders, as was later confirmed by the statistical analysis. Finally, this study also provides interpretations of the best-performing models on both observed datasets, accompanied by the analysis of the importance and impact each feature has on the predictions. [ABSTRACT FROM AUTHOR]

This paper presents a robust interval economic dispatch (RIED) model for power systems with large-scale wind power integration. Differing from existing interval optimization (IO) approaches that merely rely on the upper and lower boundaries of random variables, the distribution information retained in the historical data is introduced to the IO method in this paper. Based on the available probability distribution function (PDF), wind power curtailment and load shedding are quantified as the operational risk and incorporated into the decision-making process. In this model, we need not rely on the forecasted value of wind power, which is randomly fluctuating and quite unpredictable. Furthermore, when the PDFs of wind power are taken into account, the resulting dispatch solution makes a good tradeoff between the generation cost and the operational risk. Finally, the RIED model yields an optimal dispatch solution for thermal units and the allowable intervals of wind power for the wind farms, which efficiently mitigates the uncertainty in wind power generation and provides more practical suggestions for system operators. Simulation studies are conducted on a modified IEEE-118 bus system and the results verify the effectiveness of the proposed RIED model.

This study addresses the optimal power flow (OPF) problem incorporating renewable energy sources (RES) and flexible alternating current transmission systems (FACTS) using the Chaos Game Optimization (CGO) algorithm. Five objective functions are considered, which include minimizing generation costs, emissions, active power loss, voltage deviation, and enhancing voltage profiles. The OPF formulation considers the anticipated electricity production from wind turbines (WT) and photovoltaic (PV) units as dependent variables, while the voltage magnitude at WT and PV buses is treated as a control variable. Probabilistic models based on wind speed and solar irradiance are used to forecast the electrical output of WT and PV units. The proposed OPF methodology and solution method are validated on the IEEE 30-bus test network. This paper introduces and applies four optimization techniques inspired by biological and natural phenomena, namely CGO, Osprey Optimization Algorithm (OOA), RIME Algorithm, and Slime Mould Algorithm (SMA), to address both single-OPF and multi-OPF objective problems in electric power networks. The suggested optimization approaches are tested under different operational scenarios, considering various combinations of FACTS, renewable energy sources (solar PV and wind), and their locations in the network. To predict wind and solar PV power generation, Weibull and lognormal probability density functions are utilized, respectively. The objective function accounts for reserve cost due to overestimation and penalty cost due to underestimation of intermittent solar and wind power. The results demonstrate that the CGO technique is more efficient than other methods in solving OPF instances.

The utilization of renewable energy in power generation has been increasing in recent years, with the use of wind power sources being the most promising solution for sustainable power generation. The doubly-fed induction generator (DFIG) is one of the most commonly used generators in wind power generation applications, as it offers some technical of advantages. However, the increasing penetration of wind power generation poses tremendous technical challenges in power system operation as this can potentially affect system stability, requiring better control and monitoring schemes. Dynamic state estimation (DSE) offers the ability to achieve this purpose. With respect to this, the present paper proposes a DSE framework on a high-order model of DFIG consisting of 18 states. The method uses the unscented Kalman filter (UKF) which provides an accurate estimate of DFIG states under a strong system non linearity present in the wind turbine system. Furthermore, this paper demonstrates the robustness of the proposed method under different faults and noisy conditions. Finally, the paper also extends the use of UKF to estimate the unknown inputs of a DFIG system, such as control references in the rotor-side converter (RSC) and grid-side converter(GSC).

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.

Constraining the virtual inertia of variable-speed wind turbines within the variable frequency limit time will be the key to improving the wind power’s reliability in actively supporting the system frequency. First, this paper analyzes the controllable characteristics and evaluation methods of the virtual inertia of the wind turbine. Second, it quantifies the variable frequency limit time of the grid with high-proportion wind power and considers the wind turbine speed and power margin to restrict the virtual inertia of the wind turbine during the period of frequency drop or increase to provide reliable inertia support to ensure the safety of the system frequency. On this basis, the inertia demand under the frequency safety of the system was analyzed, and the virtual inertia control strategy of the variable-speed wind turbine in the variable frequency limit time was proposed using the speed tracking performance of the wind turbine. Finally, the grid with a high-proportion wind power simulation system is built to verify that the variable-speed wind turbine can reliably satisfy the inertia demand of system frequency modulation under the proposed control strategy and significantly improve its active support performance to the power grid.

The development of modern society requires not only attention to development methods and efficiency, but also correct evaluation of various environmental issues that arise during development, to achieve optimal social development without damaging the ecological environment. Intelligent power systems utilize new energy technologies, energy storage technologies, and smart grid technologies. Against this development background, how to achieve sustainable, environmentally friendly, and Low Carbon (LC) development in the power system operation, which has been at the center of China's national economic construction and development system, has become a common concern for all participants. Therefore, this paper proposed a study on supporting role of intelligent power systems based on LC and environmental protection. In the new era, the target functions to be achieved by smart grids can provide the necessary support for the construction and development of LC power systems, and its importance is self-evident. Based on this reality, this article took LC power systems as the research object and combined the analysis of the functions of smart grids to discuss clean energy power systems. The experimental results showed that the Power Generation (PG) capacity of thermal PG increased from 280.4 billion kWh to 289.7 billion kWh. The increase in wind PG from 2.5 billion kWh to 5.5 billion kWh showed that although clean energy accounted for a small proportion, the power system was still dominated by traditional PG. However, clean energy PG is increasingly being valued. At the end of this article, corresponding rectification suggestions were also proposed, with the purpose of helping to optimize the development and improvement of LC power systems. [ABSTRACT FROM AUTHOR]

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]

Energy storage systems (ESS) and permanent magnet synchronous generators (PMSG) are speculated to be able to exhibit frequency regulation capabilities by adding differential and proportional control loops with different control objectives. The available PMSG kinetic energy and charging/discharging capacities of the ESS were restricted. To improve the inertia response and frequency control capability, we propose a short-term frequency support strategy for the ESS and PMSG. To this end, the weights were embedded in the control loops to adjust the participation of the differential and proportional controls based on the system frequency excursion. The effectiveness of the proposed control strategy was verified using PSCAD/EMTDC. The simulations revealed that the proposed strategy could improve the maximum rate of change of the frequency nadir and maximum frequency excursion. Therefore, it provides a promising solution of ancillary services for frequency regulation of PMSG and ESS. [ABSTRACT FROM AUTHOR]

This study introduces a novel method to assess solar-wind hybrid energy systems, utilizing data analysis for key metrics (Performance Yields Y R , Capacity Factor CF , and Performance Ratio PR), focused on their uncertainties estimation. Employing a bootstrapped-based K-means algorithm on features from a local weather station in Universidad de La Salle, Bogotá, the approach evaluates the technical feasibility of a photovoltaic system on the campus. It discusses the clustering algorithm advantages, emphasizing its suitability for modeling continuous processes in renewable energy systems. The hypothesis assumes independence and identically distributed centroid coordinates, aligning with large-scale energy system characteristics. Methods include data normalization, feature selection, statistical calculations, bootstrapping, and Probability Density Functions (PDF) estimation through a Maximum Likelihood Estimator (MLE) to assess uncertainties. Results suggest the unfeasibility of a wind energy system due to technological constraints. The study evaluates system flexibility, and design considerations, providing valuable decision-making insights. The conclusion highlights the potential use for accurate estimations, enhancing the system reliability. [ABSTRACT FROM AUTHOR]

High Penetration of renewable energy with conventional power system increases the system instability problem during recent year. Load frequency control (LFC) is a valuable part in controlling the smart power system. In this paper create a model for automatic generation control with wind power system using fuzzy logic controller (FLC) in single area power system. It is very important to control the integral load frequency while generating thermal and wind. In this paper integral controller is used to obtain the value of FLC. All FLC controllers are to be analyzed and ultimately compared with existing controllers. And finally, it is proved that the calculation done on the basis of FLC is much better than the existing control schemes. [ABSTRACT FROM AUTHOR]

The useful wind energy potential in the Sahelian part of Chad is estimated by considering two representative sites namely, Mongo and Abeche from a long series of in situ wind speed measurements over a period of thirty years at 10 m height from the ground. The Weibull distribution statistic was used and its parameters were used to show the influence of turbulence sources on the wind speed at 10 m. Vertical extrapolation of the Weibull parameters and wind speed at different heights from 10 m to 100 m provided more usable wind speeds. The results show that on average the wind is more intense and stable in Mongo than in Abeche and that the wind speed values increase with height with the influence of turbulence sources quantified at about 22.15% in Abeche and 19.93% in Mongo. The comparative study of the five turbines used shows that the De Wind D7 turbines with $0.057/kWh at 70 m in Abeche and Bonus 1MW/54 turbines with $0.067/kWh at 50 m in Mongo are better suited to produce energy according to their production capacity. The minimum and maximum electricity costs per kilowatt hour obtained using these two best turbines are very competitive compared to the cost of electricity in Chad (about $0.16/kWh) and that the installation of a wind farm in these cities could significantly improve the socio-economic situation of households. [ABSTRACT FROM AUTHOR]

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & 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.)

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, 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.

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.

Wind power generation has been introduced to reduce carbon emissions; however, recycling or recovering the waste of wind blades, which contain fibre-reinforced plastic, is difficult. Converting the recovered materials for secondary use is also difficult owing to the decreased strength and low material value. Many countries, including Japan, have not considered the future energy and CO2 emission scenarios, particularly CO2 emissions from wind blade waste. Based on these scenarios, Japan has planned to introduce large amounts of onshore/offshore wind power generation through 2050. Therefore, we aimed to evaluate quantitatively the total amount of waste and the global warming potential (GWP) from multiple blade waste treatment processes. Based on the average lifetime of blades (20–25 years), we found that the GWP of wind blade waste treatment in Japan may reach a maximum of 197.3–232.4 MtCO2eq by 2060–2065. Based on this lifetime, the wind blade treatment in 2050 accounted for 63.9–80.1% of the total greenhouse gas emissions in 2050. We also showed that the rise in CO2 emissions from the wind blade wastes would make up 82.5–93.6% of the potential reduction in the GWP, which is achievable by shifting from thermal to wind power generation.

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]

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]

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]

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]

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]

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]

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.)

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]

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]

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]

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]

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