Presence Of Wind Turbines Research Articles

Thermally sprayed coatings for highly stressed sliding bearings

Sliding bearings have to fulfil various requirements depending on the operating conditions. Conventional sliding bearings, however, cannot be used for critical operating conditions such as frequent acceleration and shut-down procedures or they require the use of complex manufacturing processes and cost-intensive material solutions. For this reason, complex multilayer systems or rolling bearings are currently used for frequent start-up cycles and low relative velocities. New material systems and manufacturing processes are of particular interest in order to operate sliding bearings cost-effectively and reliably even under these increased requirements.Within the scope of this study, new material concepts are developed by thermal spraying. Thermal spraying allows the production of innovative material combinations, which cannot be produced by melting metallurgy. In addition, coatings can be applied by thermal spraying on a wide variety of geometries directly on the bearing support structure, thus achieving increased freedom of design and additional cost savings compared to conventional processes. The coating concepts are based on a cobalt or bronze matrix into which friction-reducing solid lubricants can be integrated during the coating process. One particular goal of the coating concepts is to improve the operating properties under critical conditions, such as low relative velocities and high loads. Therefore, the developed coatings are tested on a modified ring-on-disc tribometer and are compared to a reference sliding bearing material produced by conventional casting. To verify the coating capability, the most promising coating is investigated on a full-scale main bearing test bench of a wind turbine. For the first time, a thermally sprayed coating and a new conical sliding bearing design are evaluated as a substitution for currently used rolling bearings as main bearings in wind turbines. Hereby, it is demonstrated whether these new coating concepts can meet the requirements of this highly stressed bearing position.

Research on Extraction of Compound Fault Characteristics for Rolling Bearings in Wind Turbines.

Wind turbines work in strong background noise, and multiple faults often occur where features are mixed together and are easily misjudged. To extract composite fault of rolling bearings from wind turbines, a new hybrid approach was proposed based on multi-point optimal minimum entropy deconvolution adjusted (MOMEDA) and the 1.5-dimensional Teager kurtosis spectrum. The composite fault signal was deconvoluted using the MOMEDA method. The deconvoluted signal was analyzed by applying the 1.5-dimensional Teager kurtosis spectrum. Finally, the frequency characteristics were extracted for the bearing fault. A bearing composite fault signal with strong background noise was utilized to prove the validity of the method. Two actual cases on bearing fault detection were analyzed with wind turbines. The results show that the method is suitable for the diagnosis of wind turbine compound faults and can be applied to research on the health behavior of wind turbines.

Optimal stochastic bilevel scheduling of pumped hydro storage systems in a pay-as-bid energy market environment

• The energy storage arbitrage problem is investigated for pay-as-bid energy market. • The problem is modeled as a bi-level programming resulting price-quantity pairs. • The bi-level problem is linearized and solved efficiently using Mccormick envelops. • The complementarity constraints modeled exactly using SOS1 variables. • The results are compared to show the different scheduling of storage system. This paper proposes a stochastic bilevel optimization approach to owners of pumped hydro storage systems (PHSSs) to participate in pay-as-bid power market and provide optimal bids and offers. The price offering of other generation units is modeled by stochastic programming. The upper-level of the proposed bilevel programming seeks maximization of the profit of the PHSS arbitrage, where the lower-level assures the optimal system dispatching (and market-clearing), and keeps the network security. The bilevel optimization is then transferred into a single-level equivalent via Karush-Kuhn-Tucker (KKT) complementarity conditions. The equations of the KKT conditions are linearly modeled using special ordered sets of type 1 (SOS1) variables. Furthermore, the bilinear objective function of the upper-level is approximated using McCormick envelopes relaxation method, in order to obtain the solutions as fast as possible and making the problem as mixed-integer linear programming. The proposed method is verified on the IEEE 24-bus reliability test system (RTS) considering different cases. The operation of a single PHSS is assessed in the network's normal and congested conditions. The results show that the PHSS achieve more revenue in a limited network as the offered prices go up to price cap in some periods. In the studied cases, the profit of energy arbitrage by the PHSS increases from $ 3302 in a normal network, to $ 8170 in a limited network. Moreover, the effect of wind generation uncertainty on the arbitrage problem is investigated using five sub-scenarios dedicated to wind generation. It is shown that the arbitrage profit is more sensitive on generation cost uncertainty rather than wind generation uncertainty. Furthermore, it is revealed that the expected profit in the presence of wind turbines is slightly lower than that without wind turbines. This is due to the fact that the wind generation power is always accepted and dispatched in the market and lowers the load demand and deteriorates the arbitrage opportunity. In this case the expected profit of the PHSS is decreased from $ 3302 to $ 3288. Furthermore, the effects of three PHSS units in the mentioned network is investigated. For a particular PHSS in the studied system, it is found that the increase of PHSS units in the network decreased the expected profit from $ 3302 to about $ 3176. Also, the location of PHSS units is deduced as an influential factor on profitability of merchant storage facilities.

A review modeling of optimal location and sizing integrated M–FACTS with wind farm and fuel cell

In recent years, due to the reduction of non-renewable resources and increasing environmental pollution, exploiting of renewable energy sources is a good solution. Wind turbines and fuel cells are appropriate sources of renewable energy. On the other hand, FACTS devices are used to maximize the power injection capability to supply electricity demand and minimizing voltage deviation, power losses and operational cost of power system. Generalized Unified Power Flow Controller (GUPFC) and Interline Power Flow Controller (IPFC) are two kinds of the best FACTS devices. GUPFC can adjust the voltage and the power flow in the connected transmission lines synchronically. IPFC is capable of minimizing voltage drops and reactive power of line, enhancing the system stability against the dynamic disturbance, and load ability of the system. In this paper, with the aim of minimizing the cost function and improving the performance of system wind farm and fuel cell are integrated with power system in the presence of GUPFC and IPFC via power injection model. Then, using a new algorithm integrated with the Newton-Raphson method, the best place for installing IPFC and GUPFC is funded. This algorithm is improved squirrel search which is inspired by nature. On the other hand, the optimum range of parameter (value and angle of injected voltage) is determined with the aim of achieving the best performance of system. Ultimately, the proposed method has been applied in IEEE 57 Bus system in the presence of wind turbine, fuell cell and DG to test the effect of M-FACTS. The result confirms that the new method improved voltage profile, minimized the operational cost and power losses of system. Final, it is concluded that the performance of GUPFC is better than the IPPFC.

A methodology for reliability assessment and prognosis of bearing axial cracking in wind turbine gearboxes

This article describes an interdisciplinary methodology to calculate the probability of failure for bearing axial cracking, the dominant failure mode in the intermediate and high-speed stages of many wind turbine gearboxes. This approach is mainly a physics-domain method with needed inputs from the data domain. The gearbox and bearing design along with operations data and component failure records from a wind power plant provide the input to physics-based models and define axial cracking damage metrics. The physics-domain models predict the bearing loads and sliding velocities, which are the essential elements for quantifying the accumulated frictional energy. Both accumulated frictional energy and electrical energy generation are proposed as damage metrics for bearing axial cracking. A first-order reliability method is then used to compare the proposed damage metrics to failure threshold functions and calculate the probability of failure of each individual bearing. Although the probability of failure for the failed turbines is not separated from the population, a feature engineering analysis shows the potential of frictional energy as a damage metric when combined with roller loads, bearing sliding speed, lubricant type, and terrain features. Through statistical analysis of historical data, the proposed methodology enables reliability assessment of axial cracking in individual wind turbine bearings and connects the reliability forecast with turbine design and operations.

Improved Remaining Useful Life Estimation of Wind Turbine Drivetrain Bearings Under Varying Operating Conditions

The failure progression of wind turbine bearings comprises of multiple degraded health states due to applied load by varying operating conditions (VOC). Therefore, determining the VOC impact on the failure dynamics severity is an essential task for bearing failure prognostics. This article introduces a hybrid prognosis method using real-time supervisory control and data acquisition (SCADA) and vibration signals to predict remaining useful life (RUL) for wind turbine bearings. The SCADA data are utilized to define the role of environmental conditions such as wind speed and ambient temperature in bearing failure dynamics. Afterward, for each environmental condition, failure dynamics are identified by the vibration signal. Finally, RUL of the faulty bearings is forecast via an adaptive Bayesian algorithm using the failure dynamics, conditional to the VOC. The efficacy of the method is validated using experimental data, and test results indicate a higher RUL accuracy compared to the Bayesian algorithm.

A study of grease lubricants under wind turbine pitch bearing conditions

Pitch bearings in wind turbines are affected by reciprocating motion due to pitch control. The combination of oscillating operation, high loads, and mixed lubrication often leads to wear. Grease lubricants in wind turbine pitch bearings should be designed to avoid such wear. Due to different available grease lubricants, the anti-wear properties are investigated under downscaled wind turbine pitch bearing conditions. The downscaling is accomplished by load simulation for a pitch bearing and analysis of the pitch movements for a 7.5 MW reference turbine. The operational conditions of the four point contact ball bearing with 5 m outer diameter are scaled to angular contact ball bearings of the size 7208. The investigations are concluded with contact model experiments. Six Industrial grease lubricants for wind turbine pitch bearings are tested that follow very different compositions. The investigations reveal, that none of the tested grease lubricants was able to reduce wear for all tested conditions. After 250000 cycles certain conditions lead to severe wear. Therefore, the pitch controller should avoid such conditions if possible. Nevertheless, grease lubricants with low base oil viscosities and high bleeding rates are best on average for the tested conditions. Furthermore, the results of the bearing experiments are comparable to the model experiments.

Weak fault detection for wind turbine bearing based on ACYCBD and IESB

To detect the rolling bearing fault of a wind turbine at incipient injury phase, a novel method combining adaptive cyclostationary blind deconvolution (ACYCBD) with instantaneous energy slice bispectrum (IESB) is proposed. Cuckoo search algorithm (CSA) is fused with cyclostationary blind deconvolution (CYCBD) to optimize cyclic frequency and filter length parameters, and the satisfied processing result is able to adaptively use this developed ACYCBD method. Besides, fusing the complementary superiorities of frequency weighted energy operator and slice bispectrum, a new frequency domain analysis method named IESB is put forward to identify the characteristic frequency components. During fault diagnosis, the deconvolution signal with higher signal to noise ratio is first separated from the collected signal by applying ACYCBD method. However, owing to the intense background noises, external interferences still remain in the deconvolution signal. Then the IESB is further used as a post-processing method, the residual noises are suppressed effectively, and the characteristic spectral lines are highlighted immensely. In the end, the incipient fault injury of wind turbine bearing can be diagnosed by analyzing the acquired instantaneous energy slice bispectrum. The analyzed results of wind turbine bearing fault signals from the experimental rig and the actual engineering testing field demonstrate the feasibility and the superiority of this novel detection method.

Remaining life prediction of wind turbine bearing based on Wiener process

In order to know about the real-time operating condition and the remaining life of the wind turbine bearings in time, a method of life prediction was proposed based on Wiener process. Firstly, the relationship model of bearing temperature parameters and speed parameters of wind turbine was established by using multiple linear regression; Secondly, the established relationship model was been used to build the real-time monitoring and performance degradation model of the wind generator bearings; Thirdly, Linear Wiener process and nonlinear Wiener process were respectively used to establish the remaining life prediction model of wind turbine bearing; Finally, the feasibility and effectiveness of the method in practical application were verified by analyzing the temperature data of a wind turbine’s gearbox.

Measurement of roller load, load variation, and lubrication in a wind turbine gearbox high speed shaft bearing in the field

The variability of applied load and the integrity of lubrication are crucial factors dictating the durability of wind turbine gearbox bearings. In this work, new ultrasonic reflectometry techniques have been implemented to measure both load and lubrication in the high-speed shaft bearing of a field operational Vestas V42 wind turbine gearbox.Miniature piezoelectric transducers were bonded onto the bearing outer raceway. The reflected pulses were used to infer bearing load and lubrication. Roller load measurements were seen to vary by 33% across the roller complement, with similar trend observed for reflection coefficient. Inspection of the reflection coefficient patterns were able to show whether the roller inlet region was fully flooded or partially starved, capturing the stochastic behaviour of bearing lubrication.

Simultaneous Multi-area Economic-Environmental Load Dispatch Modeling in Presence of Wind Turbines by MOPSO

Multi-objective particle swarm optimization (MOPSO) algorithm was proposed in this paper to solve the load dispatch problem among thermal and wind turbines considering the environmental factors in multi-area power systems. The unpredictable nature of wind generations has resulted in the higher complexity of the economic load dispatch (ELD) problem. As the wind farm’s generation is very difficult to predict, the extra cost during the evaluation of the ELD are taken into account as well. In the developed method, the storage and additional cost are defined in the objective function of the ELD problem, using the mean value of wind energy density. The operational constraints among multi-areas, including the transmission lines capacity limit and generation-demand balance constraints as well as the constraints of generation limit of generators are considered in the economic-environmental load dispatch (EELD) problem. The efficiency of the proposed method was evaluated based on the simulation results on IEEE 118 bus test system, considering twelve thermal power plants, two wind farms and three areas.

An Integrated Feature-Based Failure Prognosis Method for Wind Turbine Bearings

In North America, many utility-scale turbines are approaching the half-way point of their anticipated initial lifespan. Accurate remaining useful life (RUL) estimation can provide wind farm owners insight into how to optimize the life and value of their farm assets. An improved understanding of the RUL of turbine components is particularly essential as many owners consider retiring, life-extending, or repowering their farms. In this article, an integrated prognosis method based on signal processing and an adaptive Bayesian algorithm is proposed to predict the RUL of various faulty bearings in wind turbines. The signal processing leverages feature extraction, feature selection, and signal denoising to detect the dynamics of various failures. Then, RUL of the faulty bearings is forecast via the adaptive Bayesian algorithm using the extracted features. Finally, a new fusion method based on an ordered weighted averaging (OWA) operator is applied to the RUL obtained from the features to improve accuracy. The efficacy of the method is evaluated using data from historical failures across three different Canadian wind farms. Experimental test results indicate that the OWA operator delivers a higher accuracy for RUL prediction compared to the other feature-based methods and Choquet integral fusion technique.

ThermalTracker-3D: A thermal stereo vision system for quantifying bird and bat activity at offshore wind energy sites

We present a new, efficient method for extracting three-dimensional animal motion trajectories from thermal stereo video data. Understanding animal behavior in the wild or other unconstrained environments is often based on animal movements. The technology described here is for understanding how bird and bat behavior is affected by the presence of wind turbines, specifically offshore wind turbines which are challenging to monitor. There is a need for both baseline data prior to wind farm construction and post-construction data when the turbines are operating. Thermal cameras were chosen because they are equally effective both night and day. In previous work, we developed a method for generating two-dimensional images of animal motion using thermal video from a single camera. The motion track image is formed by combining a sequence of video frames into a single composite image that shows the entire flight trajectory. Here we demonstrate that the composite motion track images from a stereo pair of thermal cameras can be used directly to generate three-dimensional tracks in real time without the need for an explicit tracking algorithm. The method was evaluated using an unmanned aerial system (UAS) equipped with GPS. The UAS flew both straight and curving trajectories at distances between 50 and 325 m from the camera system. The ThermalTracker-3D estimated positions were within ±10 m of the GPS-derived positions in the x and y (flight height) dimensions, and within ±20 m in the z (range) dimension for 90% of the data points.The range estimates were within the bounds of the achievable accuracy of the cameras and camera arrangement used. The results demonstrate the practical usefulness of the method for assessing collision risk to seabirds at proposed offshore wind energy sites and for quantifying avoidance behavior at operating offshore wind farms.

Intelligent fault diagnosis of Wind Turbines via a Deep Learning Network Using Parallel Convolution Layers with Multi-Scale Kernels

In recent years, the intelligent diagnosis technology of wind turbines has made great progress. However, in practical engineering applications, the operating states of wind turbine are various, accompanied by a large number of noise interference, which leads to the decline of the discrimination accuracy of intelligent diagnosis. In order to solve this problem, inspired by the Google team Inception model, this paper proposes a concurrent convolution neural network (C–CNN), the raw data is fed into the network without any prior knowledge, and the characteristics are learned directly and adaptively from the input. Even if the data is accompanied by noise, the model still has high accuracy and strong generalization ability. The model is composed of a CNN with multiple branches. Meanwhile, the convolutional layer of different branches selects the kernels with different scales in same level, thus improving the learning ability of entire network. In this paper, the feasibility of this method for fault diagnosis of bearings in wind turbines is demonstrated by three bearing datasets. The results show that the proposed method can extract discriminative features and classify bearing data accurately under the disturbance of different rotating speed, different load and random noise.

A Robust Model-Based Approach for Bearing Remaining Useful Life Prognosis in Wind Turbines

Accurate remaining useful life prognosis of bearings in wind turbines can effectively help to schedule maintenance strategy and reduce operational costs at wind farms. Unscented particle filter is good at state tracking in nonlinear problem. A robust model-based approach based on improved unscented particle filter is presented to deal with bearing life prognosis in wind turbines, which involves: (1) The mean of sigma points after unscented Kalman transform is regarded as the particles in particle filter to guarantee the particles aggregation; (2) Several past measurements are utilized to estimate the likelihood function of current step; (3) Uniform distribution is adopted for resampling particles to make them diversity. The presented remaining useful life prognosis approach depends more on the measurement, rather than the initial parameters of degradation model, which makes it practicable for the on-site wind turbines. Three life-cycle bearings from wind turbine high-speed shafts demonstrate the effectiveness of the proposed approach.

Fault diagnosis of high power grid wind turbine based on particle swarm optimization BP neural network during COVID-19 epidemic period

During the COVID-19 pandemic, the maintenance of the wind turbine is unable to be processed due to the problem of personnel. This paper presents two neural network models: BP neural network and LSTM neural network combined with Particle Swarm Optimization (PSO) algorithm to realize obstacle maintenance detection for wind turbine. Aiming at the problem of gradient vanishing existing in the traditional regression neural network, a fault diagnosis model of wind turbine rolling bearing is proposed by using long-term and short-term memory neural network. Through the analysis of an example, it is verified that the diagnosis results of this method are consistent with the actual fault diagnosis results of wind turbine rolling bearing and the diagnosis accuracy is high. The results show that the proposed method can effectively diagnose the rolling bearing of wind turbine, and the long-term and short-term memory neural network still has good fault diagnosis performance when the difference of fault characteristics is not obvious, which shows the feasibility and effectiveness of the method.

Adaptive fractional‐order control of power system frequency in the presence of wind turbine

Inertial and droop control are common methods for improving grid frequency control. Using the first-order derivative of the frequency in the inertial control loop only investigates the rate of frequency variation, while other useful information is not taken into consideration. Accordingly, in this study, the application of a fractional-order derivative (between 1 and 2) in the inertial control loop is proposed to have more useful frequency-based information. To achieve this goal, firstly continued fraction approximations and secondly, the discretization is adopted by using the Tustin Method with Prewarping. Inertia loop and droop loop gain have a significant impact on the operation of the wind turbines and the power system frequency control. Therefore, in this paper, a novel method for the adaptive adjusting of droop and inertia control loops gains in DFIG is proposed. To utilize more frequency error information, fractional-order adaptive law is proposed to update the loop gains and the inertia control loop. In order to have a robust system, which is permanently stable for every fractional order (FO), is used parameter space method. Simulation results demonstrate the proper performance of the FO-based adaptive approach to increase the frequency nadir (FN) and decrease the frequency variations.

An engineering condition indicator for condition monitoring of wind turbine bearings

AbstractCondition monitoring (CM) of wind turbine becomes significantly important part of wind farms in order to cut down operation and maintenance costs. The large amount of CM system vibration data collected from wind turbines are posing challenges to operators in signal processing. It is crucial to design sensitive and reliable condition indicator (CI) in wind turbine CM system. Bearing plays an important role in wind turbine because of its high impact on downtime and component replacement. CIs for wind turbine bearing monitoring are reviewed in the paper, and the advantages and disadvantages of these indicators are discussed in detail. A new engineering CI (ECI), which combined the energy and kurtosis representation of the vibration signal, is proposed to meet the requirement of easy applicability and early detection in wind turbine bearing monitoring. The quantitative threshold setting method of the ECI is provided for wind turbine CM practice. The bearing run‐to‐failure experiment data analysis demonstrates that ECI can evaluate the overall condition and is sensitive to incipient fault of bearing. The effectiveness in engineering of ECI is validated though a certain amount of real‐world wind turbine generator and gearbox bearing vibration data.

Research on Fault Diagnosis Method of Wind Turbine Bearing Based on Deep belief Network

Bearings of wind turbines have become one of the components with high failure rate in wind turbines because of their bad operating environment. In this paper, a fault diagnosis model based on deep belief network is proposed for bearing fault diagnosis of wind turbine. The time-frequency spectrum of wind turbine bearing vibration data after short-time Fourier transform (STFT) is used as the input of fault diagnosis model, and the output is the identification code of various fault types of wind turbine bearing. Compared with the deep belief network diagnosis model based on the time domain signal input to the vibration data of wind turbine bearings, the deep belief network fault diagnosis model based on the short-time Fourier transform of the input signal has higher recognition accuracy. Based on the vibration data of different working conditions and rotating speeds, the model can automatically find fault features and identify the faults of rolling elements, inner rings and outer rings of rolling bearings at different locations, thus avoiding expert experience and feature engineering, making the model more versatile and generalizable and potential for efficient on-site rolling bearing fault diagnosis.

Fracture Mechanics Assessment of Large Diameter Wind Turbine Bearings

The structural integrity of large diameter wind turbine bearings have been investigated using the built-in “contour integral” tool in ABAQUS finite element software package by modeling three-dimensional penny-shaped cracks and evaluating the stress intensity factors. In order to sub-model a crack and investigate the fracture mechanics of the rolling contact between the rollers and the raceway, a python script was developed and implemented in the analysis. Important steps to build the crack model are detailed and recommendations are made for further use of the finite element modeling tool in compressive mixed mode fracture mechanics assessment of wind turbine bearings. Moreover, the influence of initial residual stresses due to induction hardening of the raceway is also investigated and discussed in this paper. For frictionless contacts between the two crack faces, “contour integral” in ABAQUS appears to be a suitable method to obtain accurate stress intensity factor solutions for modes II and III. The results from this study are validated through comparison with the analytical solutions available in the literature and are expected to facilitate numerical life assessment of wind turbine bearings.