Energy Production Of Wind Turbines Research Articles

PID and Fuzzy Logic Optimization of the Pitch Control of Wind Turbines

In the future, wind turbines may become an important source of energy generation because of their scalability and ability to be constructed in most places on Earth. To meet net-zero greenhouse gas emissions by 2050, emissions need to be reduced globally. This can be achieved in part by making renewable energy cheaper than fossil fuels. The energy production of wind turbines may be increased by integrating a smart controller that can optimize the pitch angles of the turbine. A smart controller would also reduce maintenance and servicing costs while increasing the turbine’s functional lifespan. By integrating proportional-integral-derivative (PID) and fuzzy logic controllers, which are used in many industrial control systems, the pitch control of a wind turbine could be optimized to generate the most electricity while avoiding overshoot. We hypothesized that the fuzzy-PID controller, which uses both fuzzy and PID controller algorithms, would be the most optimal for electricity generation by having the shortest settling and rise time with very little overshoot and settling error. We modeled a proportional-integral (PI) controller, a proportional-derivative (PD) controller, a PID controller, 7 and 9 membership function fuzzy controllers, and a fuzzy-PID controller in Simulink. In general, we observed that fuzzy logic-based controllers rose and settled slower than PID-based controllers with less overshoot and steady-state error. Overall, the PID controller had a fast rise time with little overshoot and appeared to be the most optimal for electricity generation, but the PI controller provided the best life span for the wind turbine by having zero overshoot with a slightly slower rise time.

Predicting wind turbine energy production with deep learning methods in GIS: A study on HAWTs and VAWTs

The increasing global demand for renewable energy necessitates accurate forecasting methods to optimize wind energy production, particularly in regions with varying climatic conditions. This study addresses this need by utilizing advanced deep learning techniques and Geographical Information Systems (GIS) to estimate the energy output of wind turbines. Specifically, it focuses on predicting the energy production of both horizontal axis wind turbines (HAWTs) and vertical axis wind turbines (VAWTs) using a combination of Markov and Cellular Automata-Markov (CA-Markov) models, alongside deep learning methods such as long short-term memory (LSTM), LSTM-Wavelet, and Support Vector Regression (SVR). Additionally, the study evaluates the energy output of each turbine type, factoring in their construction costs within the study area. The analysis reveals significant variations in energy output over time, with maximum values increasing from 85,017 Wh in 2000 to 166,050 Wh in 2020 in the northern region, while minimum outputs also rose significantly. Projections for 2030 suggest that approximately 17% of the northern region experience a substantial increase in wind power potential. Among the forecasting methods, the LSTM-Wavelet hybrid model demonstrated superior accuracy, surpassing the 90% threshold, primarily due to its effective handling of data instability and noise reduction. This study underscores the potential of using sophisticated modeling techniques to enhance wind energy forecasting, contributing to more efficient energy management in regions with high energy demand and limited resources.

Assessing the impact of waves and platform dynamics on floating wind-turbine energy production

Abstract. Waves have the potential to increase the power output of a floating wind turbine by forcing its rotor to move against the wind. Starting from this observation, we use four multi-physics models of increasing complexity to investigate the role of waves and platform movements in the energy conversion process of four floating wind turbines of 5–15 MW in the Mediterranean Sea. Progressively adding realism to our simulations, we show that large along-wind rotor movements are needed to increase the power output of a floating wind turbine; however, these are prevented by the current technology of spar and semi-submersible platforms. Wind turbulence is the main cause of power fluctuations for the four floating wind turbines we examined and is preponderant over the effect of platform motions due to waves. In a realistic met-ocean environment, the power curve of the floating wind turbines we studied is lower than that obtained with a fixed foundation, with reductions in the annual energy production of 1.5 %–2.5 %. The lower energy production is mainly ascribed to the platform mean tilt, which reduces the rotor's effective area.

Opensource machine learning metamodels for assessing blade performance impairment due to general leading edge degradation

Blades leading edge erosion can significantly reduce annual energy production of wind turbines. Accurate estimates of the resulting blade performance impairment are paramount to predict the resulting energy losses and enable cost-informed decisions on optimal maintenance and operational strategies, maximizing energy production and reducing maintenance costs. Computational Fluid Dynamics (CFD) is a robust approach for predicting the performance losses due to LEE. However, the impact of the damage on blade aerodynamics varies depending on damage pattern, extent and location. Therefore, direct CFD simulation of a sufficiently general set of damaged blades is computationally not viable in industrial applications, since the energy loss assessment needs to be performed for hundreds of turbines at many times of the wind farm operation. To address this issue, previous studies showed how CFD can be used to train machine learning metamodels of the perfomance of damaged blade sections, enabling the definition of multi-fidelity energy loss prediction systems. This study presents improved metamodels, using validated CFD to generate training datasets that cover a more general and wider range of erosion patterns, from low-amplitude roughness to severe grooves. In order to provide the industry with additional erosion geometry-linked tools for estimating energy yield losses, and foster further research and development in this area, the developed meta-models have been made available online with unrestricted access.

Application of passive and active scenarios to a residential building in a dry and hot climate to achieve a positive energy building (PEB)

Designing a nearly zero energy building (NZEB) and more exclusively a positive energy building (PEB) hold significant importance these days. Achieving this objective requires an effective blend of renewable systems and the energy efficient envelope. This study focuses on a villa situated in the southeast of Kerman, Iran with hot and dry summers. Commencing with the current energy-efficient design of the structure, the research conducted various analyses to explore the impacts of different strategies or alternative approaches that contribute to achieving a NZEB or PEB. The parameters examined in the analysis that pertain to the building envelope, encompass insulation scenarios, window-to-wall ratio, and glass types as passive strategies, along with the integration of wind turbine energy production as an active energy solution. The findings indicate that, under hot and dry conditions in this region, the optimal passive strategies for the case study involve a 40 % window-to-wall ratio, polyurethane insulation, and triple-layered glass with argon. This combination results in a noteworthy reduction in total annual energy consumption, decreasing from 24,344.56 kWh to 11,909.64 kWh. To offset the remaining energy requirements and transform the building into a positive energy structure, a wind turbine was employed. This integrated approach not only aligns with the specific climatic conditions of the area but also demonstrates the feasibility of achieving a positive energy building which resulted in a surplus energy of 1,414.13 kWh.

Influence of Atmospheric Stability on Wind Turbine Energy Production: A Case Study of the Coastal Region of Yucatan

Wind energy production mainly depends on atmospheric conditions. The atmospheric stability can be described through different parameters, such as wind shear, turbulence intensity, bulk Richardson number, and the Monin–Obukhov length. Although they are frequently used in micrometeorology and the wind industry, there is no standard comparison method. This study describes the atmospheric stability of a coastal region of Yucatan, Mexico, using these four parameters. They are calculated using six-month data from a meteorological mast and a marine buoy to determine atmospheric stability conditions and compare their results. The unstable atmospheric condition was predominant at the site, with an 80% occurrence during the measurement period, followed by 12% in neutral and 6% in stable conditions. Wind speed estimations were performed for each atmospheric stability scenario, and the variation in the energy produced was derived for each case. Unstable atmospheric conditions deliver up to 8% more power than stable conditions, while neutral conditions deliver up to 9% more energy than stable conditions. Therefore, considering a neutral state may lead to a considerably biased energy production estimation. Finally, an example calculation indicates that atmospheric stability is a crucial parameter in estimating wind energy production more accurately.

Evaluation of the reduction in greenhouse gas emissions attributable to wind energy: A retrospective evaluation of Indian Offshore and Coastal Site

This study assesses the possible reduction in carbon dioxide emissions brought on by the growth of wind power in India by developing offshore site. This study uses novel evolutionary algorithms and computational techniques to analyse wind potential on flat and offshore and sites. The wind data were recorded using remote sensing technique LiDAR (Light Detection and Ranging). This study employs the life cycle assessment (LCA) to quantify the atmospheric carbon emissions of offshore wind turbines, to understand and to guide policy regulatory for offshore wind energy, and future investment decisions for wind power expansion. Parameter estimates are used to compute wind densities. The study demonstrates that wind turbine lifespan and energy output have major effects on GHG (Green House Gas) emission, signifying that it is an efficient strategy to extend wind turbine lifetime and enhance wind turbine energy production to minimise wind turbine GHG emission intensity. Over the course of their lifetimes, offshore wind turbines emit 0.130 kg CO2-eq/MJ. Compared to coal power stations, wind turbines have significantly lower life-cycle GHG emission intensities. If wind turbines erected in 2014 supplant coal, the saved GHG emissions might equal 5.08 x 107t CO2-eq, or 0.09% of the global GHG emissions in 2021.

Pattern mining based data fusion for wind turbine condition monitoring

The profitability of wind turbine energy production is for an important part determined by the operation and maintenance costs of wind turbines. An important driver of these costs is currently the premature failure of components due to excessive wear. If it would be possible to accurately predict these failures, preventive maintenance can be made more effective, which should result in less downtime and expensive unexpected failures. This in turn should lower the operational and maintenance costs. The research presented here is a contribution to the research on condition monitoring and failure prediction for wind turbines. To this end, a methodology for failure prediction is designed that combines (fuses) multiple information sources (i.e. SCADA and status log data). The novelty of this research lies in the fact that pattern mining techniques are used to identify relevant rules for a rule-based failure classifier. The methodology is validated on generator bearing failure cases from a real operational wind farm. The results show that the methodology is able to predict generator bearing failures accurately well in advance. The rules on which the predictions are based are interpretable and correspond in general to expert knowledge on the matter.

Air density calculation at high altitude locations for wind energy use: the alpines validation

ABSTRACT Atmospheric air density has an essential role in the energy production of wind turbines. It is directly proportional to the power taken out from the airflow. The common practice at a planned wind farm location is to measure atmospheric parameters and calculate the air density as monthly and yearly averages based on the International Committee for Weights and Measures (CIPM). After that, the reference point is used to calibrate spatial data to study the siting of wind turbines at a large spatial domain of interest using an engineering method based on only temperature and elevation a.m.s.l. The engineering method is also employed with only temperature and elevation data when there are no pressure and relative humidity measurements. The point-to-spatial transformation is done through the simplified engineering formula, and it is known that the method is primarily valid up to a.m.s.l. Above these elevations, the engineering methods have a significant bias, up to error in estimating the air density. This bias leads to a substantial error in energy yield estimations. This study uses more than one in-situ measurement at high altitude locations to calibrate the engineering method at the Alpine Convention Perimeter. It aims to improve the calculation accuracy by calculating the pressure gradient within the region. It is found that the seasonal and yearly averaging errors can be improved by to in the air density calculation with the new approach. The method can be applied to other locations with similar conditions.

Toward control co-design of utility-scale wind turbines: Collective vs. individual blade pitch control

A large-eddy simulation framework has been coupled with controller modules to systematically investigate the impacts of collective (CPC) and individual (IPC) pitch control strategies on utility-scale wind turbine energy production and fatigue loads. Wind turbine components were parameterized using an actuator surface model to simulate the rotor blades and the turbine nacelle. The baseline CPC and IPC algorithms, consisting of single-input single-output proportional–integral controllers and two integral controllers, respectively, were incorporated into the numerical framework. A series of simulations were carried out to investigate the relative performance of the two controllers under various turbulent inflow conditions, spanning hub-height velocities of 7 to 14 m/s. The numerical simulation results of this study showed that, in comparison to the CPC, the IPC controller could successfully reduce the damage equivalent loads of utility-scale turbines at regions 2 and 3 of turbine operation by about 3% and 40%, respectively, without any penalty on the power production of the turbine. It was also shown that, despite its minor impact on the turbulence kinetic energy of the wake, the IPC controller did not influence the recovery of the turbine wake.

Employing Bayesian Quadrature to Improve Fitting of Surrogate Models to Wind Turbine Loads

Stochastic, high-fidelity simulations are increasingly used to estimate wind turbine and plant loads and performance. While these simulations are more accurate, they are prohibitively computationally expensive. Surrogate models can be used to replace direct simulation for lower computational cost; for stochastic surfaces, a Gaussian Process (GP) is appropriate for random variables that follow Gaussian distributions, such as turbine loads and performance.In this study, we employ an advanced surrogate modeling technique to estimate the loads, reliability, and energy production of wind turbines in a wind plant, based on mid-fidelity aero-elastic simulations. Specifically, we use Bayesian Quadrature (BQ) to produce a training data set that, for a given size, minimizes the variance over the entire domain of a GP surrogate model to estimate rotor torque load on a waked turbine. When applied to a GP that was trained with aero-elastic simulation data, the BQ-enabled algorithm reduced variance—a measure of predictive uncertainty—over the entire GP to < 0.3 with 30 samples. When compared to a uniform sampling approach with the same solution space, the BQ method reduces computational time by 99.88%, or over 590 simulation evaluations.

Wind Blade Twist Correction for Enhanced Annual Energy Production of Wind Turbines

Blade geometry is an important design parameter that influences global wind turbine energy harvesting performances. The geometric characteristics of the blade profile are obtained by determining the distribution of the chord and twist angle for each blade section. In order to maximize the wind energy production, implying a maximum lift-to-drag ratio for each wind speed, this distribution should be optimized. This paper presents a methodology to numerically determine the change in the twist angle by introducing a range of pitch angles for the maximum power coefficient case. The obtained pitch values were distributed from the root to the tip of blade. The results prove that the power coefficient increases for wind speeds greater than the rated point, which improves the yearly production of energy by 5% compared to the reference case.

The Socio-Economic Cost of Wind Turbines: A Swedish Case Study

The expansion of wind turbines plays a significant role in developing the ability of a country like Sweden to achieve climate-neutral energy production without relying on nuclear power plants. Wind-turbine energy production is expected to grow in the coming decades. Conflicts may arise between, on the one hand, the government and the energy authority, and, on the other hand, municipalities and property owners, especially if this expansion affects other economic activities, such as tourism and reindeer husbandry, or property values. This report aims to analyse the negative capitalization of wind turbines on property values in Sweden over the last ten years. Our conclusions clearly show a relatively significant capitalization and that this capitalization is relatively local, within eight kilometers of the wind power plant. Large wind turbines, or larger clusters of wind turbines in wind farms, impose a greater socio-economic cost on lower value properties.

Fault Tolerant Fuzzy Logic Control of a 6-Phase Induction Generator for Wind Turbine Energy Production

— In this paper, a fault tolerant fuzzy logic control of a 6-phase induction generator for wind turbine energy production is proposed. The aim is to define a suitable fault tolerant control algorithm based on the fuzzy logic theory. This control algorithm allows the electrical generation even with the presence of one to three missing phases or power converter legs fault situations. The proposed algorithm is tested on an experimental 24 kW wind turbine test bed that represents a 1/100 of the electrical power of a grid connected real wind turbine generator (2.5 MW). A comparison between simulation and experimental results is performed in order to validate the control algorithm effectiveness in healthy and faulty (1 missing phase) modes. Furthermore, the proposed strategy has been compared to classical PI regulators tuned for each kind of faults, while the proposed fuzzy logic controller can be applied in both healthy and any faulty modes without any tuning and detection system.

АНАЛИЗ ТЕХНИКО-ЭКОНОМИЧЕСКОЙ ЭФФЕКИВНОСТИ РАБОТЫ ВЕТРОЭЛЕКТРОСТАНЦИЙ В КРЫМУ

The article provides data on the generation and consumption of electricity by a wind farm. To maintain the operability of the wind farm, it is connected to the general grid of the power system, not only for the output of generated electricity, but also for the consumption of the necessary electricity to start the operation of wind turbines. Electricity generation, payback and net profit of a wind power plant of 12 wind turbines were estimated. Subject of study. Wind power plants and their efficiency. Materials and methods. The theoretical and methodological basis is the works of domestic and foreign scientists in the field of wind energy. In the work, analytical research methods were used, including predictive calculation of the annual energy production of wind turbines. Conclusions. The instability of electricity generation using renewable energy generating units is a serious problem that affects the cost of energy produced. According to the calculations, in 14 years, provided the electricity price is equal to 1.8 rubles, the power plant will recoup the investment and begin to generate net income. The correlation coefficient was determined, which was 0.94.

An Integrated Approach to Predict Wind Resource Energy from an Urban Wind Turbine in a Complex Built Environment

Introducing an urban wind turbine in the crowded and complex City of North Sydney built environment can provide a significant opportunity to generate onsite wind energy and reduce electric demand and utility costs. Elevated turbulent conditions present a number of well-known challenges to urban wind turbines and as a result the energy production may reduce due to changes in wind speeds and directions. This current case study presents a procedure to optimize urban wind turbine energy production comprising key steps which include the project site potential for the installation of wind turbines, the estimation of the annual wind power available and the cost estimate for installation and maintenance. The wind potential for the project site was initially determined from statistical wind data cross-referenced with typical weather data for the Sydney region. Computational Fluid Dynamics (CFD) simulations of principal wind directions were then used to adjust the local wind climate data and establish a suitable wind turbine position. Finally, the annual energy production for a number of 10-20 kW commercially available wind turbines was estimated taking into account the wind turbine power curve and technical specifications. The CFD simulations in the current study accounted for the complex site topography and incorporated the shielding impact of nearby trees and other vegetation in order to find the least turbulent area for a successful installation. This study assessed all the parameters that have impact on the accuracy of the numerical model including, computational domain, mesh distribution, numerical scheme and CFD results integration with the localized weather data for the project site.

Wind resource prospecting: Predicting winds aloft using surface winds and the position of the sun

The sun’s position in the sky appears to strongly influence the vertical wind gradient within the atmospheric boundary layer. This study uses a quantitative research design that combines the position of the sun along with surface wind speeds to estimate the wind gradient. The model applies to uncluttered simple terrain and requires only 10 m wind speed data to extrapolate wind speeds and wind turbine energy production to heights of 60 m or more. The average daytime and nighttime model wind speed errors are 1.7% and 2.4%, respectively. The average daytime and nighttime model turbine energy production errors are 3.8% and 6.1%, respectively. The model offers a practical and low-cost alternative to tall tower systems to assess wind resources, especially for remote sites.

Assessment of IEC Normal Turbulence Model and Modelling of the Wind Turbulence Intensity for Small Wind Turbine Design in Tropical Area: Case of the Coastal Region of Benin

The wind turbulence intensity observed on a site have an influence the wind turbine energy production and the lifetime of the blades. It is therefore primordial to master this parameter for the optimization of the production. So therefore, this study is interested on the modelling of the wind turbulence intensity at 10 m above the ground on the coast of Benin. Four years of wind data measured on the site of Cotonou Port Authority (PAC) from 2011 to 2014 and recorded with a temporal resolution of 10 min were used. From the transport equation of turbulent kinetic energy followed by a numerical simulation based on the Nelder-Mead algorithm developed under the Matlab software, we proposed five new models for estimating the wind turbulence intensity. The results of the different simulations reveal that four of proposed models and based on the roughness, the speed of friction and the length of Obukhov better fit the data, during the periods of January, April, June, July, August, September and December. The estimators of the Root Mean Square Error (RMSE) and the Mean Absolute Error (MAE) vary from (0.02; 0.01) in December to (0.09; 0.07) in August. As for the model which is a function of roughness and the wind shear coefficient (expressed only according to the wind speed), it gives better performance whatever the time of the year and the atmosphere stability conditions. The estimations errors are included between (0.02; 0.01) obtained in December and (0.08; 0.06) observed in March. A comparative study between the existing models in the literature and the best model proposed in this study showed that only this model gives the best adjustment with the data. It can therefore be used on the sites where turbulence is influenced by the roughness and the atmosphere stability. Finally, from this model a new wind turbine design class has been proposed for the site of Cotonou. It takes into account the actual levels of turbulence observed and thus allow to optimize the energy production.

Estimation of Wind Turbine Energy Production Value by Using Machine Learning Algorithms and Development of Implementation Program

ABSTRACT In recent years, analyzing the data gathered during production process of the energy has become an important issue in the wind energy sector in order to increase the efficiency of the produced energy. This study estimated the produced energy value by using machine learning algorithms depending on the temperature, wind speed and direction values gathered from the wind turbine in 2015. A mathematical equation which correctly estimates the energy production value by 90% has been obtained. A computer program was developed for other users to see the results of this mathematical equation.

Using field data–based large eddy simulation to understand role of atmospheric stability on energy production of wind turbines

A novel and a robust high-fidelity numerical methodology has been developed to realistically estimate the net energy production of full-scale horizontal axis wind turbines in a convective atmospheric boundary layer, for both isolated and multiple wind turbine arrays by accounting for the wake effects between them. Large eddy simulation has been used to understand the role of atmospheric stability in net energy production (annual energy production) of full-scale horizontal axis wind turbines placed in the convective atmospheric boundary layer. The simulations are performed during the convective conditions corresponding to the National Renewable Energy Laboratory field campaign of July 2015. A mathematical framework was developed to incorporate the field-based measurements as boundary conditions for the large eddy simulation by averaging the surface flux over multiple diurnal cycles. The objective of the study is to quantify the role of surface flux in the calculation of energy production for an isolated, two and three wind turbine configuration. The study compares the mean value, +1 standard deviation, and −1 standard deviation from the measured surface flux to demonstrate the role of surface heat flux. The uniqueness of the study is that power deficits from large eddy simulation were used to determine wake losses and obtain a net energy production that accounts for the wake losses. The frequency of stability events, from field measurements, is input into the calculation of an ensemble energy production prediction with wake losses for different wind turbine arrays. The increased surface heat flux increases the atmospheric turbulence into the wind turbines. Higher turbulence results in faster wake recovery by a factor of two. The faster wake recovery rates result in lowering the power deficits from 46% to 28% for the two-turbine array. The difference in net energy production between the +1 and −1 standard deviation (with respect to surface heat flux) simulations was 10% for the two-turbine array and 8% for the three-turbine array. An ensemble net energy production by accounting for the wake losses indicated the overestimation of annual energy production from current practices could be corrected by accounting for variation of surface flux from the mean value.