Uncertainty Of Wind Speed Research Articles

A novel hybrid wind speed prediction framework based on multi-strategy improved optimizer and new data pre-processing system with feedback mechanism

As a kind of renewable energy, wind energy has great potential for development and has been paid attention to by governments all over the world. However, due to the high uncertainty of wind speed, how to accurately predict wind speed and make use of wind energy has been recognized as a difficult problem. In order to solve this problem, a new hybrid wind speed prediction framework is proposed, which is composed of two subsystems, data preprocessing system and high-accuracy prediction system. In the system 1, the feedback mechanism is creatively added to the singular spectrum analysis (SSA) to find out the optimal decomposition-recombination strategy through the accuracy feedback. In the system 2, the unconstrained weighting mechanism is realized through the combination of combined neural network and multi-objective optimization algorithm to maximize the prediction accuracy on the premise of ensuring the stability of prediction. Besides, an improved meta-heuristic optimization algorithm based on cross-perturbation strategy (CP-JAYA) and its multi-objective form (MO-CPJAYA) are applied on two systems respectively to further improve the prediction ability of the framework. In 5 groups of experiments, the accuracy, advancement, generalization and sensitivity of the model are tested and compared with 13 other models. The proposed prediction framework has the best performance in all four sets of data. In 3 groups of discussions, we verify the advanced nature of CP-JAYA and MO-CPJAYA respectively through 13 single-objective test functions (CEC) and 4 multi-objective test functions (ZDT), and the speed advantage of the framework by recording the CPU running time.

Uncertainty of CYGNSS-Derived Heat Flux Variations at Diurnal to Seasonal Time Scales over the Tropical Oceans

Air–sea heat flux is one of the most important factors that affects ocean circulation, weather, and climate. Satellite remote sensing could serve as an important supplement to the sparse in situ observations for heat flux estimations. In this study, we analyze the uncertainty of the turbulent heat fluxes derived from wind speed measured by the Cyclone Global Navigation Satellite System (CYGNSS) over the global tropical oceans at different time scales. In terms of spatial distribution, there is large uncertainty (approximately 50 to 85 W·m−2 in the RMSE) near the equator in the western Pacific Ocean, the Arabian Sea, the Bay of Bengal, and near the Gulf of Guinea. The turbulent heat fluxes are in agreement with the buoys in representing the intraseasonal and seasonal variability, but more specific regional validations are needed for revealing the synoptic and sub-synoptic phenomena and the diurnal cycle. The uncertainty of the CYGNSS wind speed contributes approximately 50–57% to the uncertainty of the estimation of turbulent heat fluxes at the frequency band with a typical period of 3–7 days. In addition, the input sea surface temperature, rather than the wind speed, results in differences in the estimation of the monthly mean turbulent heat fluxes in the tropical Atlantic Ocean based on the COARE 3.5 algorithm. In conclusion, although the CYGNSS-derived turbulent heat fluxes are basically in good agreement with the in situ observations, our analysis highlights the importance of considering the limitations of these datasets, particularly in high wind speed conditions and for higher-frequency variations, including at synoptic, sub-synoptic, and diurnal time scales.

Comparing scanning lidar configurations for wake measurements based on the reduction of associated measurement uncertainties

Scanning lidars are increasingly being used for new measurement applications, such as wake measurements. This type of measurement requires an uncertainty quantification, and the setup of such instruments should ideally be planned to minimize the overall measurement uncertainty. Based on a previous experimental study, we quantify the overall uncertainty for a scanning lidar wake measurement campaign by analyzing the pointing accuracy for distinct configuration scenarios. The selected scenarios are based on a single ground-based scanning lidar measuring the downstream flow, a scanning lidar mounted on the turbine’s nacelle and, finally, two scanning lidars measuring dual-Doppler points within the wake. The results for each configuration are presented in terms of the overall uncertainty and measurement outputs provided from these setups. In a case study using the experimental dataset to compare ground- and nacelle-based configurations, we find that – under slightly stable conditions at 5D downstream – the overall radial wind speed uncertainty, normalized by reference wind speed without counting the sample size uncertainty, decreases on average from 2.78% for the ground-based lidar to 1.33% for the nacelle-based setup. Another advantage of the nacelle-based setup is the potentially larger sample size, as it can measure multiple downstream distances at once. The dual-lidar configuration has advantages in providing horizontal wind speed and direction instead of radial wind speed components only, hence reducing uncertainties associated with wind direction. However, this comes with the cost of fewer measured points to be compared with an engineering wake model and less flexibility to position the lidars, since their relative spatial position impacts the uncertainty. In summary, the presented methodology can benefit the best selection of scanning lidar configuration for a particular application, here demonstrated for wake measurements, proving the user with an estimation of the overall measurement uncertainty and, most importantly, the sensitivity of each parameter.

Long-term uncertainty quantification in WRF-modeled offshore wind resource off the US Atlantic coast

Abstract. Uncertainty quantification of long-term modeled wind speed is essential to ensure stakeholders can best leverage wind resource numerical data sets. Offshore, this need is even stronger given the limited availability of observations of wind speed at heights relevant for wind energy purposes and the resulting heavier relative weight of numerical data sets for wind energy planning and operational projects. In this analysis, we consider the National Renewable Energy Laboratory's 21-year updated numerical offshore data set for the US East Coast and provide a methodological framework to leverage both floating lidar and near-surface buoy observations in the region to quantify uncertainty in the modeled hub-height wind resource. We first show how using a numerical ensemble to quantify the uncertainty in modeled wind speed is insufficient to fully capture the model deviation from real-world observations. Next, we train and validate a random forest to vertically extrapolate near-surface wind speed to hub height using the available short-term lidar data sets in the region. We then apply this model to vertically extrapolate the long-term near-surface buoy wind speed observations to hub height so that they can be directly compared to the long-term numerical data set. We find that the mean 21-year uncertainty in 140 m hourly average wind speed is slightly lower than 3 m s−1 (roughly 30 % of the mean observed wind speed) across the considered region. Atmospheric stability is strictly connected to the modeled wind speed uncertainty, with stable conditions associated with an uncertainty which is, on average, about 20 % larger than the overall mean uncertainty.

An optimized deep nonlinear integrated framework for wind speed forecasting and uncertainty analysis

Accurate wind speed prediction improves the efficiency of electricity and also increases the economic benefits of wind farms. However, the previous studies do not fully consider the chronological feature of wind speed, and do not properly identify and use the characteristic decomposed components of wind speed series in different time periods. The phenomenon of insufficient and excessive decomposition of components weakens the prediction accuracy of the model and destroys the power generation plan of the wind farm. In this paper, a segmented multi-modal deep learning integrated model based on periodicity is proposed. Firstly, the time series is reconstructed into matrix according to date; the matrix is divided into different segment matrices according to chronological characteristics; each sub-matrix is spliced into sub-sequence according to chronological order. Secondly, the contribution extremum method is used to determine the optimal feature components of all sub-sequences. Thirdly, the optimized deep learning model integrates all sub-sequences and then obtains the prediction results of the current date. Finally, the uncertainty analysis of the predicted value further improves the reliability of wind speed prediction. The data of wind farm in Hexi corridor area of China are used to simulate the experiment, and the results show that the proposed model has good performance.

Uncertainty analysis of wind speed of wind turbine

AbstractThe uncertainty of wind speed has a great impact on the reliability of wind turbines. However, previous studies usually used average wind speed or rated wind speed as inputs, ignoring the impact of time and space distribution on wind speed. Based on the hourly ground observation data of meteorological stations, four observation stations are taken as the research object of the uncertainty of wind speed spatial distribution. The four observation stations are divided into single day and one week, and seven data analysis indicators and normal distribution models are used day and night to analyze the uncertainty of wind speed time distribution. The results show that both spatial distribution and temporal distribution are important factors that cause the uncertainty of wind speed. Due to different geographical locations of four stations, their wind speed time domain diagrams are different, and the maximum wind speed, fluctuation frequency and fluctuation amplitude of wind speed at each observation station are different; The wind speed distribution of a single day is quite different from that of a week. The data of a single day and a week are analyzed by day and night, which is quite different from the overall data analysis. The necessity of wind speed uncertainty research of wind turbine considering the temporal and spatial distribution is verified, and it is concluded that normal distribution is not suitable for fitting the distribution model of four stations under study, which lays a foundation for determining the wind speed distribution model, analyzing the power characteristics of wind turbine, evaluating the generation capacity of wind turbine, and analyzing the reliability of wind turbine.

Day-Ahead Scheduling of Multi-Energy Microgrids Based on a Stochastic Multi-Objective Optimization Model

Dealing with multi-objective problems has several interesting benefits, one of which is that it supplies the decision-maker with complete information regarding the Pareto front, as well as a clear overview of the various trade-offs that are involved in the problem. The selection of such a representative set is, in and of itself, a multi-objective problem that must take into consideration the number of choices to show the uniformity of the representation and/or the coverage of the representation in order to ensure the quality of the solution. In this study, day-ahead scheduling has been transformed into a multi-objective optimization problem due to the inclusion of objectives, such as the operating cost of multi-energy multi-microgrids (MMGs) and the profit of the Distribution Company (DISCO). The purpose of the proposed system is to determine the best day-ahead operation of a combined heat and power (CHP) unit, gas boiler, energy storage, and demand response program, as well as the transaction of electricity and natural gas (NG). Electricity and gas are traded by MGs with DISCO at prices that are dynamic and fixed, respectively. Through scenario generation and probability density functions, the uncertainties of wind speed, solar irradiation, electrical, and heat demands have been considered. By using mixed-integer linear programming (MILP) for scenario reduction, the high number of generated scenarios has been significantly reduced. The ɛ-constraint approach was used and solved as mixed-integer nonlinear programming (MINLP) to obtain a solution that meets the needs of both of these nonlinear objective functions.

Optimal maximum power point tracking of wind turbine doubly fed induction generator based on driving training algorithm

The operation of wind power system at optimum power point is a big challenge particularly under uncertainty of wind speed. As a result, it is necessary to install an effective maximum power point tracking (MPPT) controller for extracting the available maximal power from wind energy conversion system (WECS). Therefore, this paper aims to obtain the optimal values of injected rotor phase voltage for doubly fed induction generator (DFIG) to ensure the extraction of peak power from wind turbine under different wind speeds as well as to get the optimal performance of DFIG. A new metaheuristic optimization approach; Driving Training Algorithm (DTA) is used to crop the optimal DFIG rotor voltages. Three different scenarios are presented to have MPPT, the first one is the MPPT with unity stator power factor, the second one is the MPPT with minimum DFIG losses, and the third scenario is MPPT with minimum rotor current to reduce the rating of rotor inverter. The MATLAB environment is used to simulate and study the proposed controller with 2.4 MW wind turbine. The optimum power curve of wind turbine has been estimated to get the reference values of DFIG mechanical power. The results ensured the significance and robust of the proposed controller to have MPPT under different wind speeds. The DTA results are compared with other two well-known optimization algorithms; water cycle algorithm (WCA) and particle swarm optimizer (PSO) to verify the accuracy of results.

Robust sliding mode controller for frequency regulation in a microgrid

The distribution of power at minimum frequency deviation is a primary objective in an isolated microgrid due to variation in inertia and uncertainties in distributed generations. Thus, in this study, a centralized robust sliding mode controller is proposed to achieve minimum frequency deviation with optimal power distribution among distributed generations in an isolated microgrid. The microgrid model is developed for small signal analysis purpose using the electric vehicle charging model dynamics, the wind energy model dynamics and the run-of-river hydro model dynamics. The proposed controller parameters are optimized using linear matrix inequality (LMI), and its asymptotic convergence law is verified via Lyapunov’s stability theorem. The effective reduction in frequency deviations, time characteristics and chattering in control signals is achieved using the proposed controller. Furthermore, robustness nature of the proposed controller conforms against random plant outage sequence, random load and wind uncertainty. In addition, the performance of proposed controller is compared with existing state proportional–integral–derivative (PID) controller and traditional sliding mode control (SMC) even in presence of random plant outage sequence, load and wind speed uncertainties. The proposed controller is capable of regulating power share scenario intelligently according to load disturbance, plant uncertainties and also reducing time characteristics, oscillations and chattering phenomena in control effort within a short duration. As a result, the responses of proposed controller are compared with existing controllers in terms of improved closed-loop dynamics and its effectiveness. The performance of the proposed controller is demonstrated using MATLAB© simulations platform.

Improving Wind Speed Uncertainty Forecasts Using Recurrent Neural Networks

For integration of growing amounts of volatile renewable energy in the European electricity system, reliable weather prognosis gains importance. But, depending on weather conditions, forecast reliability of wind speed for predicting wind power can vary drastically with time. Thus, relevance of risk-aware system operation strategies is increasing based on wind speed uncertainty a measure of which is provided by the standard deviations of ensemble forecasts of the German Weather Service. However, lacking validity of this measure is known as a long-standing problem.Therefore, this work investigates how machine learning based on a suitably selected set of physical quantities of weather ensemble data as well as historic wind data allows for a more realistic uncertainty quantification. A recurrent neural network (RNN) based sequence-to-sequence architecture is implemented and probabilistic wind speed forecasts are generated for a region in northern Germany.The results are evaluated and compared with the forecasts of the German Weather Service thereby revealing improved validity of such deep-learning based uncertainty measures.

Understanding the potential of Sentinel-2 for monitoring methane point emissions

Abstract. The use of satellite instruments to detect and quantify methane emissions from fossil fuel production activities is highly beneficial to support climate change mitigation. Different hyperspectral and multispectral satellite sensors have recently shown potential to detect and quantify point-source emissions from space. The Sentinel-2 (S2) mission, despite its limited spectral design, supports the detection of large emissions with global coverage and high revisit frequency thanks to coarse spectral coverage of methane absorption lines in the shortwave infrared. Validation of S2 methane retrieval algorithms is instrumental in accelerating the development of a systematic and global monitoring system for methane point sources. Here, we develop a benchmarking framework for such validation. We first develop a methodology to generate simulated S2 datasets including methane point-source plumes. These benchmark datasets have been created for scenes in three oil and gas basins (Hassi Messaoud, Algeria; Korpeje, Turkmenistan; Permian Basin, USA) under different scene heterogeneity conditions and for simulated methane plumes with different spatial distributions. We use the simulated methane plumes to validate the retrieval for different flux rate levels and define a minimum detection threshold for each case study. The results suggest that for homogeneous and temporally invariant surfaces, the detection limit of the proposed S2 methane retrieval ranges from 1000 to 2000 kg h−1, whereas for areas with large surface heterogeneity and temporal variations, the retrieval can only detect plumes in excess of 500 kg h−1. The different sources of uncertainty in the flux rate estimates have also been examined. Dominant quantification errors are either wind-related or plume mask-related, depending on the surface type. Uncertainty in wind speed, both in the 10 m wind (U10) and in mapping U10 to the effective wind (Ueff) driving plume transport, is the dominant source of error for quantifying individual plumes in homogeneous scenes. For heterogeneous and temporally variant scenes, the surface structure underlying the methane plume affects the plume masking and can become a dominant source of uncertainty.

Adaptive optimal energy management in multi-distributed energy resources by using improved slime mould algorithm with considering demand side management

In this article, a stochastic programming model is developed to solve a multi-renewable sources-based energy management with the help of a multi-objective improved slime mould algorithm (MOISMA). As a result, the microgrid's performance is expected to improve with this technique. However, such a fast-acting methodology reduces operating generation costs and emissions by using multiple renewable sources. Firstly, Hong's (2 m + 1) point estimate method is used to generate as well as control the uncertainty of wind speed and solar irradiance patterns. Secondly, this MOISMA-based-energy management study outperforms better when compared with multi-objective genetic algorithms (MOGA) and particle swarm optimization (MOPSO). All these performances are carried out at the time of use (ToU) based on demand response (DR) load management. Using these techniques, which can reduce the cost of generating power and increase energy resource use, a demand-side management study can be performed within 24 h of the generation of load data for analysis purposes. So, in this research by the application of demand-side management, we get a minimum generation of power costs and reduce the environmental emission by 12.62% and 7.43%, for wind energy, similarly 31.53% and 2.51% for solar energy systems with this DR technique. Thus, overall, it has been validated that using such a novel optimization-based energy management study can reduce system generation cost, energy usage, peak demand shifting, and environmental emissions.

Sizing of Microgrid System Including Multi-Functional Battery Storage and Considering Uncertainties

Battery storage units (BSUs) are usually used to perform a single function in most planning studies related to microgrids (MGs). This paper presents an effective methodology to use the BSUs to perform multi-function including supply/demand matching and energy arbitrage. This is done according to a system policy containing all possible scenarios to fully utilize the BSUs to maximize the benefit. In the proposed work, the optimal sizing of the MG system under study containing wind turbines (WTs), photovoltaic system (PV), BSUs, and diesel units (DUs) is obtained. The main objectives of the proposed methodology are; 1) minimizing the total costs of the MG, 2) minimizing the harmful gas emissions, and 3) minimizing the accumulated power difference between the generation from renewable energy systems (RESs) and the demand. Due to the stochastic behavior of the output from the RESs, the uncertainties of wind speed, solar irradiance, and temperature are considered in the study. Two modes of operation of the MG (grid-connected and islanded) and the demand side management (DSM) are also considered. The problem is formulated as a constrained nonlinear optimization problem and is solved using two metaheuristic optimization algorithms, Moth-Flame Optimization (MFO) and Hybrid Firefly and Particle Swarm Optimization (HFPSO). Moreover, the uncertainties in the different parameters are considered by using the Latin Hypercube Sampling (LHS) method to generate samples of wind speed, solar irradiance, and temperature. To examine the proposed methodology, different case studies are presented and discussed. Moreover, the results of the used two algorithms, MFO and HFPSO, are compared to show their effectiveness in solving the proposed problem and assure the optimal solution. The optimization problem is implemented and solved using MATLAB software.

Experimental Studies for Glass Light Transmission Degradation in Solar Cells Due to Dust Accumulation Using Effective Optical Scattering Parameters and Machine Learning Algorithm

Environmental impacts influence solar cell performance significantly. A harsh environment may cause temperature, wind speed, or humidity uncertainties. In the case of photovoltaic systems in desert or semidesert areas, dust accumulation critically impacted solar cell degradation. Herein, we developed a novel machine-learning-based optical model that estimates solar cell efficiency's degradation because of dust accumulation. Glass substrates were directed to dust for 16 weeks with a weekly characterization procedure. Samples were morphologically and optically characterized to be seeded into the scattering model. Morphological characterization measurements were conducted using SEM and EDX-mapping to explore the internal composition of dust particles. Optically, the dusty glass substrate sample's transmission spectra were measured using a UV-Vis spectrometer. Refractive index, thickness, and scattering particle radius have been extracted as effective parameters with fitting accuracy of 95%. The extracted dataset is then inputted into a machine-learning model to predict the transparency degradation in the glass substrate. Finally, predicted data is validated against the actual degraded monocrystalline cell, showing 89.4% of matching.

Robust linear parameter varying frequency control for islanded hybrid AC/DC microgrids

• The LPV representation of a nonlinear isolated hybrid microgrid model is investigated. • The intermittent nature of the wind speed, solar irradiation, and loads, and several parameter uncertainties are considered in the frequency control design procedure. • A dynamic robust LPV controller synthesis based on the three-step algorithm is proposed for the nonlinear model. • The proposed secondary frequency control approach suggests a practical dynamic output feedback controller with lower order than the IHMG model. • The algorithm superiority is verified in various disturbance-based and uncertain parameter-based scenarios. The applicability of a robust linear parameter varying (LPV) control for frequency fluctuation damping in an islanded hybrid microgrid (IHMG) system (DEG/WTG /PV/FC/ESSs) is investigated in this paper. Due to the erratic behavior of the majority of the renewable energy sources (RESs) and the varying time nature of loads, frequency deviation from the nominal value is inevitable, especially in the islanded mode. Load variation, solar irradiation, and wind power disturbance, as well as system parametric uncertainties, can significantly disturb the system frequency operation. In this paper, the wind speed and rotated speed of the nonlinear wind turbine are highlighted as scheduling parameters, and the nonlinearity of the wind turbine is hidden by the LPV approach. In the proposed method, a dynamic output feedback controller is obtained based on the power balance equation, by solving three LMIs to minimize the upper bound of the L 2 -Norm of the uncertain IHMG in an algorithmic approach. For better evaluation, the ultimate LPV control approach is compared with two other methods on the understudy IHMG model. The result represents a noticeable advantage of the proposed robust method compared to other methods in terms of frequency stability based on time-domain and norms characteristics in presence of the simultaneous disturbances and parametric uncertainties.

Stochastic-Risk Based Approach for Microgrid Participation in Joint Active, Reactive, and Ancillary Services Markets Considering Demand Response

In the restructured power systems, renewable energy sources (RES) have been developed. Uncertainties of these generators reduce the reliability and stability of power systems. The frequency and voltage for the correct operation of the power systems must always be maintained within a nominal value. Ancillary services (AS), energy storage systems (ESS), and demand response programs (DRPs) can be effective solutions for mentioned problems. Microgrids (MG) can make an improvement in their profits and efficiency by participating in various markets. This paper provides an optimal scheduling for the simultaneous participation of MGs in coupled active, reactive power and AS markets (regulation, spinning reserve and non-spinning reserve) by considering ESS, DRPs, call for deploying AS, and the uncertainties of wind and solar productions. Capability diagrams; mathematical equations are used to model active and reactive power of generation units. Risk management in this paper is done by the conditional value at risk (CVaR) method and probability distribution functions (PDF) are used for modeling uncertainties of wind speed and solar radiation. The ERCOT (Electric Reliability Council of Texas) market is simulated with real world data.

Optimal Sizing and Sitting of Distributed Generation in Distribution Network considering Power Generation Uncertainty

This paper presents an application of the recent metaheuristic Geometric Mean Optimizer (GMO) for the allocation of renewable energy sources (RES), including wind turbine (WT) and biomass-based Distributed Generation (DG) units in the distribution network (DN). The primary objective function is to minimize the total power and energy losses. The Weibull probability distribution function (PDF) is employed to describe the uncertainty of wind speed. The high penetration of RES with intermittent availability and demand variations has introduced many challenges to DN, such as power fluctuations, voltage rise, high losses, and low voltage stability. Therefore, the use of dispatchable biomass is considered to smooth out supply fluctuations and maintain supply continuity. A standard IEEE 69-bus test system is used to verify the performance of the proposed approach. The simulation results and comparison with other techniques demonstrate the significant energy loss reduction achieved by the proposed technique.

Long-term wind and solar energy generation forecasts, and optimisation of Power Purchase Agreements

Due to more affordable solar and wind power, and the European Union regulations for decarbonisation of the economy, more than 40% of the Fortune 500 companies have targets related to green energy. This is one of the main reasons why multi-technology Power-Purchase Agreements (PPAs) are becoming increasingly important. However, there are risks associated with the uncertainty and variable generation patterns in wind speed and solar radiation. Moreover, there are challenges to predict intermittent wind and solar generation for the forecasting horizon required by PPAs, which is usually of several years. We propose a long-term wind and solar energy generation forecasts suitable for PPAs with cost optimisation in energy generation scenarios. We use Markov Chain Monte Carlo simulations with suitable models of wind and solar generation and optimise long-term energy contracts with purchase of renewable energy.

Research on Robust Model Predictive Control Strategy of Wind Turbines to Reduce Wind Power Fluctuation

• Establish a robust model predictive control (RMPC) strategy for wind turbines based on wind speed uncertainty. • The error model of the nominal system and actual system is constructed and Lyapunov stability is used to prove that the error system tends to be stable. • Simulation analysis of the influence of RMPC strategy on wind turbine operation at different stable operating points. In the process of wind power generation, the random fluctuation of wind speed affects the smooth operation of the wind turbine and the stability of the output power of the wind turbine. Aiming at the influence of wind speed random fluctuation on the stable operation of wind turbines, this paper regards wind speed random fluctuation as bounded disturbance and adopts Robust Model Predictive Control (RMPC) strategy to divide the actual wind power system with wind speed disturbance into two parts: undisturbed nominal system and perturbed parts. As for the mismatch between the actual wind power system and the nominal system, the state of the actual wind power system is limited to a constant set by a certain feedback control law. The feedback control law and robust invariant set are then solved off-line via linear matrix inequalities (LMI). The 5MW wind turbine model developed by Nrel is used to simulate the feasibility of the algorithm on the MATLAB platform. RMPC control strategy can achieve the control goals of maximum wind energy capture at low wind speed and constant power operation at high wind speed. RMPC can effectively reduce the influence of wind speed uncertainty on stable operation of wind turbines, reduce output power fluctuation and improve power generation quality.

Multi model robust control design for a floating offshore variable speed wind turbine with tension leg platform

This paper presents a multi-model robust control (MMRC) design for an offshore variable speed wind turbine with tension leg platform. The proposed control scheme covers the model uncertainty in the above rated wind speed, and it provides a reliable control for power regulation while minimizing the mechanical loads on the wind turbine structure. For this purpose, the above rated wind speed region is divided into several wind speed groups, and a set of linearized models are obtained from the Fatigue, Aerodynamics, Structures, and Turbulence (FAST) simulator for various mean wind speeds of each group. Using Weibull wind speed distribution, a nominal model with additive uncertainty is generated for each wind speed group, to be used in the multi-model robust control design. The MATLAB/Simulink software environment is used to verify the performance of the proposed control strategy using the National Research Energy Laboratory (NREL) three-bladed horizontal axis 5-MW baseline wind turbine. The performance of the proposed multi-model robust controller is compared with the state-of-the-art gain-scheduling PI (GSPI) controller. The results show improvement in power regulation and platform movement minimization while reducing the mechanical loads of the wind turbine. By using the proposed control scheme, the severe oscillation in the blade pitch actuation and power output, which is due to the changes in the operating point of the wind turbine, decreases drastically.