Machine Learning-Based Tools for Wind Turbine Acoustic Monitoring

The identification and separation of sound sources has always been a difficult problem for acoustic technicians to tackle. This is due to the considerable complexity of a sound that is made up of many contributions at different frequencies. Each sound has a specific frequency spectrum, but when many sounds overlap it becomes difficult to discriminate between the different contributions. In this case, it can be extremely useful to have a tool that is capable of identifying the operating conditions of an acoustic source. In this study, measurements were made of the noise emitted by a wind turbine in the vicinity of a sensitive receptor. To identify the operating conditions of the wind turbine, average spectral levels in one-third octave bands were used. A model based on a support vector machine (SVM) was developed for the detection of the operating conditions of the wind turbine; then a model based on an artificial neural network was used to compare the performance of both models. The high precision returned by the simulation models supports the adoption of these tools as a support for the acoustic characterization of noise in environments close to wind turbines.

Download Full-text

Model-Based Design and Hardware-in-the-Loop Simulation of Engine Lean Operation

Volume 3: Industrial Applications; Modeling for Oil and Gas, Control and Validation, Estimation, and Control of Automotive Systems; Multi-Agent and Networked Systems; Control System Design; Physical Human-Robot Interaction; Rehabilitation Robotics; Sensing and Actuation for Control; Biomedical Systems; Time Delay Systems and Stability; Unmanned Ground and Surface Robotics; Vehicle Motion Controls; Vibration Analysis and Isolation; Vibration and Control for Energy Harvesting; Wind Energy ◽

10.1115/dscc2014-6000 ◽

2014 ◽

Author(s):

Pushkar Agashe ◽

Yang Li ◽

Bo Chen

Keyword(s):

Gasoline Engine ◽

Control Strategies ◽

Electronic Control Unit ◽

Control Unit ◽

Simulation Models ◽

Operating Conditions ◽

Hardware In The Loop ◽

Vehicle Performance ◽

Model Based ◽

Hil Simulation

This paper presents model-based design and hardware-in-the-loop (HIL) simulation of engine lean operation. The functionalities of the homogeneous combustion subsystem in engine Electronic Control Unit (ECU) in dSPACE Automotive Simulation Models (ASM) are first analyzed. To control the gasoline engine in lean operation without the drop of output torque, the combustion subsystem in engine ECU is modified by introducing two control loops, torque modifier and fuel multiplier. The performance of these two controllers is evaluated by HIL simulation using a dSPACE HIL simulator. The HIL simulation models, including vehicle plant model and softECUs in HIL simulator and engine lean control model in hardware engine ECU are modeled using model-based design. With HIL simulation, the designed engine control strategies can be immediately tested to evaluate the overall vehicle performance. The HIL simulation results show that the designed lean combustion control strategy can reduce fuel consumption and is able to meet the torque requirement at lean engine operating conditions.

Download Full-text

Prediction of Remaining Useful Life of Wind Turbine Bearings under Non-Stationary Operating Conditions

Energies ◽

10.3390/en11123318 ◽

2018 ◽

Vol 11 (12) ◽

pp. 3318 ◽

Cited By ~ 4

Author(s):

Lixiao Cao ◽

Zheng Qian ◽

Hamid Zareipour ◽

David Wood ◽

Ehsan Mollasalehi ◽

...

Keyword(s):

Wind Turbine ◽

Wind Turbines ◽

Wavelet Packet ◽

Health Indicators ◽

Degradation Process ◽

Remaining Useful Life ◽

Operating Conditions ◽

Support Vector ◽

Useful Life ◽

Artificial Neural Network Ann

Wind-powered electricity generation has grown significantly over the past decade. While there are many components that might impact their useful life, the gearbox and generator bearings are among the most fragile components in wind turbines. Therefore, the prediction of remaining useful life (RUL) of faulty or damaged wind turbine bearings will provide useful support for reliability evaluation and advanced maintenance of wind turbines. This paper proposes a data-driven method combining the interval whitenization method with a Gaussian process (GP) algorithm in order to predict the RUL of wind turbine generator bearings. Firstly, a wavelet packet transform is used to eliminate noise in the vibration signals and extract the characteristic fault signals. A comprehensive analysis of the real degradation process is used to determine the indicators of degradation. The interval whitenization method is proposed to reduce the interference of non-stationary operating conditions to improve the quality of health indicators. Finally, the GP method is utilized to construct the model which reflects the relationship between the RUL and health indicators. The method is assessed using actual vibration datasets from two wind turbines. The prediction results demonstrate that the proposed method can reduce the effect of non-stationary operating conditions. In addition, compared with the support vector regression (SVR) method and artificial neural network (ANN), the prediction accuracy of the proposed method has an improvement of more than 65.8%. The prediction results verify the effectiveness and superiority of the proposed method.

Download Full-text

Small Wind Turbine Emulator Based on Lambda-Cp Curves Obtained under Real Operating Conditions

Energies ◽

10.3390/en12132456 ◽

2019 ◽

Vol 12 (13) ◽

pp. 2456

Author(s):

Camilo I. Martínez-Márquez ◽

Jackson D. Twizere-Bakunda ◽

David Lundback-Mompó ◽

Salvador Orts-Grau ◽

Francisco J. Gimeno-Sales ◽

...

Keyword(s):

Power Control ◽

Wind Turbine ◽

Low Cost ◽

Operating Conditions ◽

Control Algorithms ◽

Graphic User Interface ◽

Small Wind Turbine ◽

Power Electronics Converters ◽

Control Curves

This paper proposes a new on-site technique for the experimental characterization of small wind systems by emulating the behavior of a wind tunnel facility. Due to the high cost and complexity of these facilities, many manufacturers of small wind systems do not have a well knowledge of the characteristic λ - C p curve of their turbines. Therefore, power electronics converters connected to the wind generator are usually programmed with speed/power control curves that do not optimize the power generation. The characteristic λ - C p curves obtained through the proposed method will help manufacturers to obtain optimized speed/power control curves. In addition, a low cost small wind emulator has been designed. Programmed with the experimental λ - C p curve, it can validate, improve, and develop new control algorithms to maximize the energy generation. The emulator is completed with a new graphic user interface that monitors in real time both the value of the λ - C p coordinate and the operating point on the 3D working surface generated with the characteristic λ - C p curve obtained from the real small wind system. The proposed method has been applied to a small wind turbine commercial model. The experimental results demonstrate that the point of operation obtained with the emulator is always located on the 3D surface, at the same coordinates (rotor speed/wind speed/power) as the ones obtained experimentally, validating the designed emulator.

Download Full-text

Data-Driven Model-Based Fault Diagnosis in a Wind Turbine With Actuator Faults

Volume 4B: Dynamics, Vibration, and Control ◽

10.1115/imece2014-38686 ◽

2014 ◽

Author(s):

Hamed Badihi ◽

Javad Soltani Rad ◽

Youmin Zhang ◽

Henry Hong

Keyword(s):

Wind Turbine ◽

Wind Turbines ◽

Dynamic Models ◽

Full Range ◽

Fault Detection And Diagnosis ◽

Operating Conditions ◽

Data Driven ◽

Offshore Wind Turbine ◽

Model Based ◽

Highly Nonlinear

Wind turbines are renewable energy conversion devices that are being deployed in greater numbers. However, today’s wind turbines are still expensive to operate, and maintain. The reduction of operational and maintenance costs has become a key driver for applying low-cost, condition monitoring and diagnosis systems in wind turbines. Accurate and timely detection, isolation and diagnosis of faults in a wind turbine allow satisfactory accommodation of the faults and, in turn, enhancement of the reliability, availability and productivity of wind turbines. The so–called model-based Fault Detection and Diagnosis (FDD) approaches utilize system model to carry out FDD in real-time. However, wind turbine systems are driven by wind as a stochastic aerodynamic input, and essentially exhibit highly nonlinear dynamics. Accurate modeling of such systems to be suitable for use in FDD applications is a rather difficult task. Therefore, this paper presents a data-driven modeling approach based on artificial intelligence (AI) methods which have excellent capability in describing complex and uncertain systems. In particular, two data-driven dynamic models of wind turbine are developed based on Fuzzy Modeling and Identification (FMI) and Artificial Neural Network (ANN) methods. The developed models represent the normal operating performance of the wind turbine over a full range of operating conditions. Consequently, a model-based FDD scheme is developed and implemented based on each of the individual models. Finally, the FDD performance is evaluated and compared through a series of simulations on a well-known large offshore wind turbine benchmark in the presence of wind turbulences, measurement noises, and different realistic fault scenarios in the generator/converter torque actuator.

Download Full-text

Thermal and Electrical Characterization of a Semi-Transparent Dye Sensitized Photovoltaic Module under Real Operating Conditions

10.20944/preprints201712.0046.v1 ◽

2017 ◽

Author(s):

Cristina Cornaro ◽

Ludovica Renzi ◽

Marco Pierro ◽

Aldo Di Carlo ◽

Alessandro Guglielmotti

Keyword(s):

Low Cost ◽

Electrical Characterization ◽

Simulation Models ◽

Gain Coefficient ◽

Operating Conditions ◽

Photovoltaic Module ◽

Glass Substrates ◽

Energy Saving Potential ◽

Dye Sensitized

Dye sensitized solar cell technology is having an important role in renewable energy research due to its features and low cost manufacturing processes. Devices based on this technology appear very well suited for integration into glazing systems due to their characteristics of transparency, color tuning and manufacturing directly on glass substrates. Field data of thermal and electrical characteristics of dye sensitized solar modules (DSM) are important since they can be used as input of building simulation models for the evaluation of their energy saving potential when integrated into buildings. However still few works in the literature provide this information. The study here presented wants to contribute to fill this gap providing a thermal and electrical characterization of a DSM in real operating conditions using a method developed in house. This method uses experimental data coming from test boxes exposed outdoor and dynamic simulation to provide thermal transmittance and solar heat gain coefficient (SHGC) of a DSM prototype. The device exhibits an U-value of 3.6 W/m2K, confirmed by an additional measurement carried on in the lab using a heat flux meter, and a SHGC of 0.2, value compliant with literature results. Electrical characterization evidences an increase of module power with respect to temperature causing DSM suitable for integration in building facades.

Download Full-text

Viscosity of Ionic Liquids: Application of the Eyring’s Theory and a Committee Machine Intelligent System

Molecules ◽

10.3390/molecules26010156 ◽

2020 ◽

Vol 26 (1) ◽

pp. 156

Author(s):

Seyed Pezhman Mousavi ◽

Saeid Atashrouz ◽

Menad Nait Amar ◽

Abdolhossein Hemmati-Sarapardeh ◽

Ahmad Mohaddespour ◽

...

Keyword(s):

Ionic Liquids ◽

Intelligent System ◽

Operating Conditions ◽

Boiling Temperature ◽

Develop Model ◽

Support Vector ◽

Rate Theory ◽

Committee Machine ◽

Model Based ◽

Marquardt Algorithm

Accurate determination of the physicochemical characteristics of ionic liquids (ILs), especially viscosity, at widespread operating conditions is of a vital role for various fields. In this study, the viscosity of pure ILs is modeled using three approaches: (I) a simple group contribution method based on temperature, pressure, boiling temperature, acentric factor, molecular weight, critical temperature, critical pressure, and critical volume; (II) a model based on thermodynamic properties, pressure, and temperature; and (III) a model based on chemical structure, pressure, and temperature. Furthermore, Eyring’s absolute rate theory is used to predict viscosity based on boiling temperature and temperature. To develop Model (I), a simple correlation was applied, while for Models (II) and (III), smart approaches such as multilayer perceptron networks optimized by a Levenberg–Marquardt algorithm (MLP-LMA) and Bayesian Regularization (MLP-BR), decision tree (DT), and least square support vector machine optimized by bat algorithm (BAT-LSSVM) were utilized to establish robust and accurate predictive paradigms. These approaches were implemented using a large database consisting of 2813 experimental viscosity points from 45 different ILs under an extensive range of pressure and temperature. Afterward, the four most accurate models were selected to construct a committee machine intelligent system (CMIS). Eyring’s theory’s results to predict the viscosity demonstrated that although the theory is not precise, its simplicity is still beneficial. The proposed CMIS model provides the most precise responses with an absolute average relative deviation (AARD) of less than 4% for predicting the viscosity of ILs based on Model (II) and (III). Lastly, the applicability domain of the CMIS model and the quality of experimental data were assessed through the Leverage statistical method. It is concluded that intelligent-based predictive models are powerful alternatives for time-consuming and expensive experimental processes of the ILs viscosity measurement.

Download Full-text

Numerical Framework for Aerodynamic Characterization of Wind Turbine Airfoils: Application to Miniature Wind Turbine WiRE-01

Energies ◽

10.3390/en13215612 ◽

2020 ◽

Vol 13 (21) ◽

pp. 5612

Author(s):

Tristan Revaz ◽

Mou Lin ◽

Fernando Porté-Agel

Keyword(s):

Wind Turbine ◽

Computational Cost ◽

Aerodynamic Performance ◽

Operating Conditions ◽

Eddy Simulation ◽

Turbulent Inflow ◽

Inflow Turbulence ◽

Volume Method ◽

Turbine Airfoils

A numerical framework for the aerodynamic characterization of wind turbine airfoils is developed and applied to the miniature wind turbine WiRE-01. The framework is based on a coupling between wall-resolved large eddy simulation (LES) and application of the blade element momentum theory (BEM). It provides not only results for the airfoil aerodynamics but also for the wind turbine, and allows to cover a large range of turbine operating conditions with a minimized computational cost. In order to provide the accuracy and the flexibility needed, the unstructured finite volume method (FVM) and the wall-adapting local eddy viscosity (WALE) model are used within the OpenFOAM toolbox. With the purpose of representing the turbulence experienced by the blade sections of the turbine, a practical turbulent inflow is proposed and the effect of the inflow turbulence on the airfoil aerodynamic performance is studied. It is found that the consideration of the inflow turbulence has a strong effect on the airfoil aerodynamic performance. Through the application of the framework to WiRE-01 miniature wind turbine, a comprehensive characterization of the airfoil used in this turbine is provided, simplifying future studies. In the same time, the numerical results for the turbine are validated with experimental results and good consistency is found. Overall, the airfoil and turbine designs are found to be well optimized, even if the effective angle of attack of the blades should be reduced close to the hub.

Download Full-text

An Online Hybrid Model for Temperature Prediction of Wind Turbine Gearbox Components

Energies ◽

10.3390/en12203920 ◽

2019 ◽

Vol 12 (20) ◽

pp. 3920

Author(s):

Qiang Zhao ◽

Kunkun Bao ◽

Jia Wang ◽

Yinghua Han ◽

Jinkuan Wang

Keyword(s):

Prediction Model ◽

Wind Turbine ◽

Error Correction ◽

Hybrid Model ◽

Wind Turbines ◽

Short Term Memory ◽

Operating Conditions ◽

Temperature Prediction ◽

Wind Turbine Gearbox ◽

Model Based

Condition monitoring can improve the reliability of wind turbines, which can effectively reduce operation and maintenance costs. The temperature prediction model of wind turbine gearbox components is of great significance for monitoring the operation status of the gearbox. However, the complex operating conditions of wind turbines pose grand challenges to predict the temperature of gearbox components. In this study, an online hybrid model based on a long short term memory (LSTM) neural network and adaptive error correction (LSTM-AEC) using simple-variable data is proposed. In the proposed model, a more suitable deep learning approach for time series, LSTM algorithm, is applied to realize the preliminary prediction of temperature, which has a stronger ability to capture the non-stationary and non-linear characteristics of gearbox components temperature series. In order to enhance the performance of the LSTM prediction model, the adaptive error correction model based on the variational mode decomposition (VMD) algorithm is developed, where the VMD algorithm can effectively solve the prediction difficulty issue caused by the non-stationary, high-frequency and chaotic characteristics of error series. To apply the hybrid model to the online prediction process, a real-time rolling data decomposition process based on VMD algorithm is proposed. With aims to validate the effectiveness of the hybrid model proposed in this paper, several traditional models are introduced for comparative analysis. The experimental results show that the hybrid model has better prediction performance than other comparative models.

Download Full-text