A stochastic DEVS wind turbine component model for wind farm simulation

PurposeThe rapid rising of renewable energy sources particularly wind energy cannot be ignored. The numerical increase in wind energy farms throughout the world is the best example. The purpose of this paper is to assess the basic question of whether wind characteristics affect the performance and cost of energy. The importance of this question cannot be ruled out while comparing renewable energy to a conventional form of energy more specifically especially for the developing country where the cost of energy is very high.Design/methodology/approachThe research design of this paper is consists of an assessment of local wind characteristics of the wind farm site using Weibull k and c parameters. The performance model is used to assess the performance of the wind turbine (WT) corresponding to local wind characteristics. The wind correlation with WT in terms of changing wind speed has been assessed to quantify the effects of wind speed on the WT behavior and failure of WT components. Similarly, the power curve of WT is assessed and compared with the International Electrotechnical Commission standards 61400-12-2. The WT power coefficient and tip speed ratio corresponding to wind speed is also investigated. The energy volume and cost of energy lost model is used to determine the cost and volume loss of energy/kWh of the wind farm.FindingsThe findings of practical wind farms showed that the wind conditions of the site are showing a strong tendency that can be determined from the results of Weibull k and c parameters. The k and c parameters are observed to be 3.44 and 9.16 m/s, respectively, for a period of a year. The standard deviation is observed to be 2.56 for a period of a year. WT shows the efficient behavior can be obtained from the power coefficient and tip speed of WT at different wind speeds. Also, wind farm observation showed that to be some increasing wind speed cause of based WT component failures. The results of energy volume and cost/kWh assessment showed that the major portion of energy volume and cost of energy is lost owing to network, voltage dip and frequency surge, electrical and mechanical components failures.Originality/valueGenerally, it can be concluded that the WTs are now able to cope with variable wind speeds. However, the results of this paper are showing that WT performance and availability decreased due to increased wind speeds. It can also be a reason to decreased volume and increase the cost of energy/kWh.

<strong class="journal-contentHeaderColor">Abstract.</strong> As many coastal regions experience a rapid increase in offshore wind farm installations, inter-farm distances become smaller with a tendency to install larger turbines at high capacity densities. It is however not clear how the wake losses in wind farm clusters depend on the characteristics and spacing of the individual wind farms. Here, we quantify this based on multiple COSMO-CLM simulations, each of which assumes a different, spatially invariant combination of the turbine type and capacity density in a projected, future wind farm layout in the North Sea. An evaluation of the modelled wind climate with mast and lidar data for the period 2008&ndash;2020 indicates that the frequency distributions of wind speed and wind direction at turbine hub height are skillfully modelled and the seasonal and inter-annual variations in wind speed are represented well. The wind farm simulations indicate that at a capacity density of 8.1 MW km^-2 and for SW-winds, inter-farm wakes can reduce the capacity factor at the inflow edge of wind farms from 59 % to between 55 % and 40 % depending on the proximity, size and number of the upwind farms. However, the long-term impact of wake losses in and between wind farms is mitigated by adopting next-generation, 15 MW wind turbines instead of 5 MW turbines, as the annual energy generation over all wind farms in the simulation is increased by 24 % at the same capacity density. In contrast, the impact of wake losses is exacerbated with increasing capacity density, as the layout-integrated, annual capacity factor varies between 54.4 % and 44.3 % over the considered range of 3.5 to 10 MW km^-2. Overall, wind farm characteristics and inter-farm distances play an essential role in cluster-scale wake losses, which should be taken into account in future wind farm planning.

<strong class="journal-contentHeaderColor">Abstract.</strong> As many coastal regions experience a rapid increase in offshore wind farm installations, inter-farm distances become smaller with a tendency to install larger turbines at high capacity densities. It is however not clear how the wake losses in wind farm clusters depend on the characteristics and spacing of the individual wind farms. Here, we quantify this based on multiple COSMO-CLM simulations, each of which assumes a different, spatially invariant combination of the turbine type and capacity density in a projected, future wind farm layout in the North Sea. An evaluation of the modelled wind climate with mast and lidar data for the period 2008&ndash;2020 indicates that the frequency distributions of wind speed and wind direction at turbine hub height are skillfully modelled and the seasonal and inter-annual variations in wind speed are represented well. The wind farm simulations indicate that at a capacity density of 8.1 MW km^-2 and for SW-winds, inter-farm wakes can reduce the capacity factor at the inflow edge of wind farms from 59 % to between 55 % and 40 % depending on the proximity, size and number of the upwind farms. However, the long-term impact of wake losses in and between wind farms is mitigated by adopting next-generation, 15 MW wind turbines instead of 5 MW turbines, as the annual energy generation over all wind farms in the simulation is increased by 24 % at the same capacity density. In contrast, the impact of wake losses is exacerbated with increasing capacity density, as the layout-integrated, annual capacity factor varies between 54.4 % and 44.3 % over the considered range of 3.5 to 10 MW km^-2. Overall, wind farm characteristics and inter-farm distances play an essential role in cluster-scale wake losses, which should be taken into account in future wind farm planning.

Cluster-based wind turbine maintenance prioritization for a utility-scale wind farm

Strategy for wind power plant contribution to frequency control under variable wind speed

During recent years, wind energy has moved from an emerging technology to a nearly competitive technology. This fact, coupled with an increasing global focus on environmental concern and a political desire of a certain level of diversification in the energy supply, ensures wind energy an important role in the future electricity market. For this challenge to be met in a cost-efficient way, a substantial part of new wind turbine installations is foreseen to be erected in big onshore or offshore wind farms. This fact makes the production, loading and reliability of turbines operating under such conditions of particular interest.

Researching on interaction between wind farm and power system, it usually uses simple model to describe the wind farm. The wind turbines in a wind farm always suppose as a coherent generator group, every wind turbines have identical wind speed. As wind turbine locates in the different site in the wind farm, the wind speed at each wind turbines will not change simultaneously. Wake effect is another factor that should take into consideration. So, it needs to get wind speed equivalent model. The equivalent model of wind speed in the wind farm is based on output of power. Gust wind and ramp wind are discussed. Wind farm will change gust wind into step varying wind and make ramp wind into different type wind. The arrangement of wind turbines will also affect equivalent model of wind speed. The result show that the length of wind farm will smooth the power output. It explains why the power output fluctuation of wind farm is smaller than single wind turbine. The onshore wind farm located in Dongtai Jiangsu province is proposed as an example. The equivalent wind speed model and equivalent impedance connected to grid are calculated. The results illustrate the impact of wind speed model on wind farm output.

This study analyzes wind structures up to 509 m in the atmospheric boundary layer in the coastal area of Hainan Island, using a dataset obtained from ultrasonic anemometers housed in three towers. The wind profile, consisting of the measurements from the three towers, followed logarithmic law. In a diurnal variation, the maximum wind speed occurred at night, with a greater component of northerly wind, while the minimum wind speed was observed at noon, with a greater component of easterly wind. The variation in wind speed suggests that the measurements were representative of the wind field in the upper part of the atmospheric boundary layer, and the variation in wind direction might be affected by sea and land breezes, which can be induced by the different thermal conditions of underlying surfaces. The diurnal variation in average wind speed ranged from 0.5 to 1.5 m s−1, and the diurnal variation in wind direction was 10–20 degrees. In our measurements, the diurnal trajectory of the wind vector was observed to be counterclockwise, which differs from previous studies conducted over uniform and flat underlying surfaces. This is partially due to the different thermodynamic conditions of the underlying land and sea surfaces. The impact of topographic relief on wind measurement is also discussed. The measurements suggest that wind speeds at altitudes above 50 m are less influenced by terrain. The height of the reversal layer, which is generated by the different diurnal variations in wind speed in the upper and lower parts of the boundary layer, was estimated to be around 300 m.

This paper presents an improved approach to assess wind turbine reliability considering the impact of hub levels using Monte Carlo simulation. The hub level of wind turbine affects wind turbine reliability in two aspects: 1) different hub levels are subject to different wind speeds, which produce different wind power outputs; 2) due to the correlation between wind speeds and turbine failure rates, wind turbines with different hub levels have different rates of failure. Therefore, it is important to consider the wind turbine's height, particular hub levels, when evaluating wind turbine reliability. The purpose of this paper is to develop a model to evaluate wind farm reliability while considering the effect of hub levels on wind turbine output and on wind turbine failure rates. The proposed approach is tested on the IEEE RTS-79 system with relevant modifications using probabilistic simulation. The efficacy of the approach is verified by evaluating reliability indexes of wind farms when the hub levels of wind turbines are taken into account.

Expert System of Fault Diagnosis for Gear Box in Wind Turbine

NREL is the only organization in the US that is accredited to conduct tests of wind turbine systems and components in accordance with International Electrotechnical Commission (IEC) test standards. The National Wind Technology Center houses the only full-scale wind turbine blade drivetrain test facilities in the US. To maintain these facilities, accreditation, and the acceptance of the wind industry, NREL must strive continuously to maintain and improve staff capabilities, procedures, facility and equipment. In addition, as wind turbines continue to increase in size, NREL must improve its capability to test the larger components associated with these new turbines. NREL is currently investigating methods to increase blade and drivetrain testing capabilities. Mitsubishi Power Systems (MPS) must also strive to meet the demands of the evolving wind energy industry by developing, proving and manufacturing new, cost-effective blades for its turbines. Mitsubishi has established a joint venture with TPI composites of Rhode Island for manufacturing blades to be installed on US wind turbines. There is an obvious advantage to test these prototype blades in a US facility. The NREL blade test facility is uniquely capable of fulfilling this need. In addition, NREL can provide other testing support to MPS through the use of its dynamometer facility for drivetrain testing and through the use of NREL’s field-testing capabilities for testing wind turbine systems. This CRADA provides the opportunity for both organizations to achieve critical goals in development of wind energy in the US. MPS obtains verification of its wind turbine components and system. NREL obtains improved test capabilities.

PurposeThe purpose of this paper is to analyze wind characteristics and their effects on wind turbine components and energy generation at the candidate site.Design/methodology/approachThe methodology covered the detailed investigation of wind characteristics using Weibull k and c parameters and standard deviation at 30 m above the ground level (AGL). The wind shear coefficient and air density were also studied. The weight model was developed to determine the effects on wind turbine components and energy generation. At last, an economic assessment was carried out to determine the pre- and post-effects of the weight model on the cost of energy per kilowatt-hour.FindingsThe mean standard deviation, Weibull k parameter and Weibull c parameter were found to be 2.157, 2.617 and 6.087 m/s, respectively, at 30 m for a period of a year. The mean wind shear coefficient was found to be 0.176 for a year. The calculated results showed that site-specific midrange and amplitude force were 40.95 per cent and 37.75 per cent on wind turbine mechanical components, respectively. The average rise in force and drop in energy was found to be 35.50 per cent and 47.55 per cent, respectively. The lift coefficient, drag coefficient and pitching moment considering values (a, 0.1 and 0.2) showed an increase in force on wind turbine components that resulted in a drop in energy. The cost assessment results showed that the cost of energy was increased from US$0.032/kWh to 0.0466/kWh for wind turbine A.Practical implicationsAn accurate determination of the weight factor is necessary for near-reality assessment of wind energy yield and rise of force on the wind turbine. The results paved the way for site-specific design optimization of wind turbines.Originality/valueThe study contributes to the site-specific wind characteristic-based weight model to determine the effects of wind loads on wind turbine components and energy generation and compared with the specified design standard. The lift coefficient, drag coefficient and pitching moment coefficient show a rise in the force while considering the weight factor values. The results show that the site has the potential to generate energy at the lowest cost per kilowatt-hour, but it needs wind turbine design adjustments according to site-specific wind characteristics. If site-specific wind characteristics are considered, it would lead to maximum energy generation and high reliability of wind turbine components.

As wind energy adoption is growing, ensuring the efficient operation and maintenance of wind turbines becomes essential for maximizing energy production and minimizing costs and downtime. Many AI applications in wind energy, such as in condition monitoring and power forecasting, may benefit from using operational data not only from individual wind turbines but from multiple turbines and multiple wind farms. Collaborative distributed AI which preserves data privacy holds a strong potential for these applications. Federated learning has emerged as a privacy-preserving distributed machine learning approach in this context. We explore federated learning in wind turbine condition monitoring, specifically for fault detection using normal behaviour models. We investigate various federated learning strategies, including collaboration across different wind farms and turbine models, as well as collaboration restricted to the same wind farm and turbine model. Our case study results indicate that federated learning across multiple wind turbines consistently outperforms models trained on a single turbine, especially when training data is scarce. Moreover, the amount of historical data necessary to train an effective model can be significantly reduced by employing a collaborative federated learning strategy. Finally, our findings show that extending the collaboration to multiple wind farms may result in inferior performance compared to restricting learning within a farm, specifically when faced with statistical heterogeneity and imbalanced datasets.

In complex-terrain wind farms (WFs), a serious uneven distribution of fatigue loads increases the operation and maintenance cost of wind power generation. Therefore, this study proposes a comprehensive optimization for fatigue loads of wind turbine (WT) components. The optimization is performed at two levels: WT and WF. The central controller of WF redispatches the active power reference for each WT, and the local controller of WT is performed to appropriately reduce the generator speed. For WT level, a variance-based sensitivity analysis is performed to assess the effect of generator speed and active power on component fatigue loads. The analysis results show a high sensitivity of all fatigue loads of components to the generator speed, while the load sensitivity of blade (Mz) and tower (My) to active power is considerably high. For WF level, a novel Multi-objective adaptive Yin-Yang pair optimization algorithm is proposed to coordinatively optimize fatigue load distribution and active power dispatch. The Pareto Front of optimized fatigue load deviations is obtained for the target components, which correspond to a series of optimization solutions. Then, weighted analysis is employed to evaluate the optimal solutions, and the solution with the smallest weighted value is selected as the appropriate solution. Finally, the simulation test using the real data from the complex-terrain WF is conducted, and the comparison results confirm the effectiveness of the proposed optimization approaches.

This study explores the smoothing effect on the uncertainty of the wind power fluctuations that affect a power system at points of common coupling. Set pair analysis is applied to evaluate the similarity between the power fluctuations of a single wind turbine and those of all aggregated wind turbines, based on which a quantitative index describing the wind power smoothing effect is proposed. The smoothing effect characteristics of a wind farm cluster are investigated for different numbers of wind turbines, different wind speeds, different seasons, and multiple sampling intervals. A significant smoothing effect is usually observed at a shorter sampling interval and a higher wind speed, and the smoothing effect index increases with an increase in the numbers of wind turbines and farms. Additionally, the correlation between the smoothing effect of aggregated wind farms and the forecast accuracy for the corresponding aggregated power output is examined. The experimental results indicate that the wind power forecast accuracy varies with the smoothing effect index, which is influenced by the number of wind farms. Furthermore, the aggregated output from a wind farm cluster with a higher smoothing effect index exhibits better forecasting performance than that from a single wind farm, showing that the trend of the wind power series becomes smoother due to the smoothing effect, thus enabling one-step-ahead wind power forecasting with higher accuracy.