Data-Tuned Fuzzy Logic Controller Applied to a Horizontal Axis Wind System

This paper deals with the control problem concerning the output voltage frequency and amplitude regulation of a wind system power plant not connected to the supply grid. The wind system configuration includes a horizontal-axis wind-turbine which drives a synchronous generator. An appropriate modeling approach has been adopted for both the wind-turbine and the synchronous generator. The proposed controller makes use of the fuzzy logic environment in order to take advantage of the wind plant system informations integrated into a limited number of equilibrium condition points (input variable - output variable pairs). The fuzzy logic controller described in the present paper merges the most appropriate fuzzy rules clusters, based on the steady state working conditions. Then, thanks to a Least Square Estimator algorithm, the proposed control algorithm evaluates, for each sample time, the linear relation between control law correction and control tracking error levels. In order to demonstrate robustness of the suggested fuzzy control algorithm, two sets of results have been provided: the first one consider a fuzzy base with equally spaced rules, whereas, in the second set results, the number of fuzzy rules is reduced by a 25%.

Investigations of Unsteady Aerodynamics of an Offshore Floating Wind Turbine With Platform Motions Using Dual-Sliding Method

Offshore wind energy developed rapidly in recent years. Due to the platform motions causing by ocean waves, the aerodynamics of floating offshore wind turbines (FOWT) show strong unsteady characters than onshore wind turbines. The widely used methods to investigate the unsteady aerodynamic performance of wind turbine are Blade Element Momentum (BEM) and Free-Vortex Wake (FVW) methods. The accuracy of these two methods strongly depend on empirical formula or correction models. However, for dynamics motions of wind turbine, there is still a lack of accurate models. CFD simulations using overset or dynamic mesh methods also have been used for FOWT aerodynamic investigations. However, the mesh deforming or reconstruction methods are usually suitable for small movement of wind turbine blade. In this paper, a dual-sliding mesh method is employed to simulate the unsteady aerodynamic characters of an offshore floating wind turbine with supporting platform motions using Unsteady Reynolds Averaged Navier-Stokes (URANS) simulations. Both rotor rotation and platform motions are treated as rigid angular motions. The relative motion and data exchange were simulated using sliding mesh method. The NREL 5MW reference wind turbine with platform pitching and rolling motions are considered. The pitching and rolling motions of floating platform are simplified in the form of a prescribed sinusoidal function. Different conditions with two amplitudes and two frequencies of pitching and rolling motions were investigated. URANS were performed with full structured mesh for wind turbine rotor using commercial software FLUENT. The platform motions were set using User Defined Function (UDF). Transitional Shear Stress Turbulence (T-SST) model was employed. The simulation results were compared with BEM method and FVW method results. Both steady status and dynamic pitching processes are investigated. The variations of wind turbine aerodynamic load, as well as the aerodynamic character of airfoils at different spanwise positions, were obtained and analyzed in detail. The simulations results show that the platform pitching introduce remarkable influence on the wind turbine aerodynamic performance. The platform pitching has much larger influence on the wind turbine power and thrust than the platform rolling. The dual-sliding mesh method shows potentials to investigation the dynamic process with multiple rigid motions.

Experimental and Numerical Study on the Flow Field Around a Parallel-Foil Turbine

An experiment facility of parallel-foil turbine is proposed in this study. The flow field around foils at different reduced frequency, pitching amplitude and plunging amplitude is measured by 2D Particle Image Velocimetry (PIV) system. And the energy extraction performance at different motion parameters is analyzed numerically. The comparison between experimental and numerical flow field is conducted at different reduced frequency. The evolution of flow field and the aerodynamic force with different pitching amplitude and plunging amplitude are discussed. The effect of pitching amplitude and plunging amplitude on energy extraction performance is obtained. Results indicate that the pitching amplitude can increase the range and the strength of acceleration area by varying the pitching velocity and the effective angle of attack. The optimal extraction performance appears at 70°. Due to the increase in plunging amplitude, the energy extraction performance and efficiency increase gradually. The optimal plunging amplitude is 1.0. The pitching amplitude and the plunging amplitude influence the power output by affecting the vortex shedding and the flow reattachment in oscillation process.

Development of a CFD-CSD Coupling Technique for Large Scale Offshore Wind Turbines

Wind engineering technology has been continuously investigated and developed over the past several decades in response to steadily growing demand for renewable energy resources, in order to meet the increased demand for power production, fixed and floating platforms with different mooring configurations have been fielded, accommodating large-scale offshore wind turbines in deep water areas. In this study, the aerodynamic loads on such systems are modeled using a computational structural dynamics solver called OpenFAST developed by National Renewable Energy Laboratory, coupled to an in-house computational fluid dynamics solver called GT-Hybrid. Coupling of the structural/aerodynamic motion time history with the CFD analysis is done using an open File I/O process. At this writing, only a one-way coupling has been attempted, feeding the blade motion and structural deformations from OpenFAST into the fluid dynamics analysis. The sectional aerodynamic loads for a large scale 5 MW offshore wind turbine are presented, and compared against the baseline OpenFAST simulations with classical blade element-momentum theory. Encouraging agreement has been observed.

Multi-Criteria Selection of Wind Turbine Using TOPSIS Approach

The recent revolution in the use of renewable energy worldwide has opened many dimensions of research and development for sustainable energy. In this context, the use of wind energy has received notable attention. One critical decision in the development of a wind farm is the selection of the most appropriate turbine compatible with the characteristics of the geographical location under consideration in order to harness maximum energy. This selection process considers multiple decision criteria which are often in conflict with each other, as improving one criterion negatively affects one or more other criteria. Therefore, it is desired to find a tradeoff solution where all selection criteria are simultaneously optimized to the best possible level. This paper proposes a TOPSIS (The Technique for Order of Preference by Similarity to Ideal Solution) based approach for multi-criteria selection of wind turbine. Three decision criteria, namely, hub height, wind speed, and net capacity factor are used in the decision process. A case study is shown on real data collected from the Aljouf region located at an altitude of 753 meters above sea level in the northern part of Saudi Arabia. Seventeen turbines with rated capacities ranging from 1.5 GW to 3 GW from various manufacturers are evaluated. Results indicate that Vestas V110 turned out to be the most appropriate turbine for the underlying site.

Intelligent Fault Diagnosis of Planetary Wheel Bearing Using Constrained Independent Component Analysis and Stacked Sparse Autoencoder

Industrial machinery is developing in the direction of large-scale, automation, and high precision, which brings novel troubles to mechanical equipment management and maintenance. Intelligent diagnosis of mechanical running state based on vibration signals is becoming increasingly important, and it is still a great challenge at pattern recognition. As one of the indispensable components in mechanical equipment, planetary gearboxes are widely used in wind power, aerospace, and heavy industry. However, the problem of automatically maximizing the accuracy of planetary gearbox under different working conditions has not been solved. Therefore, an intelligent diagnosis method for planetary wheel bearing based on constrained independent component analysis (CICA) and stacked sparse autoencoder (SSAE) is presented in this research. Firstly, the fault signal with obvious time-domain characteristics is extracted by constrained independent component analysis (CICA), and the fault signals and noise is separated. Then, calculating the correlation kurtosis value of the time domain signals at different iteration periods as the eigenvalue to obtain the training samples and the test samples. The parameters of the network layer, the number of hidden nodes and learning rate are determined to build the model of SSAE. In the end, the training samples are input into the model for training and the whole network is fine-tuned. The advantages and disadvantages of the model are verified by the test samples. The intelligent classification and diagnosis of the mechanical running state are completed. Experiments analysis with real datasets of planetary wheel bearing show that the proposed method can achieve higher accuracy and robustness for fault classification compared with other data-driven methods. The application of this method in other major machinery industry also has bright prospects.

Gaining Insights in Loading Events for Wind Turbine Drivetrain Prognostics

Wind energy is one of the largest sources of renewable energy in the world. To further reduce the operations and maintenance (O&M) costs of wind farms, it is essential to be able to accurately pinpoint the root causes of different failure modes of interest. An example of such a failure mode that is not yet fully understood is white etching cracks (WEC). This can cause the bearing lifetime to be reduced to 5–10% of its design value. Multiple hypotheses are available in literature concerning its cause. To be able to validate or disprove these hypotheses, it is essential to have historic high-frequency measurement data (e.g., load and vibration levels) available. In time, this will allow linking to the history of the turbine operating data with failure data. This paper discusses the dynamic loading on the turbine during certain events (e.g., emergency stops, run-ups, and during normal operating conditions). By combining the number of specific events that each turbine has seen with the severity of each event, it becomes possible to assess which turbines are most likely to show signs of damage.

Aeromechanical Analysis of a Complete Wind Turbine Using Nonlinear Frequency Domain Solution Method

The high-fidelity computational fluid dynamics (CFD) simulations of a complete wind turbine model usually require significant computational resources. It will require much more resources if the fluid-structure interactions between the blade and the flow are considered, and it has been the major challenge in the industry. The aeromechanical analysis of a complete wind turbine model using a high-fidelity CFD method is discussed in this paper. The distinctiveness of this paper is the application of the nonlinear frequency domain solution method to analyse the forced response and flutter instability of the blade as well as to investigate the unsteady flow field across the wind turbine rotor and the tower. This method also enables the aeromechanical simulations of wind turbines for various inter blade phase angles in a combination with a phase shift solution method. Extensive validations of the nonlinear frequency domain solution method against the conventional time domain solution method reveal that the proposed frequency domain solution method can reduce the computational cost by one to two orders of magnitude.

Probabilistic Analysis of the Fatigue Life of Offshore Wind Turbine Structures

The structural response of the main components of offshore wind turbines (OWTs) is considerably sensitive to amplification as their excitation frequencies approach the natural frequency of the structure. Furthermore, uncertainties present in the loading conditions, soil and structural properties highly influence the dynamic response of the OWT. In most cases, the cost of the structure reaches around 30% of the entire OWT because conservative design approaches are employed to ensure its reliability. As a result, this study aims to address the following research question: can the structural reliability of OWT under fatigue loading conditions be predicted more consistently? The specific aims are to (1) establish the design parameters that most impact the fatigue life, (2) determine the probability distributions of the design parameters, and (3) predict the structural reliability. An analytical model to determine the fatigue life of the structure under 15 different loading conditions and two different locations were developed. Global sensitivity analysis was used to establish the more important design parameters. Also, a systematic uncertainty quantification (UQ) scheme was employed to model the uncertainties of model input parameters based on their available information. Finally, the framework used reliability analysis to consistently determine the system probability of failure of the structure based on the fatigue limit state design criterion. The results show high sensitivity for parameters usually considered as deterministic values in design standards. Additionally, it is shown that applying systematic UQ produces a better approximation of the fatigue life under uncertainty and more accurate estimations of the structural reliability. Consequently, more reliable and robust structural designs may be achieved without the need for overestimating the offshore wind turbine response.

Comparison of Blind Diagnostic Indicators for Condition Monitoring of Wind Turbine Gearbox Bearings

Under the pressure of climate change, renewable energy gradually replaces fossil fuels and plays nowadays a significant role in energy production. Among different types of energy sources, wind power covered 14% of the EU’s electricity demand in 2018. The Operations and Maintenance (O&M) costs of wind turbines may easily reach up to 20–25% of the total leverised cost per kWh produced over the lifetime of the turbine for a new unit. According to Wood Mackenzie Power & Renewables (WMPR) onshore wind farm operators are expected to spend nearly $15 billion on O&M services in 2019. Manufacturers and operators try to reduce O&M on one hand by developing new turbine designs and on the other hand by adopting condition monitoring approaches. One of the most critical and rather complex assembly of wind turbines is the gearbox. Gearboxes are designed to last till the end of asset’s lifetime, according to the IEC 61400-4 standards. On the other hand, a recent study over approximately 350 offshore wind turbines indicated that gearboxes might have to be replaced as early as 6.5 years. Therefore a plethora of sensor types and signal processing methodologies have been proposed in order to accurately detect and diagnose the presence of a fault. Among others, Envelope Analysis is one of the most important methodologies, where an envelope of the vibration signal is estimated, usually after filtering around a selected frequency band excited by impacts due to the fault. Sometimes the gearbox is equipped with many acceleration sensors and its kinematics is clearly known. In these cases Cyclostationary Analysis and the corresponding methodologies, i.e. the Cyclic Spectral Correlation and the Cyclic Spectral Coherence, have been proposed as powerful tools. On the other hand often the gearbox is equipped with a limited number of sensors and a simple global diagnostic indicator is demanded, being capable to detect globally various faults of different components. The scope of this paper is the application and comparison of a number of blind global diagnostic indicators which are based on Entropy (Permutation entropy, Approximate entropy, Samples entropy, Fuzzy entropy, Conditional entropy and Wiener entropy), on Negentropy (Infogram), on Sparsity (Sparse-L2/L1, Sparse-L1/L0, Sparse-Gini index) and on Statistics (Mean, Standard deviation, Kurtosis, etc.). The performance of the indicators is evaluated and compared on a wind turbine data set, consisted of vibration data captured by one accelerometer mounted on six 2.5 MW wind turbines, located in a wind park in northern Sweden, where two different bearing faults have been filed, for one wind turbine, during a period of 46 months. Among the different diagnostic indicators Permutation entropy, Approximate entropy, Samples entropy, Fuzzy entropy, Conditional entropy and Wiener entropy achieve the best results detecting blindly the two failure events.