Wind Turbine Layout Research Articles

Clustering-Based Coordinated Control of Large-Scale Wind Farm for Power System Frequency Support

This paper proposes a frequency support control scheme for large-scale wind farm (WF) by considering distributed layout of wind turbines (WT) and wake effects inside the WF. The frequency support of WF is formulated as optimization problems. A novel clustering-based optimization method is proposed to coordinate all the WTs so that the whole WF can work at the optimal operating states. In large-scale WF, number of WTs is numerous. With the proposed method, the WTs are first clustered into groups according to their wind profiles. By dispatching the same control commands to the WTs belonging to the same group, each group can be further seen as one single WT. As a result, the number of the WTs and control variables reduces significantly and hence the optimization problems are highly simplified. A case study is carried out to verify the effectiveness of the proposed method.

A new mathematical programming approach to wind farm layout problem under multiple wake effects

Wind farm layout optimization is one of the challenging problems in the field of renewable energy. In the present study, a new nonlinear mathematical model for layout of wind turbines under multiple wake effects is proposed considering two objective functions separately: maximization of total power production and minimization of cost per power. To incorporate multiple wake effects into the proposed model, Jensen's wake decay model is employed. It was proven that the proposed model has totally unimodularity property and according to this property, relaxation of binary decision variables related with the wind turbine locations makes the model relatively simple to solve. Computational study reveals that results of total power production and cost of power obtained from the proposed model outperform that of the previous studies in the literature on a set of example cases and therefore, can be used to layout more productive wind farms.

Small 500 kW onshore wind farm project in Kribi, Cameroon: Sizing and checkers layout optimization model

For the purpose of providing cheap, affordable and reliable electrical energy to communities in need and power-up new industries and enterprises, a small onshore wind farm with an estimated capacity of 500 kW is designed and studied. With the help of a wind rose, wind resource map, and results from Weibull statistics, a potential site is selected and the wind farm is positioned along Cameroon’s coastline for maximum energy capture. Using the PARK model for wind turbine layout optimization, two different layout patterns (Checkers pattern and column pattern) are studied for the purpose of minimizing the wake effect and thereby, maximizing the output power from the farm. The Checkers model was found suitable as compared to the column model to be used on Grand Batanga, a small locality South of the city of Kribi, Cameroon.

Optimizing layout of wind farm turbines using genetic algorithms in Tehran province, Iran

Installation layout of wind turbines plays a prominent role in the design of every wind farm. Thus, the wind farm layout optimization problem is proposed to maximize the total power output with the minimum cost. In this research, Kahrizak region in Tehran province of Iran is selected as a windy region and its real wind speed data are gleaned. Three different scenarios are also considered, with various number of generations and populations for GA parameters, effective distances, and longitude and latitude distances of turbines from each other. Among these scenarios, the best result is obtained for the one in which the longitudinal distance between turbines is greater than the latitudinal distance. By observing the wind rose of Kahrizak region, it is observed that the dominant wind direction of the region is toward the east and south–east. Therefore, by increasing the longitudinal distance of the turbines from each other, the efficiency can be improved and the turbine layout becomes more realistic. In this case, the efficiency rate and normalized cost of turbines are 89.5% and 37.4, respectively, and also 56 turbines are needed. The amounts of efficiency and power output are very convenient for real wind speed data of a region.

Investigation into the Optimal Wind Turbine Layout Patterns for a Wind Farm in Walvis Bay, Namibia

Due to the unpredictable nature of wind speed and direction, there is a need to optimize the wind turbines placement to extract maximum available wind power at a low cost. Through optimization, best positions of the wind turbines that lead to maximum output are determined. This paper presents an into an optimal Wind Turbine (WT) layout pattern for three Wind Farm (WF) configurations (aligned, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). A Hypothetical WF (2<i>km</i> X 2<i>km</i>) is analyzed based on 2016 Wind data. Result shows that the total power generated from the customized is 863.098 kW, from Genetic Algorithm (GA) layout, the total power generated is 1296.286 kW while from Particle Swarm Optimization (PSO) the total power generated is 1300.668 kW. In comparison to the customized layout, optimization algorithms layouts resulted in a good improvement of the total power generated, GA improved the total power generated by 50.2% while PSO improved the total power generated by 50.7%. Optimization Algorithms layout proved to be efficient as compared to the customized layout because they have fewer power losses. GA and PSO layout have losses of 13.5% and 13.3% respectively, while the customized layout resulted in the most losses which are at 43%. The results from GA and PSO slightly differ, with a small difference in power of 4.4 kW.

Wake and Turbulence Analysis for Wind Turbine Layouts in an Island

There is a big wind energy potential in supplying the power in an island and most of the islands are off-grid. Due to the limited area in island(s), there is need to find appropriate layout / location for wind turbines suited to the local wind conditions. In this paper, we have considered the wind resources data of an island in Trøndelag region of the Northern Norway, situated on the coastal line. The wind resources data of this island have been analysed for wake losses and turbulence on wind turbines for determining appropriate locations of wind turbines in this island. These analyses are very important for understanding the fatigue and mechanical stress on the wind turbines. In this work, semi empirical wake model has been used for wake losses analysis with wind speed and turbine spacings. The Jensen wake model used for the wake loss analysis due to its high degree of accuracy and the Frandsen model for characterizing the turbulent loading. The variations of the losses in the wind energy production of the down-wind turbine relative to the up-wind turbine and, the down-stream turbulence have been analysed for various turbine distances. The special emphasis has been taken for the case of wind turbine spacing, leading to the turbulence conditions for satisfying the IEC 61400-1 conditions to find the wind turbine layout in this island. The energy production of down-wind turbines has been decreased from 2 to 20% due to the lower wind speeds as they are located behind up-wind turbine, resulting in decreasing the overall energy production of the wind farm. Also, the higher wake losses have contributed to the effective turbulence, which has reduced the overall energy production from the wind farm. In this case study, the required distance for wind turbines have been changed to 6 rotor diameters for increasing the energy gain. From the results, it has been estimated that the marginal change in wake losses by moving the down-stream wind turbine by one rotor diameter distance has been in the range of 0.5 to 1% only and it is insignificant. In the full-length paper, the wake effects with wind speed variations and the wind turbine locations will be reported for reducing the wake losses on the down-stream wind turbine. The Frandsen model has been used for analysing turbulence loading on the down-stream wind turbine as per IEC 61400-1 criteria. In larger wind farms, the high turbulence from the up-stream wind turbines increases the fatigues on the turbines of the wind farm. In this work, we have used the effective turbulence criteria at a certain distance between up-stream and down-stream turbines for minimizing the fatigue load level. The sensitivity analysis on wake and turbulence analysis will be reported in the full-length paper. Results from this work will be useful for finding wind farm layouts in an island for utilizing effectively the wind energy resources and electrification using wind power plants.

Optimal placement of wind turbines within a wind farm considering multi-directional wind speed using two-stage genetic algorithm

Most wind turbines within wind farms are set up to face a pre-determined wind direction. However, wind directions are intermittent in nature, leading to less electricity production capacity. This paper proposes an algorithm to solve the wind farm layout optimization problem considering multi-angular (MA) wind direction with the aim of maximizing the total power generated on wind farms and minimizing the cost of installation. A twostage genetic algorithm (GA) equipped with complementary sampling and uniform crossover is used to evolve a MA layout that will yield optimal output regardless of the wind direction. In the first stage, the optimal wind turbine layouts for 8 different major wind directions were determined while the second stage allows each of the previously determined layouts to compete and inter-breed so as to evolve an optimal MA wind farm layout. The proposed MA wind farm layout is thereafter compared to other layouts whose turbines have focused site specific wind turbine orientation. The results reveal that the proposed wind farm layout improves wind power production capacity with minimum cost of installation compared to the layouts with site specific wind turbine layouts. This paper will find application at the planning stage of wind farm.

An experimental investigation on the aeromechanics and wake interferences of wind turbines sited over complex terrain

An experimental study was conducted to investigate the aeromechanics and wake interferences of wind turbines sited on two-dimensional hills with different slopes. First, detailed flow field measurements were correlated with dynamic wind load measurements to reveal the effect of topography on the performance of an individual wind turbine sited at different locations on hilly terrains. Compared to a flat surface, wind turbines sited on a hilltop not only generate more power due to the speed-up effect but also experience reduced fatigue loads due to the decreased level of the turbulence on a hilltop. It was also found that a wind turbine located downstream of a steep hill has a greater likelihood of experiencing extreme wind loads compared to one on a gentle hill. Second, wind turbine wake characteristics and their effects on the dynamic wind loads of downstream wind turbines were also assessed. The effect of an upstream turbine wake on the wind turbine sited on a hilltop was found to be much less significant compared to a wind turbine on a flat surface. In addition, while the wake of an upstream turbine sited on hilltop has a significant influence on the dynamic wind loads of a downstream turbine sited behind a gentle hill, the effect of an upstream turbine wake on a downstream turbine placed behind a steep hill was found to be almost negligible. The quantitative measurement results of the present study not only provide a database for the validation of wake models and numerical simulations but can also be used to optimize the layout of wind turbines sited on complex terrain for higher power yield and better durability.

Decomposition based multi-objective optimization to simultaneously determine the number and the optimum locations of wind turbines in a wind farm

Wind energy is one of the most progressive types of renewable energies deployed using wind turbines, which convert wind power into electrical energy. Various methodologies and layouts have been proposed to determine the optimal layout (numbers and locations) of wind turbines inside a wind farm to extract maximum energy. However, limited availability of land area has resulted in the construction of these wind farms near to human habitats causing a negative impact on human health. Compared to other factors, noise has become an immense concern for wind farm designers, as it needs to be constrained within the mandatory limits. Using a well-established wake model and ISO-9613-2 noise calculation, this study performs a wind farm layout optimization (WFLO) based on a multi-objective trade-off between minimization of noise propagation and maximization of energy generation. A novel hybrid methodology is proposed as a combination of proposed probabilistic variable decomposed multi-objective evolutionary algorithm (VdRBNSGA-II) and a newly developed gradient-based non-dominated normalized normal constraint (nD-NNC) method. As opposed to previous works, generated non-dominated front provides various alternative energy-noise solutions along with an additional information on turbine layouts, which allows a decision maker to select among different competing solutions based on the existing noise and other regulations.

Wind farm wake modeling using NWTC design codes

Wind turbines located in a wind farm are subject to a wind field that is substantially modified when compared to the ambient wind field due to wake effects. It is clear that in addition to the power, a tool that could model the loads of the waked turbine is needed. In this work, a wind farm wake model is created using a single wake model based on the dynamic wake meandering model to systematically model the wake effects and thus the power and loads of an entire wind farm with an arbitrary wind turbine layout and wind condition. This wind farm wake model is incorporated into the National Wind Technology Center design codes. Preliminary results demonstrate that this wind farm tool yields satisfactory results when compared with Simulator for Wind Farm Applications, a high fidelity computational fluid dynamics Large eddy simulation model, and with experimentally-obtained field data. The integration of the wind farm model with the National Wind Technology Center design codes is described in detail and validation results of the tool are provided.

Optimizing the number and locations of turbines in a wind farm addressing energy-noise trade-off: A hybrid approach

Micro-siting is an optimal way of placing turbines inside a wind farm while considering various design objectives and constraints. Using a well-established Jensen wake model and ISO-9613-2 noise calculation, this study performs a wind farm layout optimization based on a multi-objective trade-off between minimization of the noise propagation and maximization of the energy generation. A novel hybrid methodology is developed which is a combination of probabilistic real-binary coded multi-objective evolutionary algorithm and a newly proposed deterministic gradient based non-dominated normalized normal constraint method. Based on the Inverted Generational Distance metric, the performance of the proposed method is found to be better than the conventional normalized normal constraint method or the concerned evolutionary method alone. Moreover, in contrast to the previous studies, the generated non-dominated front is capable of providing a trade-off between various alternative energy-noise solutions, along with an additional information about the corresponding turbine numbers and their optimal location coordinates. As a result, the decision maker can choose from different competing wind turbine layouts based on existing noise and other standard regulations.

An integrated decision support model for design and operation of a wind-based hydrogen supply system

We aim to develop a new optimization-based approach for the strategic planning of a renewable hydrogen supply system using onshore and offshore wind energy. To achieve this goal, we develop an optimization model to design and analyze a wind-powered hydrogen supply (WPHS) system using a mixed-integer linear programming technique. In this model, we include decision variables to account for a wide range of issues regarding the proposed system, from the technical selection of the wind turbine and wind farm layout design to strategies for onshore/offshore wind farm and hydrogen supply network development. To illustrate the capability of the proposed approach, we present a case study pertaining to the design of the WPHS system for the road transportation sector of Jeju Island, Korea. Using the proposed approach, we are able to i) identify the configuration and operational practices of an optimal WPHS system, ii) analyze the cost distribution and major cost drivers through economic evaluation, and iii) provide decision-making guidance to stakeholders and policy makers for planning an economically viable and a sustainable hydrogen supply system using various policies to encourage offshore wind farm development.

Wind turbine layout optimization with multiple hub height wind turbines using greedy algorithm

Wind turbine layout optimization in wind farm is one of the most important technologies to increase the wind power utilization. This paper studies the wind turbine layout optimization with multiple hub heights wind turbines using greedy algorithm. The linear wake model and the particle wake model are used for wake flow calculation over flat terrain and complex terrain, respectively. Three-dimensional greedy algorithm is developed to optimize wind turbine layout with multiple hub heights for minimizing cost per unit power output. The numerical cases over flat terrain and complex terrain are used to validate the effectiveness of the proposed greedy algorithm for the optimization problem. The results reveal that it incurs lower computational costs to obtain better optimized results using the proposed greedy algorithm than the one using genetic algorithm. Compared to the layout with identical hub height wind turbines, the one with multiple hub height wind turbines can increase the total power output and decrease the cost per unit power output remarkably, especially for the wind farm over complex terrain. It is suggested that three-dimensional greedy algorithm is an effective method for more benefits of using wind turbines with multiple hub heights in wind farm design.

Optimization of wind turbine layout position in a wind farm using a newly-developed two-dimensional wake model

The development and validation of a 2D analytical wind turbine wake model based on Jensen’s wake model using Gaussian function is presented in this paper. The velocity deficit predicted by the newly-developed Jensen–Gaussian wake model is compared with wind tunnel experimental measured data in literatures and results show that, the velocity deficit predicted by the model fits well with the measured data at different downwind distances of X=2.5D, X=5D, X=7.5D and X=10D. Considering the turbulence inside the turbine wake, a new turbulence model is developed and based on this, the Jensen–Gaussian wake model was improved and validated. The 2D Jensen–Gaussian wake model is then applied in the wind turbine layout optimizing process within a wind farm based on the multiple populations genetic algorithm (MPGA). The performance of this newly 2D model in the optimization process is validated and compared with the results presented in some typical studies on the turbine layout optimization. The comparison is performed for ‘constant wind speed of 12m/s with variable wind directions’. Using the 2D Jensen–Gaussian wake model instead of Jensen’s wake model in the MPGA turbine layout optimization program, both the total power generation and wind farm efficiency decreased. The wind farm efficiency drop to 77.83%, 78.47% and 81.84% from 96.83%, 96.34% and 96.23% for 38, 39 and 40 wind turbines, respectively which is in accordance with the literatures on the power losses caused by wake effect in large wind farm. The development and application of the 2D Jensen–Gaussian wake model means moretheorysignificance and practicalvalues in wind energy utilization.

The decision model of 3-dimensional wind farm layout design

This research investigates the maximization of the expected wind farm power output through optimizing the layout of wind turbines as well as their heights. A model for determining the wind farm layout in 3-dimensional (3-d) space is introduced. The objective of the layout design is to maximize the expected wind farm power output. To estimate the wind deficit in 3-d space, a linear 3-d wake loss model is developed and a criterion for identifying wakes affecting on a targeted wind turbine is constructed. The proposed model is complex and, therefore, solved by an evolutionary strategy algorithm. A comparative analysis of the proposed model and a benchmark model is conducted. In the computational study, the design solution offered by the proposed model indicates that the expected wind farm power output could be increased by choosing wind turbines with different heights.

Wind turbine layout optimization using multi-population genetic algorithm and a case study in Hong Kong offshore

This paper proposes a multi-population genetic algorithm (MPGA) program for wind turbine layout optimization in a wind farm which aims at extracting the maximum power in a minimum investment cost. The MPGA program is applied to a 2km×2km wind farm considering three different wind scenarios, i.e. (a): constant wind speed of 12m/s with fixed wind direction; (b): constant wind speed of 12m/s with variable wind direction and (c): variable wind speed of 8m/s, 12m/s, 17m/s with variable wind directions. Compared with previous studies, the results of power generation cost of energy and wind farm efficiency are improved in the paper using MPGA which validate that MPGA works effectively in wind turbine layout optimization with wind farm. Additionally, a case study of wind turbine layout configuration op1timization using the MPGA program in a hypothetical offshore wind farm located in Hong Kong southeastern water is attempted using the 20 years׳ wind data from 1992 to 2011. The optimization result is beneficial for the assessment of the offshore wind power potential in Hong Kong. It is indicated that, under the optimization of wind turbines layout in the wind farm, 39.14×108kWh/year electricity can be converted from the offshore wind farm, which occupies 9.09% of the 2012 electricity consumption in Hong Kong. Results show that the multi-population genetic algorithm program can be applied in any real-world wind farms for wind turbine layout optimization when the wind farm boundary is determined as well as the local wind condition is obtained.

Optimal Wind Turbines Micrositing in Onshore Wind Farms Using Fuzzy Genetic Algorithm

With the fast growth in the number and size of installed wind farms (WFs) around the world, optimal wind turbines (WTs) micrositing has become a challenge from both technological and mathematical points of view. An appropriate layout of wind turbines is crucial to obtain adequate performance with respect to the development and operation of the wind power plant during its life span. This work presents a fuzzy genetic algorithm (FGA) for maximizing the economic profitability of the project. The algorithm considers a new WF model including several important factors to the design of the layout. The model consists of wake loss, terrain effect, and economic benefits, which can be calculated by locations of wind turbines. The results demonstrate that the algorithm performs better than genetic algorithm, in terms of maximum values of net annual value of wind power plants and computational burden.

Binary-real coding genetic algorithm for wind turbine positioning in wind farm

In this paper, the wind turbine positioning problem is discussed. Binary-real coding method that combines binary coding method with real coding method is proposed. Wind turbine layout is optimized based on the linear wake model and genetic algorithm, with the target of minimizing the cost per unit power output or maximizing the profit of wind farm. Turbine power curve model with power control mechanisms is used and Weibull distribution is employed to describe the wind conditions. Two cases with simple models and a case with realistic models are used to test the present method. The results show that the new coding method inherits the advantages of both methods. The positions and the number of turbines are adjusted in the evolution process. The proposed coding method obtains the best number of wind turbines and the optimized layouts. It is an effective solution strategy for wind turbine positioning problem.

Investigation into the optimal wind turbine layout patterns for a Hong Kong offshore wind farm

Optimal WT (wind turbine) layout patterns relate in detail to the specific conditions of OWF (offshore wind farm) environments and therefore each is different. This paper presents an investigation into optimal WT layout patterns for three OWF configurations (aligned, staggered, scattered) in HK (Hong Kong) waters. A hypothetical OWF (6930 m × 9072 m) are analysed based on twenty years of wind data (1992–2011). For the aligned and staggered WFs, different WT layout separations are studied. The separations varied between 5.0D and 15.0D along the PWD (prevailing wind direction) and 5.0D to 12.0D in the CWD (crosswind direction), where D is the WT rotor diameter. A range of 25 and 45 WTs are placed in the scattered WF, with their layout optimized using the Multi-Population Genetic Algorithm. WF performance is reported for the best ten layout patterns following studies of many different layouts. Results show for this hypothetical OWF, that the optimal WT separation is 14.5D in the PWD and 11.0D in the CWD for the aligned and staggered cases. Thirty WTs are recommended as the optimum number for the scattered WF. The LCOE (levelized costs of energy) were calculated in HK$ terms 1.474/kWh (aligned), 1.467/kWh (staggered), and 1.290/kWh (scattered). APG (annual energy generation) is determined to be 40.80 × 108 kWh (aligned), 40.42 × 108 kWh (staggered), and 33.98 × 108 kWh (scattered), representing 9.48% (aligned), 9.39% (staggered), and 7.89% (scattered) of the annual electricity consumption for HK in 2012. The approach presented can be regarded as a generic method for WT layout optimization.

Optimization of the Wind Turbine Layout and Transmission System Planning for a Large-Scale Offshore WindFarm by AI Technology

The interest in the utilization of offshore wind power is increasing significantly worldwide. A typical offshore windfarm may have hundreds of generators, which is outspread in the range of several to tens of kilometers. Therefore, thereare many feasible schemes for the wind turbine location and internal line connection in a wind farm. The planner mustsearch for an optimal one from these feasible schemes, usually with a maximum wind power output and the lowestinstallation and operation cost. This paper proposes a novel procedure to determine the optimization wind turbinelocation and line connection topology by using artificial intelligence techniques: The genetic algorithm is utilized inthe optimal layouts for the offshore wind farm, and the ant colony system algorithm is utilized to find the optimal lineconnection topology. Furthermore, the wake effect, real cable parameters, and wind speed series are also considered inthis research. The concepts and methods proposed in this study could help establish more economical and efficientoffshore wind farms in the world.