This document describes a bi-level framework for visualizing trade-offs in wind farm design between capacity factor and land use. The lower level uses multi-objective optimization to explore the trade-off for different nameplate capacities. The upper level fits curves to the Pareto fronts to parametrically represent the trade-off as a function of nameplate capacity. The framework was tested on a case study comparing layouts with 13 to 67 turbines.
Wind farm development is an extremely complex process, most often driven by three im- portant performance criteria: (i) annual energy production, (ii) lifetime costs, and (iii) net impact on surroundings. Generally, planning a commercial scale wind farm takes several years. Undesirable concept-to-installation delays are primarily attributed to the lack of an upfront understanding of how different factors collectively affect the overall performance of a wind farm. More specifically, it is necessary to understand the balance between the socio-economic, engineering, and environmental objectives at an early stage in the design process. This paper proposes a Wind Farm Tradeoff Visualization (WiFToV) framework that aims to develop first-of-its-kind generalized guidelines for the conceptual design of wind farms, especially at early stages of wind farm development. Two major performance objectives are considered in this work: (i) cost of energy (COE) and (ii) land area per MW installed (LAMI). The COE is estimated using the Wind Turbine Design Cost and Scaling Model (WTDCS) and the Annual Energy Production (AEP) model incorporated by the Unrestricted Wind Farm Layout Optimization (UWFLO) framework. The LAMI is esti- mated using an optimal-layout based land usage model, which is treated as a post-process of the wind farm layout optimization. A Multi-Objective Mixed-Discrete Particle Swarm Optimization (MO-MDPSO) algorithm is used to perform the bi-objective optimization, which simultaneously optimizes the location and types of turbines. Together with a novel Pareto translation technique, the proposed WiFToV framework allows the exploration of the trade-off between COE and LAMI, and their variations with respect to multiple values of nameplate capacity.
The performance of a wind farm is affected by several key factors that can be classified into two cate- gories: the natural factors and the design factors. Hence, the planning of a wind farm requires a clear quantitative understanding of how the balance between the concerned objectives (e.g., socia-economic, engineering, and environmental objectives) is affected by these key factors. This understanding is lacking in the state of the art in wind farm design. The wind farm capacity factor is one of the primary perfor- mance criteria of a wind energy project. For a given land (or sea area) and wind resource, the maximum capacity factor of a particular number of wind turbines can be reached by optimally adjusting the layout of turbines. However, this layout adjustment is constrained owing to the limited land resource. This paper proposes a Bi-level Multi-objective Wind Farm Optimization (BMWFO) framework for planning effective wind energy projects. Two important performance objectives considered in this paper are: (i) wind farm Capacity Factor (CF) and (ii) Land Area per MW Installed (LAMI). Turbine locations, land area, and nameplate capacity are treated as design variables in this work. In the proposed framework, the Capacity Factor - Land Area per MW Installed (CF - LAMI) trade-off is parametrically represented as a function of the nameplate capacity. Such a helpful parameterization of trade-offs is unique in the wind energy literature. The farm output is computed using the wind farm power generation model adopted from the Unrestricted Wind Farm Layout Optimization (UWFLO) framework. The Smallest Bounding Rectangle (SBR) enclosing all turbines is used to calculate the actual land area occupied by the farm site. The wind farm layout optimization is performed in the lower level using the Mixed-Discrete Particle Swarm Optimization (MDPSO), while the CF - LAMI trade-off is parameterized in the upper level. In this work, the CF - LAMI trade-off is successfully quantified by nameplate capacity in the 20 MW to 100 MW range. The Pareto curves obtained from the proposed framework provide important in- sights into the trade-offs between the two performance objectives, which can significantly streamline the decision-making process in wind farm development.
The development of utility-scale wind farms that can produce energy at a cost comparable to that of conventional energy resources presents significant challenges to today’s wind energy industry. The consideration of the combined impact of key design and environmental factors on the performance of a wind farm is a crucial part of the solution to this challenge. The state of the art in optimal wind project planning includes wind farm layout design and more recently turbine selection. The scope of farm layout optimization and the predicted wind project performance however depends on several other critical site-scale factors, which are often not explicitly accounted for in the wind farm planning literature. These factors include: (i) the land area per MW installed (LAMI), and (ii) the nameplate capacity (in MW) of the farm. In this paper, we develop a framework to quantify and analyze the roles of these crucial design factors in optimal wind farm planning. A set of sample values of LAMI and installed farm capacities is first defined. For each sample farm definition, simultaneous optimization of the farm layout and turbine selection is performed to maximize the farm capacity factor (CF). To this end, we apply the recently de- veloped Unrestricted Wind Farm Layout Optimization (UWFLO) method. The CF of the optimized farm is then represented as a function of the nameplate capacity and the LAMI, using response surface methodologies. The variation of the optimized CF with these site-scale factors is investigated for a representative wind site in North Dakota. It was found that, a desirable CF value corresponds to a cutoff “LAMI vs nameplate capacity” curve – the identification of this cutoff curve is critical to the development of an economically viable wind energy project.
The performance expectations for commercial wind turbines, from a variety of geograph- ical regions with differing wind regimes, present significant techno-commercial challenges to manufacturers. The determination of which commercial turbine types perform the best under differing wind regimes can provide unique insights into the complex demands of a concerned target market. In this paper, a comprehensive methodology is developed to explore the suitability of commercially available wind turbines (when operating as a group/array) to the various wind regimes occurring over a large target market. The three major steps of this methodology include: (i) characterizing the geographical variation of wind regimes in the target market, (ii) determining the best performing turbines (in terms of minimum COE accomplished) for different wind regimes, and (iii) developing a metric to investigate the performance-based expected market suitability of currently available tur- bine feature combinations. The best performing turbines for different wind regimes are determined using the Unrestricted Wind Farm Layout Optimization (UWFLO) method. Expectedly, the larger sized and higher rated-power turbines provide better performance at lower average wind speeds. However, for wind resources higher than class-4, the perfor- mances of lower-rated power turbines are fairly competitive, which could make them better choices for sites with complex terrain or remote location. In addition, turbines with direct drive are observed to perform significantly better than turbines with more conventional gear-based drive-train. The market considered in this paper is mainland USA, for which wind map information is obtained from NREL. Interestingly, it is found that overall higher rated-power turbines with relatively lower tower heights are most favored in the onshore US market.
The development of large scale wind farms that can produce energy at a cost comparable to that of other conventional energy resources presents significant challenges to today’s wind energy industry. The consideration of the key design and environmental factors that influence the performance of a wind farm is a crucial part of the solution to this challenge. In this paper, we develop a methodology to account for the configuration of the farm land (length-to-breadth ratio and North-South-East-West orientation) within the scope of wind farm optimization. This approach appropriately captures the correlation between the (i) land configuration, (ii) the farm layout, and (iii) the selection of turbines-types. Simultaneous optimization of the farm layout and turbine selection is performed to minimize the Cost of Energy (COE), for a set of sample land configurations. The optimized COE and farm efficiency are then represented as functions of the land aspect ratio and the land orientation. To this end, we apply a recently developed response surface method known as the Reliability-Based Hybrid Functions. The overall wind farm design methodology is applied to design a 25MW farm in North Dakota. This case study provides helpful insights into the influence of the land configuration on the optimum farm performance that can be obtained for a particular site.
Wind farm development is an extremely complex process, most often driven by three im- portant performance criteria: (i) annual energy production, (ii) lifetime costs, and (iii) net impact on surroundings. Generally, planning a commercial scale wind farm takes several years. Undesirable concept-to-installation delays are primarily attributed to the lack of an upfront understanding of how different factors collectively affect the overall performance of a wind farm. More specifically, it is necessary to understand the balance between the socio-economic, engineering, and environmental objectives at an early stage in the design process. This paper proposes a Wind Farm Tradeoff Visualization (WiFToV) framework that aims to develop first-of-its-kind generalized guidelines for the conceptual design of wind farms, especially at early stages of wind farm development. Two major performance objectives are considered in this work: (i) cost of energy (COE) and (ii) land area per MW installed (LAMI). The COE is estimated using the Wind Turbine Design Cost and Scaling Model (WTDCS) and the Annual Energy Production (AEP) model incorporated by the Unrestricted Wind Farm Layout Optimization (UWFLO) framework. The LAMI is esti- mated using an optimal-layout based land usage model, which is treated as a post-process of the wind farm layout optimization. A Multi-Objective Mixed-Discrete Particle Swarm Optimization (MO-MDPSO) algorithm is used to perform the bi-objective optimization, which simultaneously optimizes the location and types of turbines. Together with a novel Pareto translation technique, the proposed WiFToV framework allows the exploration of the trade-off between COE and LAMI, and their variations with respect to multiple values of nameplate capacity.
The performance of a wind farm is affected by several key factors that can be classified into two cate- gories: the natural factors and the design factors. Hence, the planning of a wind farm requires a clear quantitative understanding of how the balance between the concerned objectives (e.g., socia-economic, engineering, and environmental objectives) is affected by these key factors. This understanding is lacking in the state of the art in wind farm design. The wind farm capacity factor is one of the primary perfor- mance criteria of a wind energy project. For a given land (or sea area) and wind resource, the maximum capacity factor of a particular number of wind turbines can be reached by optimally adjusting the layout of turbines. However, this layout adjustment is constrained owing to the limited land resource. This paper proposes a Bi-level Multi-objective Wind Farm Optimization (BMWFO) framework for planning effective wind energy projects. Two important performance objectives considered in this paper are: (i) wind farm Capacity Factor (CF) and (ii) Land Area per MW Installed (LAMI). Turbine locations, land area, and nameplate capacity are treated as design variables in this work. In the proposed framework, the Capacity Factor - Land Area per MW Installed (CF - LAMI) trade-off is parametrically represented as a function of the nameplate capacity. Such a helpful parameterization of trade-offs is unique in the wind energy literature. The farm output is computed using the wind farm power generation model adopted from the Unrestricted Wind Farm Layout Optimization (UWFLO) framework. The Smallest Bounding Rectangle (SBR) enclosing all turbines is used to calculate the actual land area occupied by the farm site. The wind farm layout optimization is performed in the lower level using the Mixed-Discrete Particle Swarm Optimization (MDPSO), while the CF - LAMI trade-off is parameterized in the upper level. In this work, the CF - LAMI trade-off is successfully quantified by nameplate capacity in the 20 MW to 100 MW range. The Pareto curves obtained from the proposed framework provide important in- sights into the trade-offs between the two performance objectives, which can significantly streamline the decision-making process in wind farm development.
The development of utility-scale wind farms that can produce energy at a cost comparable to that of conventional energy resources presents significant challenges to today’s wind energy industry. The consideration of the combined impact of key design and environmental factors on the performance of a wind farm is a crucial part of the solution to this challenge. The state of the art in optimal wind project planning includes wind farm layout design and more recently turbine selection. The scope of farm layout optimization and the predicted wind project performance however depends on several other critical site-scale factors, which are often not explicitly accounted for in the wind farm planning literature. These factors include: (i) the land area per MW installed (LAMI), and (ii) the nameplate capacity (in MW) of the farm. In this paper, we develop a framework to quantify and analyze the roles of these crucial design factors in optimal wind farm planning. A set of sample values of LAMI and installed farm capacities is first defined. For each sample farm definition, simultaneous optimization of the farm layout and turbine selection is performed to maximize the farm capacity factor (CF). To this end, we apply the recently de- veloped Unrestricted Wind Farm Layout Optimization (UWFLO) method. The CF of the optimized farm is then represented as a function of the nameplate capacity and the LAMI, using response surface methodologies. The variation of the optimized CF with these site-scale factors is investigated for a representative wind site in North Dakota. It was found that, a desirable CF value corresponds to a cutoff “LAMI vs nameplate capacity” curve – the identification of this cutoff curve is critical to the development of an economically viable wind energy project.
The performance expectations for commercial wind turbines, from a variety of geograph- ical regions with differing wind regimes, present significant techno-commercial challenges to manufacturers. The determination of which commercial turbine types perform the best under differing wind regimes can provide unique insights into the complex demands of a concerned target market. In this paper, a comprehensive methodology is developed to explore the suitability of commercially available wind turbines (when operating as a group/array) to the various wind regimes occurring over a large target market. The three major steps of this methodology include: (i) characterizing the geographical variation of wind regimes in the target market, (ii) determining the best performing turbines (in terms of minimum COE accomplished) for different wind regimes, and (iii) developing a metric to investigate the performance-based expected market suitability of currently available tur- bine feature combinations. The best performing turbines for different wind regimes are determined using the Unrestricted Wind Farm Layout Optimization (UWFLO) method. Expectedly, the larger sized and higher rated-power turbines provide better performance at lower average wind speeds. However, for wind resources higher than class-4, the perfor- mances of lower-rated power turbines are fairly competitive, which could make them better choices for sites with complex terrain or remote location. In addition, turbines with direct drive are observed to perform significantly better than turbines with more conventional gear-based drive-train. The market considered in this paper is mainland USA, for which wind map information is obtained from NREL. Interestingly, it is found that overall higher rated-power turbines with relatively lower tower heights are most favored in the onshore US market.
The development of large scale wind farms that can produce energy at a cost comparable to that of other conventional energy resources presents significant challenges to today’s wind energy industry. The consideration of the key design and environmental factors that influence the performance of a wind farm is a crucial part of the solution to this challenge. In this paper, we develop a methodology to account for the configuration of the farm land (length-to-breadth ratio and North-South-East-West orientation) within the scope of wind farm optimization. This approach appropriately captures the correlation between the (i) land configuration, (ii) the farm layout, and (iii) the selection of turbines-types. Simultaneous optimization of the farm layout and turbine selection is performed to minimize the Cost of Energy (COE), for a set of sample land configurations. The optimized COE and farm efficiency are then represented as functions of the land aspect ratio and the land orientation. To this end, we apply a recently developed response surface method known as the Reliability-Based Hybrid Functions. The overall wind farm design methodology is applied to design a 25MW farm in North Dakota. This case study provides helpful insights into the influence of the land configuration on the optimum farm performance that can be obtained for a particular site.
Wind resources vary significantly in strength from one location to another over a wide geographical region. The major turbine manufacturers offer a family/series of wind tur- bines to suit the market needs of different wind regimes. The current state of the art in wind farm design however does not provide quantitative guidelines regarding what turbine feature combinations are suitable for different wind regimes, when turbines are operating as a group in an optimized layout. This paper provides a unique exploration of the best tradeoffs between the cost and the capacity factor of wind farms (of specified nameplate capacity), provided by the currently available turbines for different wind classes. To this end, the best performing turbines for different wind resource strengths are identified by minimizing the cost of energy through wind farm layout optimization. Exploration of the “cost - capacity factor” tradeoffs are then performed for the wind resource strengths cor- responding to the wind classes defined in the 7-class system. The best tradeoff turbines are determined by searching for the non-dominated set of turbines out of the pool of best performing turbines of different rated powers. The medium priced turbines are observed to provide the most attractive tradeoffs − 15% more capacity factor than the cheapest tradeoff turbines and only 5% less capacity factor than the most expensive tradeoff turbines. It was found that although the “cost - capacity factor” tradeoff curve expectedly shifted towards higher capacity factors with increasing wind class, the trend of the tradeoff curve remained practically similar. Further analysis showed that the “rated power - rotor diameter” com- bination and the “rotor diameter/hub height” ratios are very important considerations in the current selection and further evolution of turbine designs. We found that larger rotor diameters are not preferred for mid-range turbines with rated powers between 1.5 - 2.5 MW, and “rotor diameter/hub height” ratios greater than 1.1 are not preferred by any of the wind classes.
Impact of Different Wake Models on the Estimation of Wind Farm Power GenerationWeiyang Tong
For citations, please refer to the journal version of this paper,
by Tong et al., "Sensitivity of Wind Farm Output to Wind Conditions, Land Configuration, and Installed Capacity, Under Different Wake Models", J. Mech. Des. 137(6), 061403 (Jun 01, 2015) (11 pages), Paper No: MD-14-1339; doi: 10.1115/1.4029892
available at:
http://mechanicaldesign.asmedigitalcollection.asme.org/article.aspx?articleid=2173776
Control Scheme for an IPM Synchronous Generator Based-Variable Speed Wind Tur...IJMTST Journal
This paper proposes a control strategy for an IPM synchronous generator-based variable speed wind turbine this control technique is simple and has many advantages over indirect vector control technique as in this scheme, the requirement of the continuous rotor position is eliminated as all the calculations are done in the stator reference frame and can eliminate some of the drawbacks of traditional indirect vector control scheme. This scheme possesses advantages such as lesser parameter dependence and reduced number of controllers compared with the traditional indirect vector control scheme Furthermore, the system is unaffected to variation in parameters because stator resistance is the only required criteria. This control technique is implemented in MATLAB/Sim power systems and the simulation results shows that this suggested control technique works well and can operate under constant and varying wind speeds. Finally, a sensorless speed estimator is implemented, which enables the wind turbine to operate without the mechanical speed sensor.
The maintenance cost of wind farms is one of the major factors influencing the prof- itability of wind projects. During preventive maintenance, the shutdown of wind turbines results in downtime wind energy losses. Appropriate determination of when to perform maintenance and which turbine(s) to maintain can reduce the overall downtime losses sig- nificantly. This paper uses a wind farm power generation model to evaluate downtime energy losses during preventive maintenance for a given group of wind turbines in the en- tire array. Wakes effects are taken into account to accurately estimate energy production over a specified time period. In addition to wind condition, the influence of wake effects is a critical factor in determining the selection of turbine(s) under maintenance. To min- imize the overall downtime loss of an offshore wind farm due to preventive maintenance, an optimal scheduling problem is formulated that selects the maintenance time of each turbine. Weather conditions are imposed as constraints to ensure the safety of mainte- nance personnel, transportation, and tooling infrastructure. A genetic algorithm is used to solve the optimal scheduling problem. The maintenance scheduling is optimized for a utility-scale offshore wind farm with 25 turbines. The optimized schedule not only reduces the overall downtime loss by selecting the maintenance dates when wind speed is low, but also considers the wake effects among turbines. Under given wind direction, the turbines under maintenance are usually the ones that can generate strong wake effects on others during certain wind conditions, or the ones that generate relatively less power being under excessive wake effects.
A computer Model of Fuel Consumption Estimation for Different Agricultural Fa...Agriculture Journal IJOEAR
Abstract— A computer programme was developed to estimate fuel consumption rate in liter per hour for medium agric-tractor with load and without load under different soil conditions. The programme enables the user to insert the input data through the input interface and obtain the output rapidly. The model was verified, validated and tested by using data from literature and a private agricultural services company in Sudan, for two types of heavy disc harrow (AH280, BH360), (H56,CH65C) driven by challenger track tractors, on the other hand, seeder and ridger separately operated with wheeled 4WD tractors. It was also tested by data from Sennar Agricultural Services Center, using heavy disc harrow with 4WD tractor. The sensitivity analysis showed that the change in any of input parameters, e.g. speed, unit draft, engine power affected directly the estimated fuel consumption rate. Accordingly, the computer programme performed very well in estimating fuel consumption and can be used as a good guide to the farmer or any interested person in machinery management and for quick decision-making.
The performance of a wind farm is affected by several key factors that can be classified into two cate- gories: the natural factors and the design factors. Hence, the planning of a wind farm requires a clear quantitative understanding of how the balance between the concerned objectives (e.g., socia-economic, engineering, and environmental objectives) is affected by these key factors. This understanding is lacking in the state of the art in wind farm design. The wind farm capacity factor is one of the primary perfor- mance criteria of a wind energy project. For a given land (or sea area) and wind resource, the maximum capacity factor of a particular number of wind turbines can be reached by optimally adjusting the layout of turbines. However, this layout adjustment is constrained owing to the limited land resource. This paper proposes a Bi-level Multi-objective Wind Farm Optimization (BMWFO) framework for planning effective wind energy projects. Two important performance objectives considered in this paper are: (i) wind farm Capacity Factor (CF) and (ii) Land Area per MW Installed (LAMI). Turbine locations, land area, and nameplate capacity are treated as design variables in this work. In the proposed framework, the Capacity Factor - Land Area per MW Installed (CF - LAMI) trade-off is parametrically represented as a function of the nameplate capacity. Such a helpful parameterization of trade-offs is unique in the wind energy literature. The farm output is computed using the wind farm power generation model adopted from the Unrestricted Wind Farm Layout Optimization (UWFLO) framework. The Smallest Bounding Rectangle (SBR) enclosing all turbines is used to calculate the actual land area occupied by the farm site. The wind farm layout optimization is performed in the lower level using the Mixed-Discrete Particle Swarm Optimization (MDPSO), while the CF - LAMI trade-off is parameterized in the upper level. In this work, the CF - LAMI trade-off is successfully quantified by nameplate capacity in the 20 MW to 100 MW range. The Pareto curves obtained from the proposed framework provide important in- sights into the trade-offs between the two performance objectives, which can significantly streamline the decision-making process in wind farm development.
The planning of a wind farm, which minimizes the project costs and maximizes the power generation capacity, presents significant challenges to today’s wind energy industry. An optimal wind farm planning strategy that accounts for the key factors (that can be designed) influencing the net power generation offers a powerful solution to these daunting challenges. This paper explores the influences of (i) the number of turbines, (ii) the farm size, and (iii) the use of a combination of turbines with differing rotor diameters, on the optimal power generated by a wind farm. We use a recently developed method of arranging turbines in a wind farm (the Unrestricted Wind Farm Layout Optimization (UWFLO)) to maximize the farm efficiency. Response surface based cost models are used to estimate the cost of the wind farm as a function of the the turbine rotor diameters and number of tur- bines. Optimization is performed using a Particle Swarm Optimization (PSO) algorithm. A robust mixed-discrete version of the PSO algorithm is implemented to appropriately account for the discrete choice of feasible rotor diameters. The use of an optimal combi- nation of turbines with differing rotor diameters was observed to significantly improve the net power generation. Exploration of the influences of (i) the number of turbines, and (ii) the farm size, on the cost per KW of power produced, provided interesting observations.
Wind resources vary significantly in strength from one location to another over a wide geographical region. The major turbine manufacturers offer a family/series of wind tur- bines to suit the market needs of different wind regimes. The current state of the art in wind farm design however does not provide quantitative guidelines regarding what turbine feature combinations are suitable for different wind regimes, when turbines are operating as a group in an optimized layout. This paper provides a unique exploration of the best tradeoffs between the cost and the capacity factor of wind farms (of specified nameplate capacity), provided by the currently available turbines for different wind classes. To this end, the best performing turbines for different wind resource strengths are identified by minimizing the cost of energy through wind farm layout optimization. Exploration of the “cost - capacity factor” tradeoffs are then performed for the wind resource strengths cor- responding to the wind classes defined in the 7-class system. The best tradeoff turbines are determined by searching for the non-dominated set of turbines out of the pool of best performing turbines of different rated powers. The medium priced turbines are observed to provide the most attractive tradeoffs − 15% more capacity factor than the cheapest tradeoff turbines and only 5% less capacity factor than the most expensive tradeoff turbines. It was found that although the “cost - capacity factor” tradeoff curve expectedly shifted towards higher capacity factors with increasing wind class, the trend of the tradeoff curve remained practically similar. Further analysis showed that the “rated power - rotor diameter” com- bination and the “rotor diameter/hub height” ratios are very important considerations in the current selection and further evolution of turbine designs. We found that larger rotor diameters are not preferred for mid-range turbines with rated powers between 1.5 - 2.5 MW, and “rotor diameter/hub height” ratios greater than 1.1 are not preferred by any of the wind classes.
Impact of Different Wake Models on the Estimation of Wind Farm Power GenerationWeiyang Tong
For citations, please refer to the journal version of this paper,
by Tong et al., "Sensitivity of Wind Farm Output to Wind Conditions, Land Configuration, and Installed Capacity, Under Different Wake Models", J. Mech. Des. 137(6), 061403 (Jun 01, 2015) (11 pages), Paper No: MD-14-1339; doi: 10.1115/1.4029892
available at:
http://mechanicaldesign.asmedigitalcollection.asme.org/article.aspx?articleid=2173776
Control Scheme for an IPM Synchronous Generator Based-Variable Speed Wind Tur...IJMTST Journal
This paper proposes a control strategy for an IPM synchronous generator-based variable speed wind turbine this control technique is simple and has many advantages over indirect vector control technique as in this scheme, the requirement of the continuous rotor position is eliminated as all the calculations are done in the stator reference frame and can eliminate some of the drawbacks of traditional indirect vector control scheme. This scheme possesses advantages such as lesser parameter dependence and reduced number of controllers compared with the traditional indirect vector control scheme Furthermore, the system is unaffected to variation in parameters because stator resistance is the only required criteria. This control technique is implemented in MATLAB/Sim power systems and the simulation results shows that this suggested control technique works well and can operate under constant and varying wind speeds. Finally, a sensorless speed estimator is implemented, which enables the wind turbine to operate without the mechanical speed sensor.
The maintenance cost of wind farms is one of the major factors influencing the prof- itability of wind projects. During preventive maintenance, the shutdown of wind turbines results in downtime wind energy losses. Appropriate determination of when to perform maintenance and which turbine(s) to maintain can reduce the overall downtime losses sig- nificantly. This paper uses a wind farm power generation model to evaluate downtime energy losses during preventive maintenance for a given group of wind turbines in the en- tire array. Wakes effects are taken into account to accurately estimate energy production over a specified time period. In addition to wind condition, the influence of wake effects is a critical factor in determining the selection of turbine(s) under maintenance. To min- imize the overall downtime loss of an offshore wind farm due to preventive maintenance, an optimal scheduling problem is formulated that selects the maintenance time of each turbine. Weather conditions are imposed as constraints to ensure the safety of mainte- nance personnel, transportation, and tooling infrastructure. A genetic algorithm is used to solve the optimal scheduling problem. The maintenance scheduling is optimized for a utility-scale offshore wind farm with 25 turbines. The optimized schedule not only reduces the overall downtime loss by selecting the maintenance dates when wind speed is low, but also considers the wake effects among turbines. Under given wind direction, the turbines under maintenance are usually the ones that can generate strong wake effects on others during certain wind conditions, or the ones that generate relatively less power being under excessive wake effects.
A computer Model of Fuel Consumption Estimation for Different Agricultural Fa...Agriculture Journal IJOEAR
Abstract— A computer programme was developed to estimate fuel consumption rate in liter per hour for medium agric-tractor with load and without load under different soil conditions. The programme enables the user to insert the input data through the input interface and obtain the output rapidly. The model was verified, validated and tested by using data from literature and a private agricultural services company in Sudan, for two types of heavy disc harrow (AH280, BH360), (H56,CH65C) driven by challenger track tractors, on the other hand, seeder and ridger separately operated with wheeled 4WD tractors. It was also tested by data from Sennar Agricultural Services Center, using heavy disc harrow with 4WD tractor. The sensitivity analysis showed that the change in any of input parameters, e.g. speed, unit draft, engine power affected directly the estimated fuel consumption rate. Accordingly, the computer programme performed very well in estimating fuel consumption and can be used as a good guide to the farmer or any interested person in machinery management and for quick decision-making.
The performance of a wind farm is affected by several key factors that can be classified into two cate- gories: the natural factors and the design factors. Hence, the planning of a wind farm requires a clear quantitative understanding of how the balance between the concerned objectives (e.g., socia-economic, engineering, and environmental objectives) is affected by these key factors. This understanding is lacking in the state of the art in wind farm design. The wind farm capacity factor is one of the primary perfor- mance criteria of a wind energy project. For a given land (or sea area) and wind resource, the maximum capacity factor of a particular number of wind turbines can be reached by optimally adjusting the layout of turbines. However, this layout adjustment is constrained owing to the limited land resource. This paper proposes a Bi-level Multi-objective Wind Farm Optimization (BMWFO) framework for planning effective wind energy projects. Two important performance objectives considered in this paper are: (i) wind farm Capacity Factor (CF) and (ii) Land Area per MW Installed (LAMI). Turbine locations, land area, and nameplate capacity are treated as design variables in this work. In the proposed framework, the Capacity Factor - Land Area per MW Installed (CF - LAMI) trade-off is parametrically represented as a function of the nameplate capacity. Such a helpful parameterization of trade-offs is unique in the wind energy literature. The farm output is computed using the wind farm power generation model adopted from the Unrestricted Wind Farm Layout Optimization (UWFLO) framework. The Smallest Bounding Rectangle (SBR) enclosing all turbines is used to calculate the actual land area occupied by the farm site. The wind farm layout optimization is performed in the lower level using the Mixed-Discrete Particle Swarm Optimization (MDPSO), while the CF - LAMI trade-off is parameterized in the upper level. In this work, the CF - LAMI trade-off is successfully quantified by nameplate capacity in the 20 MW to 100 MW range. The Pareto curves obtained from the proposed framework provide important in- sights into the trade-offs between the two performance objectives, which can significantly streamline the decision-making process in wind farm development.
The planning of a wind farm, which minimizes the project costs and maximizes the power generation capacity, presents significant challenges to today’s wind energy industry. An optimal wind farm planning strategy that accounts for the key factors (that can be designed) influencing the net power generation offers a powerful solution to these daunting challenges. This paper explores the influences of (i) the number of turbines, (ii) the farm size, and (iii) the use of a combination of turbines with differing rotor diameters, on the optimal power generated by a wind farm. We use a recently developed method of arranging turbines in a wind farm (the Unrestricted Wind Farm Layout Optimization (UWFLO)) to maximize the farm efficiency. Response surface based cost models are used to estimate the cost of the wind farm as a function of the the turbine rotor diameters and number of tur- bines. Optimization is performed using a Particle Swarm Optimization (PSO) algorithm. A robust mixed-discrete version of the PSO algorithm is implemented to appropriately account for the discrete choice of feasible rotor diameters. The use of an optimal combi- nation of turbines with differing rotor diameters was observed to significantly improve the net power generation. Exploration of the influences of (i) the number of turbines, and (ii) the farm size, on the cost per KW of power produced, provided interesting observations.
The performance expectations for commercial wind turbines, from a variety of geograph- ical regions with differing wind regimes, present significant techno-commercial challenges to manufacturers. The determination of which commercial turbine types perform the best under differing wind regimes can provide unique insights into the complex demands of a concerned target market. In this paper, a comprehensive methodology is developed to explore the suitability of commercially available wind turbines (when operating as a group/array) to the various wind regimes occurring over a large target market. The three major steps of this methodology include: (i) characterizing the geographical variation of wind regimes in the target market, (ii) determining the best performing turbines (in terms of minimum COE accomplished) for different wind regimes, and (iii) developing a metric to investigate the performance-based expected market suitability of currently available tur- bine feature combinations. The best performing turbines for different wind regimes are determined using the Unrestricted Wind Farm Layout Optimization (UWFLO) method. Expectedly, the larger sized and higher rated-power turbines provide better performance at lower average wind speeds. However, for wind resources higher than class-4, the perfor- mances of lower-rated power turbines are fairly competitive, which could make them better choices for sites with complex terrain or remote location. In addition, turbines with direct drive are observed to perform significantly better than turbines with more conventional gear-based drive-train. The market considered in this paper is mainland USA, for which wind map information is obtained from NREL. Interestingly, it is found that overall higher rated-power turbines with relatively lower tower heights are most favored in the onshore US market.
In this paper, we develop a flexible design platform to ac- count for the influences of key factors in optimal planning of commercial scale wind farms. The Unrestricted Wind Farm Lay- out Optimization (UWFLO) methodology, which avoids limit- ing assumptions regarding the farm layout and the selection of turbines, is used to develop this design platform. This paper presents critical advancements to the UWFLO methodology to allow the synergistic consideration of (i) the farm layout, (ii) the types of commercial turbines to be installed, and (iii) the ex- pected annual distribution of wind conditions at a particular site. We use a recently developed Kernel Density Estimation (KDE) based method to characterize the multivariate distribution of wind speed and wind direction. Optimization is performed using an advanced mixed discrete Particle Swarm Optimization algo- rithm. We also implement a high fidelity wind farm cost model that is developed using a Radial Basis Function (RBF) based response surface. The new optimal farm planning platform is applied to design a 25-turbine wind farm at a North Dakota site. We found that the optimal layout is significantly sensitive to the annual variation in wind conditions. Allowing the turbine-types to be selected during optimization was observed to improve the annual energy production by 49% compared to layout optimiza- tion alone.
The creation of wakes, with unique turbulence charac- teristics, downstream of turbines significantly increases the complexity of the boundary layer flow within a wind farm. In conventional wind farm design, analytical wake models are generally used to compute the wake-induced power losses, with different wake models yielding significantly different estimates. In this context, the wake behavior, and subsequently the farm power generation, can be expressed as functions of a series of key factors. A quantitative understanding of the relative impact of each of these factors is paramount to the development of more reliable power generation models; such an understanding is however missing in the current state of the art in wind farm design. In this paper, we quantitatively explore how the farm power generation, estimated using four different analytical wake models, is influenced by the following key factors: (i) incoming wind speed, (ii) land configuration, and (iii) ambient turbulence. The sensitivity of the maximum farm output potential to the input factors, when using different wake models, is also analyzed. The extended Fourier Amplitude Sensitivity Test (eFAST) method is used to perform the sensitivity analysis. The power generation model and the optimization strategy is adopted from the Unrestricted Wind Farm Layout Optimization (UWFLO) framework. In the case of an array-like turbine arrangement, both the first-order and the total-order sensitivity analysis indices of the power output with respect to the incoming wind speed were found to reach a value of 99%, irrespective of the choice of wake models. However, in the case of maximum power output, significant variation (around 30%) in the indices was observed across different wake models, especially when the incoming wind speed is close to the rated speed of the turbines.
Effective and time-efficient decision-making in the early stages of wind farm planning can lay the foundation of a successful wind energy project. Undesirable concept-to-installation delays in wind farm development is often caused by conflicting decisions from the major parties involved (e.g., developer, investors, landowners, and local communities), which in turn can be (in a major part) attributed to the lack of an upfront understanding of the trade-offs between the technical, socio-economic, and environmental-impact aspects of the wind farm for the given site. This paper proposes a consolidated visualization platform for wind farm planning, which could facilitate informed and co-operative decision-making by the parties involved. This visualization platform offers a GUI-based land shape chart, which provides the following information: the variation of the energy production capacity and of the corresponding required optimal land shape with different land area and nameplate capacity decisions. In order to develop this chart, a bi-objective optimization problem is formulated (using the Unrestricted Wind Farm Layout Optimization framework) to max- imize the capacity factor and minimize the land usage, subject to different nameplate capacity decisions. The application of an Optimal Layout-based land usage estimate allows the wind farm layout optimization to run without pre-specifying any farm boundaries; the optimal land shape is instead determined as a post process, using convex hull and minimum bounding rectangle concept, based on the optimal arrangement of turbines. Three land shape charts are generated under three characteristic wind patterns - (i) single dominant wind direction, (ii) two opposite dominant wind directions, and (ii) two orthogonal domi- nant wind directions, all three patterns comprising the same wind speed distribution. The results indicate that the optimal land shape is highly sensitive to the variation in LAMI for small-capacity wind farms (few turbines) and to the variation in nameplate capacity for small allowed land area. For the same decided nameplate capacity and LAMI values, we observe reasonable similarity in the optimal land shapes and the maximum energy pro- duction potentials given the “single dominant direction” and the “two opposite dominant directions” wind patterns; the optimal land shapes and the maximum energy production potentials yielded by the “two orthogonal dominant directions” wind pattern is however observed to be relatively different from the other two cases.
Wind resources vary significantly in strength from one location to another over a wide geographical region. The major turbine manufacturers offer a family/series of wind tur- bines to suit the market needs of different wind regimes. The current state of the art in wind farm design however does not provide quantitative guidelines regarding what turbine feature combinations are suitable for different wind regimes, when turbines are operating as a group in an optimized layout. This paper provides a unique exploration of the best tradeoffs between the cost and the capacity factor of wind farms (of specified nameplate capacity), provided by the currently available turbines for different wind classes. To this end, the best performing turbines for different wind resource strengths are identified by minimizing the cost of energy through wind farm layout optimization. Exploration of the “cost - capacity factor” tradeoffs are then performed for the wind resource strengths cor- responding to the wind classes defined in the 7-class system. The best tradeoff turbines are determined by searching for the non-dominated set of turbines out of the pool of best performing turbines of different rated powers. The medium priced turbines are observed to provide the most attractive tradeoffs − 15% more capacity factor than the cheapest tradeoff turbines and only 5% less capacity factor than the most expensive tradeoff turbines. It was found that although the “cost - capacity factor” tradeoff curve expectedly shifted towards higher capacity factors with increasing wind class, the trend of the tradeoff curve remained practically similar. Further analysis showed that the “rated power - rotor diameter” com- bination and the “rotor diameter/hub height” ratios are very important considerations in the current selection and further evolution of turbine designs. We found that larger rotor diameters are not preferred for mid-range turbines with rated powers between 1.5 - 2.5 MW, and “rotor diameter/hub height” ratios greater than 1.1 are not preferred by any of the wind classes.
This paper presents a new method (the Unrestricted Wind Farm Layout Optimization (UWFLO)) of arranging turbines in a wind farm to achieve maximum farm efficiency. The powers generated by individual turbines in a wind farm are dependent on each other, due to velocity deficits created by the wake effect. A standard analytical wake model has been used to account for the mutual influences of the turbines in a wind farm. A variable induction factor, dependent on the approaching wind velocity, estimates the velocity deficit across each turbine. Optimization is performed using a constrained Particle Swarm Optimization (PSO) algorithm. The model is validated against experimental data from a wind tunnel experiment on a scaled down wind farm. Reasonable agreement between the model and experimental results is obtained. A preliminary wind farm cost analysis is also performed to explore the effect of using turbines with different rotor diameters on the total power generation. The use of differing rotor diameters is observed to play an important role in improving the overall efficiency of a wind farm.
The power generation of a wind farm is significantly less than the summation of the power generated by each turbine when operating as a standalone entity. This power reduction can be attributed to the energy loss due to the wake effects − the resulting velocity deficit in the wind downstream of a turbine. In the case of wind farm design, the wake losses are generally quantified using wake models. The effectiveness of wind farm design (seeking to maximize the farm output) therefore depends on the accuracy and the reliability of the wake models. This paper compares the impact of the following four analytical wake
models on the wind farm power generation: (i) the Jensen model, (ii) the Larsen model, (iii) the Frandsen model, and (iv) the Ishihara model. The sensitivity of this impact to the Land Area per Turbine (LAT) and the incoming wind speed is also investigated. The wind farm power generation model used in this paper is adopted from the Unrestricted Wind Farm Layout Optimization (UWFLO) methodology. Single wake case studies show that the velocity deficit and the wake diameter estimated by the different analytical wake models can be significantly different. A maximum difference of 70% was also observed for the wind farm capacity factor values estimated using different wake models.
Wind farm layout optimization (WFLO) is the process of optimizing the location of turbines in a wind farm site, with the possible objective of maximizing the energy production or minimizing the average cost of energy. Conventional WFLO methods not only limit themselves to prescribing the site boundaries, they are also generally applicable to designing only small-to-medium scale wind farms (<100 turbines). Large-scale wind farms entail greater wake-induced turbine interactions, thereby increasing the computa- tional complexity and expense by orders of magnitude. In this paper, we further advance the Unrestricted WFLO framework by designing the layout of large-scale wind farms with 500 turbines (where energy pro- duction is maximized). First, the high-dimensional layout optimization problem (involving 2N variables for a N turbine wind farm) is reduced to a 6-variable problem through a novel mapping strategy, which allows for both global siting (overall land configuration) and local exploration (turbine micrositing). Sec- ondly, a surrogate model is used to substitute the expensive analytical WF energy production model; the high computational expense of the latter is attributed to the factorial increase in the number of calls to the wake model for evaluating every candidate wind farm layout that involves a large number of turbines. The powerful Concurrent Surrogate Model Selection (COSMOS) framework is applied to identify the best surrogate model to represent the wind farm energy production as a function of the reduced variable vector. To accomplish a reliable optimum solution, the surrogate-based optimization (SBO) is performed by implementing the Adaptive Model Refinement (AMR) technique within Particle Swarm Optimization (PSO). In AMR, both local exploitation and global exploration aspects are considered within a single optimization run of PSO, unlike other SBO methods that often either require multiple (potentially mis- leading) optimizations or are model-dependent. By using the AMR approach in conjunction with PSO and COSMOS, the computational cost of designing very large wind farms is reduced by a remarkable factor of 26, while preserving the reliability of this WFLO within 0.05% of the WFLO performed using the original energy production model.
Sensitivity of Array-like and Optimized Wind Farm Output to Key Factors and C...Weiyang Tong
For citations, please refer to the journal version of this paper,
by Tong et al., "Sensitivity of Wind Farm Output to Wind Conditions, Land Configuration, and Installed Capacity, Under Different Wake Models", J. Mech. Des. 137(6), 061403 (Jun 01, 2015) (11 pages), Paper No: MD-14-1339; doi: 10.1115/1.4029892
available at:
http://mechanicaldesign.asmedigitalcollection.asme.org/article.aspx?articleid=2173776
In developing complex engineering systems, model-based design approaches often face critical challenges due to pervasive uncertainties and high computational expense. These challenges could be alleviated to a significant extent though informed modeling decisions, such as model substitution, parameter estimation, localized re-sampling, or grid refine- ment. Informed modeling decisions therefore necessitate (currently lacking) design frame- works that effectively integrate design automation and human decision-making. In this paper, we seek to address this necessity in the context of designing wind farm layouts, by taking an information flow perspective of this typical model-based design process. Specif- ically, we develop a visual representation of the uncertainties inherited and generated by models and the inter-model sensitivities. This framework is called the Visually-Informed Decision-Making Platform (VIDMAP) for wind farm design. The eFAST method is used for sensitivity analysis, in order to determine both the first-order and the total-order in- dices. The uncertainties in the independent inputs are quantified based on their observed variance. The uncertainties generated by the upstream models are quantified through a Monte Carlo simulation followed by probabilistic modeling of (i) the error in the output of the models (if high-fidelity estimates are available), or (ii) the deviation in the outputs estimated by different alternatives/versions of the model. The GUI in VIDMAP is cre- ated using value-proportional colors for each model block and inter-model connector, to respectively represent the uncertainty in the model output and the impact (downstream) of the information being relayed by the connector. Wind farm layout optimization (WFLO) serves as an excellent platform to develop and explore VIDMAP, where WFLO is generally performed using low fidelity models, as high-fidelity models (e.g. LES) tend to be compu- tationally prohibitive in this context. The final VIDMAP obtained sheds new light into the sensitivity of wind farm energy estimation on the different models and their associated uncertainties.
In urban area, sitting renewable energy (RE) can be a challenging issue because only few spacious land is available but the demand of the energy is high. Hence the proper selection of RE technology is important to ensure plenty of energy are delivered from limited site area. This paper present how does the local climate condition in typical urban area, Auckland Central Business District, affect annual electricity production and energy production of PV or Wind Power system. The analysis is then extended to find the energy density for respective RE system.The result are strategic to advise which renewable energy system can actually optimize energy production in the small land area.
Integration of a Wind Turbine Based Doubly Fed Induction Generator Using STAT...IJERA Editor
Wind power stations mostly placed in remote areas; so they are characterized by weak grids and are often submitted to power system disturbance like faults, voltage sag etc. In this paper the crowbar protection method is used to ride through voltage sags and STATCOM is used to quickly sense the voltage sag and overcome it. The behavior of these machines during grid failure is an important issue. DFIG consists of a common induction generator with slip ring and a partial scale power electronic converter. Indirect field oriented controller is applied to rotor side converter for active power control and voltage regulation of wind turbine. On grid side PQ control scheme is applied. Wind turbine and its control units are described in details and also for STATCOM control. All power system components are simulated in MATLAB/ SIMULINK software. For studying the performance of controller, different abnormal conditions are applied even the worst case. Simulation results prove that the performance of STATCOM and DFIG control schemes as improving power quality and stability of wind turbine.
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
Automobile Management System Project Report.pdfKamal Acharya
The proposed project is developed to manage the automobile in the automobile dealer company. The main module in this project is login, automobile management, customer management, sales, complaints and reports. The first module is the login. The automobile showroom owner should login to the project for usage. The username and password are verified and if it is correct, next form opens. If the username and password are not correct, it shows the error message.
When a customer search for a automobile, if the automobile is available, they will be taken to a page that shows the details of the automobile including automobile name, automobile ID, quantity, price etc. “Automobile Management System” is useful for maintaining automobiles, customers effectively and hence helps for establishing good relation between customer and automobile organization. It contains various customized modules for effectively maintaining automobiles and stock information accurately and safely.
When the automobile is sold to the customer, stock will be reduced automatically. When a new purchase is made, stock will be increased automatically. While selecting automobiles for sale, the proposed software will automatically check for total number of available stock of that particular item, if the total stock of that particular item is less than 5, software will notify the user to purchase the particular item.
Also when the user tries to sale items which are not in stock, the system will prompt the user that the stock is not enough. Customers of this system can search for a automobile; can purchase a automobile easily by selecting fast. On the other hand the stock of automobiles can be maintained perfectly by the automobile shop manager overcoming the drawbacks of existing system.
Forklift Classes Overview by Intella PartsIntella Parts
Discover the different forklift classes and their specific applications. Learn how to choose the right forklift for your needs to ensure safety, efficiency, and compliance in your operations.
For more technical information, visit our website https://intellaparts.com
Event Management System Vb Net Project Report.pdfKamal Acharya
In present era, the scopes of information technology growing with a very fast .We do not see any are untouched from this industry. The scope of information technology has become wider includes: Business and industry. Household Business, Communication, Education, Entertainment, Science, Medicine, Engineering, Distance Learning, Weather Forecasting. Carrier Searching and so on.
My project named “Event Management System” is software that store and maintained all events coordinated in college. It also helpful to print related reports. My project will help to record the events coordinated by faculties with their Name, Event subject, date & details in an efficient & effective ways.
In my system we have to make a system by which a user can record all events coordinated by a particular faculty. In our proposed system some more featured are added which differs it from the existing system such as security.
Democratizing Fuzzing at Scale by Abhishek Aryaabh.arya
Presented at NUS: Fuzzing and Software Security Summer School 2024
This keynote talks about the democratization of fuzzing at scale, highlighting the collaboration between open source communities, academia, and industry to advance the field of fuzzing. It delves into the history of fuzzing, the development of scalable fuzzing platforms, and the empowerment of community-driven research. The talk will further discuss recent advancements leveraging AI/ML and offer insights into the future evolution of the fuzzing landscape.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
NO1 Uk best vashikaran specialist in delhi vashikaran baba near me online vas...Amil Baba Dawood bangali
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Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
COLLEGE BUS MANAGEMENT SYSTEM PROJECT REPORT.pdfKamal Acharya
The College Bus Management system is completely developed by Visual Basic .NET Version. The application is connect with most secured database language MS SQL Server. The application is develop by using best combination of front-end and back-end languages. The application is totally design like flat user interface. This flat user interface is more attractive user interface in 2017. The application is gives more important to the system functionality. The application is to manage the student’s details, driver’s details, bus details, bus route details, bus fees details and more. The application has only one unit for admin. The admin can manage the entire application. The admin can login into the application by using username and password of the admin. The application is develop for big and small colleges. It is more user friendly for non-computer person. Even they can easily learn how to manage the application within hours. The application is more secure by the admin. The system will give an effective output for the VB.Net and SQL Server given as input to the system. The compiled java program given as input to the system, after scanning the program will generate different reports. The application generates the report for users. The admin can view and download the report of the data. The application deliver the excel format reports. Because, excel formatted reports is very easy to understand the income and expense of the college bus. This application is mainly develop for windows operating system users. In 2017, 73% of people enterprises are using windows operating system. So the application will easily install for all the windows operating system users. The application-developed size is very low. The application consumes very low space in disk. Therefore, the user can allocate very minimum local disk space for this application.
Multi-Objective Wind Farm Design: Exploring the Trade-off between Capacity Factor and Land Use
1. Multi-objective Wind Farm Design
Exploring the Trade-off between Capacity Factor and Land Use
Weiyang Tong, Souma Chowdhury, Ali Mehmani, and Achille Messac
Syracuse University, Department of Mechanical and Aerospace Engineering
10th World Congress on Structural and Multidisciplinary Optimization
May 19-24, 2013, Orlando, Florida
2. Wind Farm Development
2
Wind farm development is an extremely complex process that is affected by
several performance objectives (energy production and cost, etc.)
Most of these factors are strongly coupled in the influence on the performance
objectives
Factors affecting wind
farm performance
Natural factors
(uncontrollable)
Wind shear
Wind speed
& direction
Mean speed Intermittency
Ambient
turbulence …
Design factors
(controllable)
Land
configuration
Land area Farm layout
Turbine
selection
Grid
connection
Energy
storage
…
Early Stage
(up to 3 mon. ~ 3 yr.)
• Wind resource
assessment
• Site selection
• Preliminary feasibility
analysis
Mid Stage
(2 ~ 5 yr.)
• Economics analysis
• Transmission capacity
analysis
• Regulatory framework
• Environmental studies
Late Stage
(up to 25 yr)
• Financing
• Construction
• Operation & Maintenance
3. Research Motivation
3
Owing to the lack of early stage conceptual design
frameworks, wind farm planning is an undesirably time-
consuming process.
Transparency and efficiency are compromised in
conventional wind farm planning due to typical
independent decision making of different factors (e.g.,
wind farm layouts are generally designed for prescribed
land area and nameplate capacity)
Quantitative exploration of the balance between the key
objectives is mostly missing in the state of the art (e.g.,
balance between energy production and land use)
4. Research Objective
4
Develop a Bi-level Wind Farm Trade-off Visualization
framework
Explore the trade-off between the concerned design
objectives: capacity factor – land use
Visualize the trade-off by parametrically translating the
Pareto
5. Outline
5
• Design Objectives
• Wind farm Energy Production (Capacity Factor)
• Land Use (Land Area per MW Installed)
• Bi-level Wind Farm Trade-off Visualization Framework
• Lower-level: Trade-off exploration
• Upper-level: Trade-off visualization
• Numerical Experiment
• Concluding Remarks
6. Wind Farm Energy Production
6
• Wind farm Capacity Factor
𝐶𝐹 =
𝐸𝑛𝑒𝑟𝑔𝑦 𝑝𝑟𝑜𝑑𝑢𝑐𝑡𝑖𝑜𝑛 𝑜𝑣𝑒𝑟 𝑎 𝑡𝑖𝑚𝑒 𝑝𝑒𝑟𝑖𝑜𝑑
𝑃𝑜𝑡𝑒𝑛𝑡𝑖𝑎𝑙 𝑒𝑛𝑒𝑟𝑔𝑦 𝑜𝑢𝑡𝑝𝑢𝑡
• Power Generational model in Unrestricted Wind Farm Layout
Optimization (UWFLO) framework
• Quantify the power generation as a function of incoming wind conditions,
farm layout, and turbine features
Denmark's Horns Rev 1 wind farm
The Wake Effect
7. Land Area per MW Installed (LAMI)
7
• Based on the farm layout, the land use is determined by the Smallest Bounding
Rectangle (SBR) enclosing all turbines
• The actual land area is represented as the buffer zone created by making a 2D
distance away from the SBR
• Turbines are not placed on the boundary of a wind farm
• Avoid “zero” area when minimizing the land area
8. Wind Farm Layout Optimization
8
wind farm layout optimization flowchart
Stop criterion
Reach the best performance?
Evaluate design
objective functions
Trade-off between
design objectives
Adjust the
location of
turbines
Prescribed
conditions
Yes
No
-4000
-3000
-2000
-1000
0
1000
2000
3000
4000
-4000 -3000 -2000 -1000 0 1000 2000 3000 4000
optimal layout of 20 turbines optimal layout of 40 turbines
How many turbines should we install?
9. Bi-level Wind Farm Visualization Framework
9
wind farm layout optimization flowchart
Stop criterion
Reach the best performance?
Evaluate design
objective functions
Trade-off between
design objectives
Adjust the
location of
turbines
Sample
design
factors
Wind
distribution
Initial
boundary
Yes
No
10. Bi-level Wind Farm Visualization Framework
10
Capital
investment
Nameplate
capacity
Turbine
features
11. Numerical Experiment
• Two design objectives:
• Maximize the wind farm capacity factor
• Minimize the Land Area per MW Installed (LAMI)
• Identical turbines are used (GE-1.5 xle)
• Wind data from a site at North Dakota is used
Upper-level: the trade-off between capacity factor and LAMI is parametrically
represented by Nameplate Capacity
Lower-level: multi-objective wind farm layout optimization is performed as a
constrained single objective optimization using Mixed-Discrete Particle Swarm
Optimization (MDPSO) algorithm*
11*: Chowdhury et al., 2013 Struct Multidisc Optim
12. Lower-level: CF-LAMI Trade-off Exploration
12
A set of sample nameplate capacities are generated within the 20 MW – 100 MW range
A square initial region is pre-defined with a size less than 120 hectares per MW installed
The bi-objective optimization is formulated as
Sample #
Nameplate
Capacity
No. of turbines
1 20 13
2 30 20
3 60 40
4 90 60
5 100 67
max 𝐶𝐹, min 𝐴
subject to
𝑔 𝑉 ≤ 0
𝑉 𝑚𝑖𝑛 ≤ 𝑉 ≤ 𝑉𝑚𝑎𝑥
𝑉 = {𝑋1, 𝑋2, ⋯ , 𝑋 𝑁, 𝑌1, 𝑌2, ⋯ , 𝑌𝑁}
13. Applying the Smallest Bounding Rectangle
enclosing all turbines
max 𝑓(𝑉) =
𝐸𝑓𝑎𝑟𝑚
365 × 24 𝑁𝐶
subject to
𝑔1 𝑉 ≤ 0
𝑔2 𝑉 ≤ 0
𝑉 𝑚𝑖𝑛 ≤ 𝑉 ≤ 𝑉𝑚𝑎𝑥
𝑉 = {𝑋1, 𝑋2, ⋯ , 𝑋 𝑁, 𝑌1, 𝑌2, ⋯ , 𝑌𝑁}
Lower-level: CF-LAMI Trade-off Exploration
Solved by: Mixed-Discrete Particle Swarm Optimization 13
Estimated using the power generation model
in UWFLO framework
N is determined by the sample nameplate
capacity
𝑉𝑚𝑎𝑥 and 𝑉 𝑚𝑖𝑛 are set based on the initial
boundary regulated by the allowable land area
𝐸𝑓𝑎𝑟𝑚 = (365 × 24)
𝑗=1
𝑁 𝑝
𝑃𝑓𝑎𝑟𝑚 𝑝∆𝑈∆𝜃
Inter-Turbine Spacing
Land Area Constraint
14. Lower-level: CF-LAMI Trade-off Exploration
14
-4000
-3000
-2000
-1000
0
1000
2000
3000
4000
-4000 -3000 -2000 -1000 0 1000 2000 3000 4000
optimal layout with land area of 180 ha
optimal layout with land area of 900 ha
optimal layout with land area of 3000 ha
Optimal layouts of 20 turbines with different land area constraints
15. Upper-level: CF-LAMI Trade-off Visualization
15
𝐶𝐹 = 𝑎𝐴 𝑏 + 𝑐
Pareto solutions of 13 turbines
Pareto solutions of 20 turbines
Pareto solutions of 40 turbines
Pareto solutions of 60 turbines
Pareto solutions of 67 turbines
Fitted curve for case of 13 turbines
Fitted curve for case of 20 turbines
Fitted curve for case of 40 turbines
Fitted curve for case of 60 turbines
Fitted curve for case of 67 turbines
22.4ha/MW 22.4ha/MW
US Average Land Use:
34ha/MW
16. Concluding Remarks
• A Bi-level Wind Farm Trade-off Visualization framework was proposed for
conceptual design of wind farms.
• The CF-LAMI trade-off was parametrically represented as a function of
nameplate capacity.
• This proposed framework can streamline the wind farm development process,
especially for large-scale wind farm projects, and help wind farm developers to
make efficient and effective decisions.
• Future work
• Use both turbine features (turbine rated power) and nameplate capacity to
parameterize the trade-off curve
• Explore the trade-off, such as Capacity Factor vs. Net Impact on
Surroundings
16
17. Acknowledgement
I would like to acknowledge my research adviser
Prof. Achille Messac, and my co-adviser Prof.
Souma Chowdhury for their immense help and
support in this research.
I would also like to thank my friend and colleague
Ali Mehmani for his valuable contributions to this
paper.
Support from the NSF Awards is also
acknowledged.
17
19. CF Response Surface Obtained
Even if turbines are allowed the same land area per MW installed, a
greater number of turbines (higher nameplate capacity) would lead to
greater wake losses, leading to lower energy production.
A contour plot of the function can provide the “LAMI vs. nameplate
capacity” cutoff curve that corresponds to the threshold CF.
LandAreaperMWinstalled(m2/MW)
Nameplate Capacity (MW)
19
20. 20
Single Wake Test: Comparing Wake Growth
Frandsen model and Larsen model predict
greater wake diameters
Jensen model has a linear expansion
The difference between wake diameters
predicted by each model can be as large as
3D, and it can be larger as the downstream
distance increases
3D
21. 21
Single Wake Test: Comparing Wake Speed
Frandsen model predicts the highest
wake speed
Ishihara model predicts a relatively
low wake speed; however, as the
downstream distance increases, the
wake recovers fast owing to the
consideration of turbine induced
turbulence in this model
22. wind
direction
Numerical Experiments
22
An array-like wind farm with 9 GE 2.5 MW – 100m turbines is considered.
A fixed aspect ratio is selected; the streamwise spacing is ranged from 5D to 20D,
while the lateral spacing is no less than 2D.
The farm capacity factor is given by
Prj: Rated capacity, Pfarm: Farm output
23. 23
Layout-based Power Generation Model
In this power generation model, the induction factor is treated as a
function of the incoming wind speed and turbine features:
U: incoming wind speed; P: power generated, given by the power curve
kg, kb: mechanical and electrical efficiencies, Dj: Rotor Diameter, 𝜌: Air density
A generalized power curve is used to represent the approximate power
response of a particular turbine
𝑈𝑖𝑛, 𝑈 𝑜𝑢𝑡, and 𝑈𝑟: cut-in speed, cut-out speed, and rated speed
𝑃𝑟: Rated capacity, 𝑃𝑛: Polynomial fit for the generalized power curve*
*: Chowdhury et al , 2011
24. 24
Layout-based Power Generation Model
Turbine-j is in the influence of the wake of Turbine-i, if and only if
Considers turbines with differing rotor-diameters and hub-heights
The Katic model* is used to account for wake merging and partial wake
overlap
𝑢𝑗: Effective velocity deficit
𝐴 𝑘𝑗: Overlapping area between Turbine-j
and Turbine-k
Partial wake-rotor overlap *: Katic et al , 1987
25. Mixed-Discrete Particle Swarm Optimization (PSO)
This algorithm has the ability to
deal with both discrete and
continuous design variables, and
The mixed-discrete PSO presents
an explicit diversity preservation
capability to prevent premature
stagnation of particles.
PSO can appropriately address the
non-linearity and the multi-
modality of the wind farm model.
25
26. Lower-level: multi-objective wind farm layout optimization
26
A set of sample nameplate capacity factors is generated
within the 20 MW – 100 MW range
A square initial region is pre-defined, f which size is
less than 110 hectares per MW installed
The bi-objective optimization was solved as a
Constrained single objective optimization
Sample #
Nameplate
Capacity
No. of
turbines
1 20 13
2 30 20
3 60 40
4 90 60
5 100 67
max 𝑓(𝑉) =
𝐸𝑓𝑎𝑟𝑚
365 × 24 𝑁𝐶
subject to
𝑔1 𝑉 ≤ 0
𝑔2 𝑉 ≤ 0
𝑉 𝑚𝑖𝑛 ≤ 𝑉 ≤ 𝑉𝑚𝑎𝑥
𝑉 = {𝑋1, 𝑋2, ⋯ , 𝑋 𝑁, 𝑌1, 𝑌2, ⋯ , 𝑌𝑁}
27. Mid-level: Quantification of Trade-offs between Design Objectives
27
• The wind distribution is unique
• A group of Pareto curves can be obtained from the multi-objective wind farm
layout optimization at the bottom-level
• Based on observation, use an appropriate form of function to fit all the Pareto
curves, for example, a form of power function with 3 coefficients
• Once the global design factors are specified, a trade-off curve between two
objectives can be generated
𝑜𝑏𝑗2
𝑛
= 𝑎(𝑝1, 𝑝2, ⋯ , 𝑝 𝐾)𝑜𝑏𝑗1
𝑛 𝑏(𝑝1,𝑝2,⋯,𝑝 𝑁)
+ 𝑐(𝑝1, 𝑝2, ⋯ , 𝑝 𝐾)
where 𝑛 = 1,2, ⋯ , 𝑁 representing 𝑁 sets of samples of global design factors; and 𝐾 is the total
number of global design factors accounted for