Pub Date : 2022-12-01DOI: 10.1177/0309524X221114350
Mauro Amaro Pinazo, Ronal Antara Arias, Jorge Luis Mírez Tarrillo
The penetration of large levels of wind energy leads to technical problems affecting the frequency stability of the power system. Naturally, a wind power plant does not provide an inertial response nor does it regulate the system frequency in case of generation-demand imbalance due to the decoupling between the rotor speed and the grid frequency through the electronic converter (AC/DC/AC). This paper reviews, analysis and compares the performance of four control methods based on the modification of the electromagnetic torque set point and one of the mechanical torque. These methods allow that activation of the wind turbine inertia when a frequency deviation occurs, contributing to the regulation of the system frequency similarly to a conventional synchronous generator. However, wind turbines only change their operating point for a limited time and not permanently.
{"title":"Comparison of primary frequency control methods for wind turbines based on the doubly fed induction generator","authors":"Mauro Amaro Pinazo, Ronal Antara Arias, Jorge Luis Mírez Tarrillo","doi":"10.1177/0309524X221114350","DOIUrl":"https://doi.org/10.1177/0309524X221114350","url":null,"abstract":"The penetration of large levels of wind energy leads to technical problems affecting the frequency stability of the power system. Naturally, a wind power plant does not provide an inertial response nor does it regulate the system frequency in case of generation-demand imbalance due to the decoupling between the rotor speed and the grid frequency through the electronic converter (AC/DC/AC). This paper reviews, analysis and compares the performance of four control methods based on the modification of the electromagnetic torque set point and one of the mechanical torque. These methods allow that activation of the wind turbine inertia when a frequency deviation occurs, contributing to the regulation of the system frequency similarly to a conventional synchronous generator. However, wind turbines only change their operating point for a limited time and not permanently.","PeriodicalId":51570,"journal":{"name":"Wind Engineering","volume":"48 1","pages":"1923 - 1947"},"PeriodicalIF":1.5,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81002434","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-01DOI: 10.1177/0309524X221106184
Yanxia Ou, Li Xu, J. Wang, Yang Fu, Yuan Chai
Accurate prediction of offshore wind speed is of great significance for optimizing operation strategies of offshore wind power. Here, a novel hybrid algorithm based on seasonal-trend decomposition with loess (STL) and auto-regressive integrated moving average (ARIMA)- long short-term memory neural network (LSTM) is proposed to eliminate seasonal factors in wind speed and fully exert the advantages of ARIMA processing linear series and LSTM processing nonlinear series. Moreover, wind speed are comprehensively preprocessed and statistically analyzed. Then, we handle information leakage problem. Finally, STL-ARIMA-LSTM model is applied to wind speed forecasting on 3 time-scales. The proposed model has the highest accuracy and resolution for the trend and periodicity of wind speed, and the lag problem of very shortterm wind speed prediction can be solved. This study also shows that when predicting offshore wind speed, we can handle the strong intermittence, volatility and outliers in wind speed by gradually adjusting time scale.
{"title":"A STL decomposition-based deep neural networks for offshore wind speed forecasting","authors":"Yanxia Ou, Li Xu, J. Wang, Yang Fu, Yuan Chai","doi":"10.1177/0309524X221106184","DOIUrl":"https://doi.org/10.1177/0309524X221106184","url":null,"abstract":"Accurate prediction of offshore wind speed is of great significance for optimizing operation strategies of offshore wind power. Here, a novel hybrid algorithm based on seasonal-trend decomposition with loess (STL) and auto-regressive integrated moving average (ARIMA)- long short-term memory neural network (LSTM) is proposed to eliminate seasonal factors in wind speed and fully exert the advantages of ARIMA processing linear series and LSTM processing nonlinear series. Moreover, wind speed are comprehensively preprocessed and statistically analyzed. Then, we handle information leakage problem. Finally, STL-ARIMA-LSTM model is applied to wind speed forecasting on 3 time-scales. The proposed model has the highest accuracy and resolution for the trend and periodicity of wind speed, and the lag problem of very shortterm wind speed prediction can be solved. This study also shows that when predicting offshore wind speed, we can handle the strong intermittence, volatility and outliers in wind speed by gradually adjusting time scale.","PeriodicalId":51570,"journal":{"name":"Wind Engineering","volume":"2013 1","pages":"1753 - 1774"},"PeriodicalIF":1.5,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86202468","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-22DOI: 10.1177/0309524X221136543
S. Tounsi
This paper concern the design and control of a wind energy conversion system regulating generator phase’s currents, voltages, and batteries charging voltage using barking systems and AC–DC converter. Indeed, the braking system permit the regulation of the generator’s angular speed and electromotives forces magnitude, and the AC–DC converter permit the regulation of the phase’s voltages, currents, and the batteries charging voltage. This method is suitable for permanent magnet axial flux synchronous generators and for Insulated Gate Bipolar Transistor converters (IGBT). In addition, we propose an innovative strategy to push the problem of adding an impedance matching transformer to minimize the over-current effect caused by the sudden variation of the voltage at the generator inductor using the AC–DC converter. It offers the advantage of power chain cost reduction and the improvement of its performances. The overall model of the power chain is implemented under the simulation environment MATLAB-Sumilink for performances analysis of studied structure.
{"title":"Design and simulation of wind energy conversion system commanded by converter and mechanical brake","authors":"S. Tounsi","doi":"10.1177/0309524X221136543","DOIUrl":"https://doi.org/10.1177/0309524X221136543","url":null,"abstract":"This paper concern the design and control of a wind energy conversion system regulating generator phase’s currents, voltages, and batteries charging voltage using barking systems and AC–DC converter. Indeed, the braking system permit the regulation of the generator’s angular speed and electromotives forces magnitude, and the AC–DC converter permit the regulation of the phase’s voltages, currents, and the batteries charging voltage. This method is suitable for permanent magnet axial flux synchronous generators and for Insulated Gate Bipolar Transistor converters (IGBT). In addition, we propose an innovative strategy to push the problem of adding an impedance matching transformer to minimize the over-current effect caused by the sudden variation of the voltage at the generator inductor using the AC–DC converter. It offers the advantage of power chain cost reduction and the improvement of its performances. The overall model of the power chain is implemented under the simulation environment MATLAB-Sumilink for performances analysis of studied structure.","PeriodicalId":51570,"journal":{"name":"Wind Engineering","volume":"110 1","pages":"564 - 578"},"PeriodicalIF":1.5,"publicationDate":"2022-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74352681","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-22DOI: 10.1177/0309524X221131999
T. Manzoor, G. A. Gohar, Asim Asghar, S. Manzoor
In this work, static and dynamic analyses are carried out on VAWT blade using analytical and numerical methods to figure out reasons behind the failure in the blade for off-shore applications arising due to turbulence of wind. The design flaws and different operating conditions play major role in failure of blades. Fatigue life cycles, natural frequencies of blade in different modes and turbine harmonic frequency have been calculated analytically using Goodman’s and vibration analysis theories respectively. In analysis, life cycles, natural frequencies and mode shapes for VAWT blade are studied. Analytical and numerical results have been compared, life cycles and frequencies are determined. The numerical results showed good agreement with theoretical concepts.
{"title":"Analysis of vertical axis wind turbine blade for off-shore applications","authors":"T. Manzoor, G. A. Gohar, Asim Asghar, S. Manzoor","doi":"10.1177/0309524X221131999","DOIUrl":"https://doi.org/10.1177/0309524X221131999","url":null,"abstract":"In this work, static and dynamic analyses are carried out on VAWT blade using analytical and numerical methods to figure out reasons behind the failure in the blade for off-shore applications arising due to turbulence of wind. The design flaws and different operating conditions play major role in failure of blades. Fatigue life cycles, natural frequencies of blade in different modes and turbine harmonic frequency have been calculated analytically using Goodman’s and vibration analysis theories respectively. In analysis, life cycles, natural frequencies and mode shapes for VAWT blade are studied. Analytical and numerical results have been compared, life cycles and frequencies are determined. The numerical results showed good agreement with theoretical concepts.","PeriodicalId":51570,"journal":{"name":"Wind Engineering","volume":"35 1","pages":"499 - 514"},"PeriodicalIF":1.5,"publicationDate":"2022-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81577135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-22DOI: 10.1177/0309524X221133791
Amita Singh, Veena Sharma, Vineet Kumar, R. Naresh, O. P. Rahi
This manuscript analyses a case study of a wind-integrated power plant where an Arithmetic Optimization Algorithm-based Model Predictive Controller (AOA-MPC) has been employed for the combined control of voltage and frequency. The work in this manuscript considers stochastic variations in wind output caused by the small stochastic drifts and sudden deterministic shifts in the wind turbine output. The proposed controller’s performance has been judged after assessing the time response performance specifications/indices and comparing it with the existing recent methodologies available in the literature. Further improvement in frequency oscillations reduction has been obtained while considering the Redox Flow Battery (RFB) loop as an auxiliary Load Frequency Control loop. Moreover, the computational potential of the presented algorithm has been tested under different nonlinearities and time delay cases in the Area Control Error (ACE) of the load frequency control (LFC) loop.
{"title":"AOA based optimal control of combined AVR-LFC model in wind integrated power system","authors":"Amita Singh, Veena Sharma, Vineet Kumar, R. Naresh, O. P. Rahi","doi":"10.1177/0309524X221133791","DOIUrl":"https://doi.org/10.1177/0309524X221133791","url":null,"abstract":"This manuscript analyses a case study of a wind-integrated power plant where an Arithmetic Optimization Algorithm-based Model Predictive Controller (AOA-MPC) has been employed for the combined control of voltage and frequency. The work in this manuscript considers stochastic variations in wind output caused by the small stochastic drifts and sudden deterministic shifts in the wind turbine output. The proposed controller’s performance has been judged after assessing the time response performance specifications/indices and comparing it with the existing recent methodologies available in the literature. Further improvement in frequency oscillations reduction has been obtained while considering the Redox Flow Battery (RFB) loop as an auxiliary Load Frequency Control loop. Moreover, the computational potential of the presented algorithm has been tested under different nonlinearities and time delay cases in the Area Control Error (ACE) of the load frequency control (LFC) loop.","PeriodicalId":51570,"journal":{"name":"Wind Engineering","volume":"512 1","pages":"515 - 527"},"PeriodicalIF":1.5,"publicationDate":"2022-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77847778","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-08DOI: 10.1177/0309524X221133810
A. Yahiaoui, A. Tlemçani
Optimization is one of the most important branches of applied mathematics and much research both practical and theoretical has been devoted to it. In this in this paper we have used the software HOMER to solve optimization problem on optimal sizing of three hybrid renewable energy systems involving of PV panel, wind turbine, battery bank, electrolyzer, H2 tank, and fuel cell for possible installation in Timimoun city in Algeria desert. The proposed systems are applied for optimal configuration, minimization of the total net present cost (TNPC) and cost of energy (COE). The study showed us that each system provides electrical power for this region, TNPC of the PV/Wind/Battery system is 22,621,932 $ with COE of 1.673 $/kWh, it is the most expensive system that we cannot adopt because of its high cost. The PV/Wind/Electrolyzer/H2 tank/Fuel cell system is cheaper than the first in terms of TNPC which equal 14,945,818 $ with COE of 1.105 $/kWh. This system is also undesirable because it is more expensive than the PV/Wind/Battery/Electrolyzer/H2 tank/Fuel cell hybrid system. The TNPC of this system is 12,322,474 $ with COE of 0.912 $/kWh, which makes it cheaper than the first two systems. The results prove that the hybrid PV/Wind/Battery/Electrolyzer/H2 tank/Fuel cell system meet the electrical energy need of the region.
{"title":"A comparison study of HRES for electrification of a rural city in Algeria","authors":"A. Yahiaoui, A. Tlemçani","doi":"10.1177/0309524X221133810","DOIUrl":"https://doi.org/10.1177/0309524X221133810","url":null,"abstract":"Optimization is one of the most important branches of applied mathematics and much research both practical and theoretical has been devoted to it. In this in this paper we have used the software HOMER to solve optimization problem on optimal sizing of three hybrid renewable energy systems involving of PV panel, wind turbine, battery bank, electrolyzer, H2 tank, and fuel cell for possible installation in Timimoun city in Algeria desert. The proposed systems are applied for optimal configuration, minimization of the total net present cost (TNPC) and cost of energy (COE). The study showed us that each system provides electrical power for this region, TNPC of the PV/Wind/Battery system is 22,621,932 $ with COE of 1.673 $/kWh, it is the most expensive system that we cannot adopt because of its high cost. The PV/Wind/Electrolyzer/H2 tank/Fuel cell system is cheaper than the first in terms of TNPC which equal 14,945,818 $ with COE of 1.105 $/kWh. This system is also undesirable because it is more expensive than the PV/Wind/Battery/Electrolyzer/H2 tank/Fuel cell hybrid system. The TNPC of this system is 12,322,474 $ with COE of 0.912 $/kWh, which makes it cheaper than the first two systems. The results prove that the hybrid PV/Wind/Battery/Electrolyzer/H2 tank/Fuel cell system meet the electrical energy need of the region.","PeriodicalId":51570,"journal":{"name":"Wind Engineering","volume":"60 1","pages":"528 - 545"},"PeriodicalIF":1.5,"publicationDate":"2022-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86114456","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-03DOI: 10.1177/0309524X221126742
Brajlata Chauhan, Rashida Tabassum, S. Tomar, A. Pal
This work focused on the prediction of generation of renewable energy (solar and wind) using the machine learning ML algorithms. Prediction of generation are very important to design the better microgrids storage. The various ML algorithms are as logistic regression LR and random forest RA and the ARIMA, time series algorithms. The performance of each algorithm is evaluated using the mean absolute error, mean squared error, root mean squared error, and mean absolute percentage error. The MAE value for the ARIMA (0.06 and 0.20) model for solar and wind energy is very less as compared to RF (15.65 and 61.73) and LR (15.78 and 54.65) of solar and wind energy. Same with MSE and RMSE, the MSE and RMSE value for the ARIMA of solar energy model obtained is 0.01 and 0.08 and wind energy is 0.07 and 0.27 respectively. Comparative analysis of all of these matrices of each algorithm for both the dataset, we concluded that the ARIMA model is best fit for the forecasting of solar energy and wind energy.
{"title":"Analysis for the prediction of solar and wind generation in India using ARIMA, linear regression and random forest algorithms","authors":"Brajlata Chauhan, Rashida Tabassum, S. Tomar, A. Pal","doi":"10.1177/0309524X221126742","DOIUrl":"https://doi.org/10.1177/0309524X221126742","url":null,"abstract":"This work focused on the prediction of generation of renewable energy (solar and wind) using the machine learning ML algorithms. Prediction of generation are very important to design the better microgrids storage. The various ML algorithms are as logistic regression LR and random forest RA and the ARIMA, time series algorithms. The performance of each algorithm is evaluated using the mean absolute error, mean squared error, root mean squared error, and mean absolute percentage error. The MAE value for the ARIMA (0.06 and 0.20) model for solar and wind energy is very less as compared to RF (15.65 and 61.73) and LR (15.78 and 54.65) of solar and wind energy. Same with MSE and RMSE, the MSE and RMSE value for the ARIMA of solar energy model obtained is 0.01 and 0.08 and wind energy is 0.07 and 0.27 respectively. Comparative analysis of all of these matrices of each algorithm for both the dataset, we concluded that the ARIMA model is best fit for the forecasting of solar energy and wind energy.","PeriodicalId":51570,"journal":{"name":"Wind Engineering","volume":"1 1","pages":"251 - 265"},"PeriodicalIF":1.5,"publicationDate":"2022-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87950563","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
To verify the effectiveness of the GPU simulation of wake effects at a large-scale offshore wind farm, we ran an in-house large-eddy simulation (LES) solver with a CFD porous disk wake model for the Horns Rev 1 wind farm. For this numerical research, we prepared the latest workstation equipped with a Xeon W-2265 CPU and an NVIDIA RTX A6000 GPU. We clarified that the calculation speed of the single GPU of the NVIDIA RTX A6000 is approximately 10 times faster than the calculation speed of the Xeon W-2265. Careful data analysis and visualization of the unsteady turbulent flow fields obtained in the current LES study suggest that the mutual interference of the wakes developed by wind turbines may frequently form a local speed-up region around wind turbines, located on the downstream side of large offshore wind farms.
{"title":"GPU simulation of wake effects at the Horns Rev 1 offshore wind farm using the CFD porous disk wake model","authors":"T. Uchida, Teppei Tanaka, Ryuta Shizui, Hiroto Ichikawa, Ryo Takayama, Kazuomi Yahagi, Ryoya Okubo","doi":"10.1177/0309524X221132003","DOIUrl":"https://doi.org/10.1177/0309524X221132003","url":null,"abstract":"To verify the effectiveness of the GPU simulation of wake effects at a large-scale offshore wind farm, we ran an in-house large-eddy simulation (LES) solver with a CFD porous disk wake model for the Horns Rev 1 wind farm. For this numerical research, we prepared the latest workstation equipped with a Xeon W-2265 CPU and an NVIDIA RTX A6000 GPU. We clarified that the calculation speed of the single GPU of the NVIDIA RTX A6000 is approximately 10 times faster than the calculation speed of the Xeon W-2265. Careful data analysis and visualization of the unsteady turbulent flow fields obtained in the current LES study suggest that the mutual interference of the wakes developed by wind turbines may frequently form a local speed-up region around wind turbines, located on the downstream side of large offshore wind farms.","PeriodicalId":51570,"journal":{"name":"Wind Engineering","volume":"1 1","pages":"408 - 421"},"PeriodicalIF":1.5,"publicationDate":"2022-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89576786","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-31DOI: 10.1177/0309524X221130723
Mohammed Beghdadi, K. Kouzi
This paper proposes a novel fully sensorless synergetic algorithm that adopts concerns from both power, torque, and speed of brushless doubly fed induction machine (BDFIM) for application in wind energy conversion system (WECS). A sensorless fuzzy-based hills climb search algorithm (HCS-MPPT) is introduced to the control system along with a comparison study with the conventional tip speed ratio algorithm (TSR-MPPT). For studying the feasibility of the suggested control, a robust control of the BDFIM based on synergetic control theory is build up for the first time ever on this machine type with different scenarios where the active and reactive power, the torque, and speed of the machine are controlled. In the second step, the aim is maximizing the wind’s energy extraction by replacing the wind speed sensors with a fuzzy-based HCS-MPPT approach. Lastly, to increase the robustness of the suggested scheme control, an extended Kalman filter EKF is employed for the estimation of rotor speed in presence of considerable noise values in order to make it closer to reality as possible. Computational simulation results confirm that the proposed method, consistently outperforms other techniques and proves effectiveness under several conditions.
{"title":"Novel fully sensorless synergetic control of brushless doubly fed induction machine integrated in wind energy conversion system driven by fuzzy-based HCS MPPT algorithm","authors":"Mohammed Beghdadi, K. Kouzi","doi":"10.1177/0309524X221130723","DOIUrl":"https://doi.org/10.1177/0309524X221130723","url":null,"abstract":"This paper proposes a novel fully sensorless synergetic algorithm that adopts concerns from both power, torque, and speed of brushless doubly fed induction machine (BDFIM) for application in wind energy conversion system (WECS). A sensorless fuzzy-based hills climb search algorithm (HCS-MPPT) is introduced to the control system along with a comparison study with the conventional tip speed ratio algorithm (TSR-MPPT). For studying the feasibility of the suggested control, a robust control of the BDFIM based on synergetic control theory is build up for the first time ever on this machine type with different scenarios where the active and reactive power, the torque, and speed of the machine are controlled. In the second step, the aim is maximizing the wind’s energy extraction by replacing the wind speed sensors with a fuzzy-based HCS-MPPT approach. Lastly, to increase the robustness of the suggested scheme control, an extended Kalman filter EKF is employed for the estimation of rotor speed in presence of considerable noise values in order to make it closer to reality as possible. Computational simulation results confirm that the proposed method, consistently outperforms other techniques and proves effectiveness under several conditions.","PeriodicalId":51570,"journal":{"name":"Wind Engineering","volume":"51 2 1","pages":"385 - 407"},"PeriodicalIF":1.5,"publicationDate":"2022-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79461435","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-31DOI: 10.1177/0309524X221118684
N. Kumar, O. Prakash
Micro wind turbine is a technology that is used to generate electricity in urban areas. The purpose of this review paper is an analysis of micro wind turbines on high-rise buildings. High-rise buildings are used as towers for micro wind turbines. Here is an analysis of the wind map of India for installing wind turbines. With the help of wind maps, the behavior of wind at different heights has been studied. The result of this study is to analyze how much wind power is generated in different states of India. At present nine states of India, wind energy is produced for commercial use and connected to the grid. Tamil Nadu is the largest wind power producer state. The wind flow in nine states is very good for wind power generation. Micro wind turbines have also been installed in these states and wind power generation for domestic use yields good results.
{"title":"Analysis of wind energy resources from high rise building for micro wind turbine: A review","authors":"N. Kumar, O. Prakash","doi":"10.1177/0309524X221118684","DOIUrl":"https://doi.org/10.1177/0309524X221118684","url":null,"abstract":"Micro wind turbine is a technology that is used to generate electricity in urban areas. The purpose of this review paper is an analysis of micro wind turbines on high-rise buildings. High-rise buildings are used as towers for micro wind turbines. Here is an analysis of the wind map of India for installing wind turbines. With the help of wind maps, the behavior of wind at different heights has been studied. The result of this study is to analyze how much wind power is generated in different states of India. At present nine states of India, wind energy is produced for commercial use and connected to the grid. Tamil Nadu is the largest wind power producer state. The wind flow in nine states is very good for wind power generation. Micro wind turbines have also been installed in these states and wind power generation for domestic use yields good results.","PeriodicalId":51570,"journal":{"name":"Wind Engineering","volume":"94 1","pages":"190 - 219"},"PeriodicalIF":1.5,"publicationDate":"2022-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91121262","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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