Pub Date : 2022-10-01DOI: 10.1177/0309524X221093531
R. Vijayapriya, P. Raja, M. P. Selvan
This paper presents a direct analytical method to predetermine the steady-state values of a permanent magnet synchronous generator (PMSG) based wind power system (WPS) at each stage of power flow. A generalized structured is developed with two independent equivalent circuits, that is, PMSG and grid side. To effectively determine the converters performance numerals despite grid disturbances, steady-state model is structured with positive sequence components of grid voltage. The advantage of the proposed model is that the methods evade the requirements of d-q modeling and a dedicated controller to evaluate the system performance. Using the proposed steady-state model, the entire WPS components ratings is predicted evading time domain simulation with complicated controller design. Also, the simple controller design is proposed to aid in optimal power flow supplement with FRT requirements under all possible system operating conditions. Ultimately, validation of predetermined values with the simulated PSCAD/EMTDC response including the proposed controller is investigated.
{"title":"A direct analytical predetermination of PMSG based WPS steady-state values under different operating conditions","authors":"R. Vijayapriya, P. Raja, M. P. Selvan","doi":"10.1177/0309524X221093531","DOIUrl":"https://doi.org/10.1177/0309524X221093531","url":null,"abstract":"This paper presents a direct analytical method to predetermine the steady-state values of a permanent magnet synchronous generator (PMSG) based wind power system (WPS) at each stage of power flow. A generalized structured is developed with two independent equivalent circuits, that is, PMSG and grid side. To effectively determine the converters performance numerals despite grid disturbances, steady-state model is structured with positive sequence components of grid voltage. The advantage of the proposed model is that the methods evade the requirements of d-q modeling and a dedicated controller to evaluate the system performance. Using the proposed steady-state model, the entire WPS components ratings is predicted evading time domain simulation with complicated controller design. Also, the simple controller design is proposed to aid in optimal power flow supplement with FRT requirements under all possible system operating conditions. Ultimately, validation of predetermined values with the simulated PSCAD/EMTDC response including the proposed controller is investigated.","PeriodicalId":51570,"journal":{"name":"Wind Engineering","volume":"76 1","pages":"1570 - 1589"},"PeriodicalIF":1.5,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86824904","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 : 2022-09-21DOI: 10.1177/0309524X221123968
Abdelrahman Amin, A. Bibo, Meghashyam Panyam, Phanindra Tallapragada
To reduce wind turbine operations and maintenance costs, we present a machine learning framework for early damage detection in gearboxes based on the cyclostationary and kurtogram analysis of sensor data. The application focus is fault diagnostics in gearboxes under varying load conditions, particularly turbulent wind. Faults in the gearbox rotating components can leave their signatures in vibrations signals measured by accelerometers. We analyze data stemming from a simulated vibration response of a 5 MW multibody wind turbine model in a healthy and damaged scenarios and under different wind conditions. With cyclostationary and kurtogram analysis applied on acquired sensor data, we generate two types of 2D maps that highlight signatures related to the fault damage. Using these maps, convolutional neural networks are trained to identify faults, including those of small magnitude, in test data with a high accuracy. Benchmark test cases inspired by an NREL study are tested and faults successfully detected.
{"title":"Vibration based fault diagnostics in a wind turbine planetary gearbox using machine learning","authors":"Abdelrahman Amin, A. Bibo, Meghashyam Panyam, Phanindra Tallapragada","doi":"10.1177/0309524X221123968","DOIUrl":"https://doi.org/10.1177/0309524X221123968","url":null,"abstract":"To reduce wind turbine operations and maintenance costs, we present a machine learning framework for early damage detection in gearboxes based on the cyclostationary and kurtogram analysis of sensor data. The application focus is fault diagnostics in gearboxes under varying load conditions, particularly turbulent wind. Faults in the gearbox rotating components can leave their signatures in vibrations signals measured by accelerometers. We analyze data stemming from a simulated vibration response of a 5 MW multibody wind turbine model in a healthy and damaged scenarios and under different wind conditions. With cyclostationary and kurtogram analysis applied on acquired sensor data, we generate two types of 2D maps that highlight signatures related to the fault damage. Using these maps, convolutional neural networks are trained to identify faults, including those of small magnitude, in test data with a high accuracy. Benchmark test cases inspired by an NREL study are tested and faults successfully detected.","PeriodicalId":51570,"journal":{"name":"Wind Engineering","volume":"9 1","pages":"175 - 189"},"PeriodicalIF":1.5,"publicationDate":"2022-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77115968","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 : 2022-09-19DOI: 10.1177/0309524X221122612
Yingxue Chen, M. Shaheed, R. Vepa
A common strategy in controlling a permanent magnet synchronous generator (PMSG) driven by a wind turbine is the maximization of output power of the wind turbine itself. A control strategy must be adopted, is to deliver a desired reduced amount of power whenever it is required. In order to realize the direct control of wind turbine output power across a wide range of wind speeds, a linearized parameter varying dynamic model of the nonlinear wind turbine system including wind disturbances is developed and used in this paper. The stability of the wind turbine system is analyzed and a blade pitch controller is designed, based on the linearized, parameter-varying, model-predictive control and is validated. Thus, the wind turbine is regulated in a way that the generator delivers the demanded power output to the load. Moreover, the blade pitch control system also performs the key function of augmenting the stability of the wind turbine, for the right choice of the gains.
{"title":"Active blade pitch control and stabilization of a wind turbine driven PMSG for power output regulation","authors":"Yingxue Chen, M. Shaheed, R. Vepa","doi":"10.1177/0309524X221122612","DOIUrl":"https://doi.org/10.1177/0309524X221122612","url":null,"abstract":"A common strategy in controlling a permanent magnet synchronous generator (PMSG) driven by a wind turbine is the maximization of output power of the wind turbine itself. A control strategy must be adopted, is to deliver a desired reduced amount of power whenever it is required. In order to realize the direct control of wind turbine output power across a wide range of wind speeds, a linearized parameter varying dynamic model of the nonlinear wind turbine system including wind disturbances is developed and used in this paper. The stability of the wind turbine system is analyzed and a blade pitch controller is designed, based on the linearized, parameter-varying, model-predictive control and is validated. Thus, the wind turbine is regulated in a way that the generator delivers the demanded power output to the load. Moreover, the blade pitch control system also performs the key function of augmenting the stability of the wind turbine, for the right choice of the gains.","PeriodicalId":51570,"journal":{"name":"Wind Engineering","volume":"20 1","pages":"126 - 140"},"PeriodicalIF":1.5,"publicationDate":"2022-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78747674","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 : 2022-09-19DOI: 10.1177/0309524X221124031
Ravi Kumar Pandit, D. Astolfi, Jiarong Hong, D. Infield, Matilde Santos
This paper reviews the recent advancement made in data-driven technologies based on SCADA data for improving wind turbines’ operation and maintenance activities (e.g. condition monitoring, decision support, critical components failure detections) and the challenges associated with them. Machine learning techniques applied to wind turbines’ operation and maintenance (O&M) are reviewed. The data sources, feature engineering and model selection (classification, regression) and validation are all used to categorise these data-driven models. Our findings suggest that (a) most models use 10-minute mean SCADA data, though the use of high-resolution data has shown greater advantages as compared to 10-minute mean value but comes with high computational challenges. (b) Most of SCADA data are confidential and not available in the public domain which slows down technological advancements. (c) These datasets are used for both, the classification and regression of wind turbines but are used in classification extensively. And, (d) most commonly used data-driven models are neural networks, support vector machines, probabilistic models and decision trees and each of these models has its own merits and demerits. We conclude the paper by discussing the potential areas where SCADA data-based data-driven methodologies could be used in future wind energy research.
{"title":"SCADA data for wind turbine data-driven condition/performance monitoring: A review on state-of-art, challenges and future trends","authors":"Ravi Kumar Pandit, D. Astolfi, Jiarong Hong, D. Infield, Matilde Santos","doi":"10.1177/0309524X221124031","DOIUrl":"https://doi.org/10.1177/0309524X221124031","url":null,"abstract":"This paper reviews the recent advancement made in data-driven technologies based on SCADA data for improving wind turbines’ operation and maintenance activities (e.g. condition monitoring, decision support, critical components failure detections) and the challenges associated with them. Machine learning techniques applied to wind turbines’ operation and maintenance (O&M) are reviewed. The data sources, feature engineering and model selection (classification, regression) and validation are all used to categorise these data-driven models. Our findings suggest that (a) most models use 10-minute mean SCADA data, though the use of high-resolution data has shown greater advantages as compared to 10-minute mean value but comes with high computational challenges. (b) Most of SCADA data are confidential and not available in the public domain which slows down technological advancements. (c) These datasets are used for both, the classification and regression of wind turbines but are used in classification extensively. And, (d) most commonly used data-driven models are neural networks, support vector machines, probabilistic models and decision trees and each of these models has its own merits and demerits. We conclude the paper by discussing the potential areas where SCADA data-based data-driven methodologies could be used in future wind energy research.","PeriodicalId":51570,"journal":{"name":"Wind Engineering","volume":"31 1","pages":"422 - 441"},"PeriodicalIF":1.5,"publicationDate":"2022-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76921775","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 : 2022-09-19DOI: 10.1177/0309524X221122512
H. Jenkal, M. Lamnadi, Sara Mensou, B. Bossoufi, A. Boulezhar
This paper presents a modeling and robust control of the DFIG (doubly fed induction generator) used in the wind energy conversion system (WECS). We started by using the MPPT method to extract the maximum power in the WECS, modeling the double-fed inductor generator, and then applying the Backstepping controller to control the reactive power and electromagnetic torque in order to test the performance and the robustness of the system. All is simulated and presented in MATLAB/SIMULINK software.
{"title":"A robust control for a variable wind speed conversion system energy based on a DFIG using Backstepping","authors":"H. Jenkal, M. Lamnadi, Sara Mensou, B. Bossoufi, A. Boulezhar","doi":"10.1177/0309524X221122512","DOIUrl":"https://doi.org/10.1177/0309524X221122512","url":null,"abstract":"This paper presents a modeling and robust control of the DFIG (doubly fed induction generator) used in the wind energy conversion system (WECS). We started by using the MPPT method to extract the maximum power in the WECS, modeling the double-fed inductor generator, and then applying the Backstepping controller to control the reactive power and electromagnetic torque in order to test the performance and the robustness of the system. All is simulated and presented in MATLAB/SIMULINK software.","PeriodicalId":51570,"journal":{"name":"Wind Engineering","volume":"53 1","pages":"141 - 156"},"PeriodicalIF":1.5,"publicationDate":"2022-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78568718","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 : 2022-09-19DOI: 10.1177/0309524X221123324
L-C. Forcier, S. Joncas
A method for structural analysis of thin-walled composite beams like wind turbine blades is presented. This method is based on the Nonhomogeneous Anisotropic Beam Section Analysis (NABSA) which consists in discretizing the beam cross section using finite elements. The proposed implementation uses 3-node line cross-sectional finite elements with nodes having rotational degrees of freedom to describe the cross-sectional warping displacements. Solutions obtained using this approach were verified against the corresponding analytical or numerical solutions. Agreement was very good to excellent for the computation of cross-sectional properties and distribution of stresses, strains and warping displacements for a broad range of possible composite beam behaviors including geometric and material couplings, open sections, multicell sections, and arbitrary laminates. For thin-walled layered structures, the proposed method provides models with fewer degrees of freedom than equivalent models based on a two-dimensional discretization of cross sections using triangular or quadrilateral elements such as conventional NABSA or VABS which suggests that computation time could be reduced.
{"title":"Development of a cross-sectional finite element for the analysis of thin-walled composite beams like wind turbine blades","authors":"L-C. Forcier, S. Joncas","doi":"10.1177/0309524X221123324","DOIUrl":"https://doi.org/10.1177/0309524X221123324","url":null,"abstract":"A method for structural analysis of thin-walled composite beams like wind turbine blades is presented. This method is based on the Nonhomogeneous Anisotropic Beam Section Analysis (NABSA) which consists in discretizing the beam cross section using finite elements. The proposed implementation uses 3-node line cross-sectional finite elements with nodes having rotational degrees of freedom to describe the cross-sectional warping displacements. Solutions obtained using this approach were verified against the corresponding analytical or numerical solutions. Agreement was very good to excellent for the computation of cross-sectional properties and distribution of stresses, strains and warping displacements for a broad range of possible composite beam behaviors including geometric and material couplings, open sections, multicell sections, and arbitrary laminates. For thin-walled layered structures, the proposed method provides models with fewer degrees of freedom than equivalent models based on a two-dimensional discretization of cross sections using triangular or quadrilateral elements such as conventional NABSA or VABS which suggests that computation time could be reduced.","PeriodicalId":51570,"journal":{"name":"Wind Engineering","volume":"36 1","pages":"157 - 174"},"PeriodicalIF":1.5,"publicationDate":"2022-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77748599","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 : 2022-09-19DOI: 10.1177/0309524X221124335
Ouassima Boqtob, Hassan El Moussaoui, H. El Markhi, T. Lamhamdi
The combination of demand response as demand side management together with energy management system has become essential to minimize energy cost, to maintain continuous supply of electricity, and to improve the safety of power system operation. This paper studies the optimal energy dispatch of connected microgrid units containing photovoltaic panels, wind turbine generators, diesel generators, and the main grid. The optimal set point of microgrid’s units is determined to satisfy the required load demand for a day-ahead horizon time. As the demand response is an important way of demand side management, this paper proposes as the main contribution the implementation of demand response cost as one of the objective functions to be maximized to view its effect on load demand consumption, on MG energy production and on MG energy cost. The demand response is implemented by using an incentive based demand response program in the optimization model in addition to the fuel cost of diesel generators and the transfer cost of transferable power. The incentive payment offered by utilities is used to motivate consumers to change their energy consumption behavior and thus to reduce their power consumption and maintain the system reliability during on-peak periods. Thus the objective function is formulated to maximize microgrid operator’s demand response benefit, and to minimize both the fuel cost of diesel generators, and the transfer cost of transferable power. For this purpose, the defined objective function is solved by a Hybrid Particle Swarm Optimization with Sine Cosine Acceleration Coefficients (H-PSO-SCAC) algorithm for an optimal energy management system of the connected microgrid. For the simulation tests, different algorithms are examined in order to validate the effectiveness of the H-PSO-SCAC algorithm. The impact of demand response program is analyzed on the load demand consumption, on the microgrid energy production and its influence on the optimized microgrid cost function. The results demonstrate that the implementation of demand response has changed the previous situation that costumers do not participate in the operation of the power system. And it enables microgrid to decrease load consumption, microgrid energy production, as well as energy cost.
{"title":"Optimal energy management of microgrid based wind/PV/diesel with integration of incentive-based demand response program","authors":"Ouassima Boqtob, Hassan El Moussaoui, H. El Markhi, T. Lamhamdi","doi":"10.1177/0309524X221124335","DOIUrl":"https://doi.org/10.1177/0309524X221124335","url":null,"abstract":"The combination of demand response as demand side management together with energy management system has become essential to minimize energy cost, to maintain continuous supply of electricity, and to improve the safety of power system operation. This paper studies the optimal energy dispatch of connected microgrid units containing photovoltaic panels, wind turbine generators, diesel generators, and the main grid. The optimal set point of microgrid’s units is determined to satisfy the required load demand for a day-ahead horizon time. As the demand response is an important way of demand side management, this paper proposes as the main contribution the implementation of demand response cost as one of the objective functions to be maximized to view its effect on load demand consumption, on MG energy production and on MG energy cost. The demand response is implemented by using an incentive based demand response program in the optimization model in addition to the fuel cost of diesel generators and the transfer cost of transferable power. The incentive payment offered by utilities is used to motivate consumers to change their energy consumption behavior and thus to reduce their power consumption and maintain the system reliability during on-peak periods. Thus the objective function is formulated to maximize microgrid operator’s demand response benefit, and to minimize both the fuel cost of diesel generators, and the transfer cost of transferable power. For this purpose, the defined objective function is solved by a Hybrid Particle Swarm Optimization with Sine Cosine Acceleration Coefficients (H-PSO-SCAC) algorithm for an optimal energy management system of the connected microgrid. For the simulation tests, different algorithms are examined in order to validate the effectiveness of the H-PSO-SCAC algorithm. The impact of demand response program is analyzed on the load demand consumption, on the microgrid energy production and its influence on the optimized microgrid cost function. The results demonstrate that the implementation of demand response has changed the previous situation that costumers do not participate in the operation of the power system. And it enables microgrid to decrease load consumption, microgrid energy production, as well as energy cost.","PeriodicalId":51570,"journal":{"name":"Wind Engineering","volume":"54 1","pages":"266 - 282"},"PeriodicalIF":1.5,"publicationDate":"2022-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79762197","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 : 2022-09-14DOI: 10.1177/0309524X221122531
L. Saihi, B. Berbaoui, Larbi Djilali, Mohammed Boura
The current study presents a robust sensorless control using passivity based control (PBC) combined with high gain observer (HGO). The proposed controller is applied to control the generated doubly-fed induction generator (DFIG) active and reactive power installed on a variable speed wind energy conversion system. The control objective is used to regulate independently the DFIG stator active and reactive power, which are decoupled by using the field oriented control technique. Additionally, this process leads to reduce the cost of control scheme by eliminating the speed sensor. Firstly, the DFIG is modeled under the port controlled Hamiltonian (PCH) model, as well as the method of simultaneous injection damping. Then, the DFIG is further modeled by assignment passivity based on the simultaneous injection damping and assignment (SIDA-PBC) control of the obtained model under such conditions and a comparison with the fuzzy sliding mode controller is carried out. Furthermore, the HGO is selected in order to estimate the rotor position and the speed from the measurement of the DFIG currents and voltages, and compared with fuzzy sliding mode observer. For testing the proposed control scheme performance, a 1.5 MW DFIG system is developed and simulated using MATLAB/Simulink. The obtained results demonstrate the effectiveness of the proposed control scheme in the presence of various DFIG parameters variation. Additionally, the control objective is achieved without speed sensor.
{"title":"Sensorless passivity based control of doubly-fed induction generators in variable-speed wind turbine systems based on high gain observer","authors":"L. Saihi, B. Berbaoui, Larbi Djilali, Mohammed Boura","doi":"10.1177/0309524X221122531","DOIUrl":"https://doi.org/10.1177/0309524X221122531","url":null,"abstract":"The current study presents a robust sensorless control using passivity based control (PBC) combined with high gain observer (HGO). The proposed controller is applied to control the generated doubly-fed induction generator (DFIG) active and reactive power installed on a variable speed wind energy conversion system. The control objective is used to regulate independently the DFIG stator active and reactive power, which are decoupled by using the field oriented control technique. Additionally, this process leads to reduce the cost of control scheme by eliminating the speed sensor. Firstly, the DFIG is modeled under the port controlled Hamiltonian (PCH) model, as well as the method of simultaneous injection damping. Then, the DFIG is further modeled by assignment passivity based on the simultaneous injection damping and assignment (SIDA-PBC) control of the obtained model under such conditions and a comparison with the fuzzy sliding mode controller is carried out. Furthermore, the HGO is selected in order to estimate the rotor position and the speed from the measurement of the DFIG currents and voltages, and compared with fuzzy sliding mode observer. For testing the proposed control scheme performance, a 1.5 MW DFIG system is developed and simulated using MATLAB/Simulink. The obtained results demonstrate the effectiveness of the proposed control scheme in the presence of various DFIG parameters variation. Additionally, the control objective is achieved without speed sensor.","PeriodicalId":51570,"journal":{"name":"Wind Engineering","volume":"1 1","pages":"86 - 103"},"PeriodicalIF":1.5,"publicationDate":"2022-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83836977","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 : 2022-09-12DOI: 10.1177/0309524X221122501
Aishvarya Narain, S. Srivastava, S. Singh
The wind farm is a collection of all wind turbine generators situated at a particular distance. The wind speed and wind direction play an important role in wind farm power calculation. The power curve of the wind farm is not simply the summation of the wind turbine’s power curve. It is complex due to the intermittent nature of wind speed and its direction. The power curve is obtained from the data taking the wind speed and wind power at that speed. There are many logistic functions used in the literature to analyze the wind power curve that helps to calculate wind farm power output and energy generated. In this paper, the 3-parameter deterministic process (3P-DP) method is used for wind power curve calculation. The wake effect is analyzed by Jensen’s model with wind speed and wind direction. The wind farm power is obtained from the new proposed formula and compared with the already existing one. The results are verified from real data obtained from the literature.
{"title":"The impact of wind direction on wind farm power output calculation considering the wake effects of wind turbines","authors":"Aishvarya Narain, S. Srivastava, S. Singh","doi":"10.1177/0309524X221122501","DOIUrl":"https://doi.org/10.1177/0309524X221122501","url":null,"abstract":"The wind farm is a collection of all wind turbine generators situated at a particular distance. The wind speed and wind direction play an important role in wind farm power calculation. The power curve of the wind farm is not simply the summation of the wind turbine’s power curve. It is complex due to the intermittent nature of wind speed and its direction. The power curve is obtained from the data taking the wind speed and wind power at that speed. There are many logistic functions used in the literature to analyze the wind power curve that helps to calculate wind farm power output and energy generated. In this paper, the 3-parameter deterministic process (3P-DP) method is used for wind power curve calculation. The wake effect is analyzed by Jensen’s model with wind speed and wind direction. The wind farm power is obtained from the new proposed formula and compared with the already existing one. The results are verified from real data obtained from the literature.","PeriodicalId":51570,"journal":{"name":"Wind Engineering","volume":"56 1","pages":"74 - 85"},"PeriodicalIF":1.5,"publicationDate":"2022-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87125236","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 : 2022-09-08DOI: 10.1177/0309524X221122594
P. Gipe, E. Möllerström
We review the development of wind turbines for generating electricity from the late 19th century to the present, summarizing some key characteristics. We trace the move from two and four blade wind turbines to the three blades common today. We establish that it was not the governmental-funded wind programs with its large-scale prototypes of the 1970–80s that developed into the commercial turbines of today. Instead, it was the small-scale Danish wind turbines, developed for an agricultural market, that developed into the commercial turbines of today. And we show that much of what we know today about wind turbine design was known by the 1930s and certainly well known by the late 1950s. This work is divided into two parts: the first part takes up the development from the first electricity producing wind turbines through to the 1960s and a second part on development from the 1970s onward.
{"title":"An overview of the history of wind turbine development: Part II–The 1970s onward","authors":"P. Gipe, E. Möllerström","doi":"10.1177/0309524X221122594","DOIUrl":"https://doi.org/10.1177/0309524X221122594","url":null,"abstract":"We review the development of wind turbines for generating electricity from the late 19th century to the present, summarizing some key characteristics. We trace the move from two and four blade wind turbines to the three blades common today. We establish that it was not the governmental-funded wind programs with its large-scale prototypes of the 1970–80s that developed into the commercial turbines of today. Instead, it was the small-scale Danish wind turbines, developed for an agricultural market, that developed into the commercial turbines of today. And we show that much of what we know today about wind turbine design was known by the 1930s and certainly well known by the late 1950s. This work is divided into two parts: the first part takes up the development from the first electricity producing wind turbines through to the 1960s and a second part on development from the 1970s onward.","PeriodicalId":51570,"journal":{"name":"Wind Engineering","volume":"56 1","pages":"220 - 248"},"PeriodicalIF":1.5,"publicationDate":"2022-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88364721","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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