Presence Of Wind Turbines Research Articles

Study on accelerated life tests for main shaft bearings in wind turbines

Study on accelerated life tests for main shaft bearings in wind turbines

Resilience enhancement of active distribution networks in the presence of wind turbines and energy storage systems by considering flexible loads

In recent years, global warming has led to a significant increase in the occurrence of disastrous conditions. These events always have devastating effects on distribution systems and lead to massive load shedding. Therefore, the structure of distribution systems must have sufficient strength and resilience to deal with these events. Hence, this paper presents a mixed-integer linear programming (MILP) framework for optimal placement of sectionalizing switches and backup distributed generators (DGs) to enhance resilience under emergency conditions. In the proposed model, low priority, medium priority and high priority loads are considered and a demand response (DR) program is also implemented. In order to evaluate the performance of the system during the extreme weather conditions, 15 scenarios have been generated by the fragility function and the outage rate of lines in each scenario has been calculated. The proposed model is implemented on a modified IEEE 33-bus distribution system and is solved in the form of five case studies by the CPLEX solver in GAMS software. The results demonstrate that optimal placement of switches leads to a 14.32% reduction in the amount energy not served (ENS) under emergency conditions. Also, the results illustrate that the optimal placement of DGs along with the possibility of topology change leads to a 57.03% reduction in the amount of ENS. Finally, the results show that applying DR program leads to a 26.7% reduction in load shedding of high priority loads.

Analysis of early fault vibration detection and analysis of offshore wind power transmission based on deep neural network

Among the main fault locations of wind turbines, downtime accidents caused by bearing faults account for the majority. Therefore, it is of practical significance to detect the fault signal of the fan bearing as early as possible, and to arrange the maintenance plan reasonably, which is of practical significance to reduce the maintenance cost and increase the power generation time. Therefore, this paper proposes an early fault vibration detection and analysis model for wind power transmission based on deep neural networks and sensors. First of all, this paper studies the failure mechanism, vibration mechanism and signal characteristics of common failures of rolling bearings, combined with sensor technology, summarises and designs various bearing failures and failure signal detection models. Secondly, the deep neural network algorithm is used for the interference situation of rolling bearing vibration signal and the process of data re-analysis. The experimental and simulation results verify the adaptability of the early fault vibration detection model of wind power transmission in the early fault processing results, which can provide a reference for the early weak fault signal processing of wind turbine bearings.

A modified nonlinear fatigue damage accumulation model for life prediction of rolling bearing under variable loading conditions

AbstractDuring the use of rolling bearings in wind turbines, aero engines, and other equipment, they often operate under variable operating and loading conditions. The practice has shown that the sequence of these loads and their interaction has a significant impact on the fatigue life of rolling bearings. In order to better understand this issue, a modified nonlinear fatigue damage accumulation model, which considers the influences of both load sequence and load interaction, is proposed in this paper and verified using the fatigue testing data for six materials. Then, the verified model was applied to evaluate the fatigue damage accumulation and fatigue life of a rolling bearing. The results have shown that, compared with the existing models, the proposed model can more accurately predict the damage accumulation and fatigue life of the rolling bearing under variable loading conditions.

Crack propagation modelling for service life prediction of large slewing bearings

Slewing bearings in wind turbines are meant to last 20 years or longer. But wind turbine accidents (structural failure) occur nevertheless in some cases earlier. Minimizing the risk and with this enhancing the public acceptance of wind turbines is of great importance for the increase of alternative energy production. For a more accurate prediction of the service life of slewing bearings XFEM calculations with crack initiation and crack propagation are developed using Abaqus XFEM. The material parameters such as Paris coefficients for crack propagation and fracture toughness values (KIC) were measured in advance on samples of different heat treatment conditions. With a linear elastic fracture mechanics approach the local stress intensity factor range ΔK for a given starting crack front is calculated. Coupling this information with the Paris-law and a maximum crack propagation interval of 0.1-0.3 mm, the number of cycles to failure during crack propagation is determined. The crack is extended stepwise until ΔK reaches the limiting fracture toughness of the material. The crack propagation model was first validated on different test specimen geometries. Correlating these first simulated test results with experimental data the crack initiation phase can be estimated. The simulation of real components, such as bearings, will be developed subsequently, using calculated stress distributions provided by other partners in the project as input parameters (sub-structure approach). Fatigue tests are also done on pre-corroded specimens since bearing failure may also be promoted by an unfavorable combination of overloads and corrosion damage.

The Multiclass Fault Diagnosis of Wind Turbine Bearing Based on Multisource Signal Fusion and Deep Learning Generative Model

Low fault diagnosis accuracy in case of the insufficient and imbalanced samples is a major problem in the wind turbine fault diagnosis. The imbalance of samples refers to the large difference in the number of samples of different categories, or the lack of a certain fault sample, which requires good learning of the characteristics of a small number of samples. Sample generation in the deep learning generation model can effectively solve this problem. In this study, we proposed a novel multi-class wind turbine bearing fault diagnosis strategy based on the conditional variational generative adversarial network (CVAE-GAN) model combining multi-source signals fusion. This strategy converts multi-source one-dimensional vibration signals into two-dimensional signals, and the multi-source two-dimensional signals were fused by using wavelet transform. The CVAE-GAN model was developed by merging the variational auto-encoder (VAE) with the generative adversarial network (GAN). The VAE encoder was introduced as the front end of the GAN generator. The sample label was introduced as the model input to improve the model’s training efficiency. Finally, the sample set was used to train encoder, generator and discriminator in the CVAE-GAN model to supplement the number of the fault samples. In the classifier, the sample set is used to do experimental analysis under various sample circumstances. The results show that the proposed strategy can increase wind turbine bearing fault diagnostic accuracy in complex scenarios.

State degradation evaluation and early fault identification of wind turbine bearings

To solve the shortage of state degradation characteristic and evaluation parameters in the monitoring of the running state of wind turbine bearings, and the low accuracy of early fault identification, a state degradation characteristic index, which combines multi-scale weighted permutation entropy (MWPE) and local linear embedding algorithm (LLE), is proposed for performance degradation monitoring and early fault identification of wind turbine bearings. The degradation index is quantitatively evaluated by four evaluation parameters: the initial time of state degradation, the sensitivity of initial degradation, the failure mutability and the consistency of degradation trend. Through the full cycle acceleration life monitoring data of the bearings, the result show that the proposed MWPE LLE degradation index can effectively characterize the whole process of bearing life cycle from stable operation to degradation to failure. Compared with the quantitative evaluation result of the kernel principal component analysis (KPCA), weighted permutation entropy (WPE), the root mean square (RMS) and kurtosis index, the comprehensive expression ability of MWPE-LLE degradation index is better. It can not only accurately capture the mutation time of state degradation, but also detect the initial degradation time in advance of the four kinds of characteristic indexes, and accurately judge the failure type of the early failure of the test bearing, and the judgment result is consistent with the inspection result after the experiment.

Fault diagnosis of wind turbine bearing using a multi-scale convolutional neural network with bidirectional long short term memory and weighted majority voting for multi-sensors

In order to solve the problems of insufficient extrapolation of intelligent models for the fault diagnosis of bearings in real wind turbines, this study has developed a multi-scale convolutional neural network with bidirectional long short term memory (MSCNN-BiLSTM) model for improving the generalization abilities under complex working and testing environments. A weighted majority voting rule has been proposed to fuse the information from multi-sensors for improving the extrapolation of multisensory diagnosis. The superiority of the MSCNN-BiLSTM model is examined through experimental data. The results indicate that the MSCNN-BiLSTM model has 97.12% mean F1 score, which is higher than existing advanced methods. Real wind turbine dataset and an experimental dataset are used to demonstrate the effectiveness of the weighted majority voting rule for multisensory diagnosis. The results present that the diagnosis result of the MSCNN-BiLSTM model with weighted majority voting rule is higher respectively 1.32% and 5.7% than the model with traditional majority voting or fusion of multisensory information in feature-level.

Optimal Load Frequency Control of Island Microgrids via a PID Controller in the Presence of Wind Turbine and PV

This article studied the load frequency control (LFC) of a multi-source microgrid with the presence of renewable energy sources. To maintain a sustainable power supply, the frequency of the system must be kept constant. A Proportional–Integral–Derivative (PID) controller is presented as a secondary controller to control the frequency of the microgrid in island mode, and the integral of squared time multiplied by error squared (ISTES) is used as a performance index. The use of the Craziness-Based Particle Swarm Optimization (CRPSO), which is an improved version of Particle Swarm Optimization (PSO), improves the convergence speed in optimizing the nonlinear problem of load and frequency controller design. The test microgrid is composed of the load and distributed generation units such as diesel generators, photovoltaics and wind turbines. The proposed controller provided the desired response to adjusting the microgrid frequency, achieving the final response after a short time and making it more stable and less oscillatory compared with the conventional system.

A novel tacholess order analysis method for bearings operating under time-varying speed conditions

Order tracking (OT) of vibration envelope signal based on instantaneous frequency (IF) estimated from a time-frequency representation (TFR) is generally applied to detect bearing defects under time-varying speed conditions. However, these TFR based OT approaches require a high resolution for accurate estimation of the IF. To overcome these issues, a tacholess OT technique based on the fast dynamic time warping (FDTW) algorithm is applied to detect bearing defects for the first time in this paper. FDTW based signal transformation is initially applied to align a filtered shaft signal to a reference signal of constant frequency, followed by reconstructing the bearing envelope signal. The resulting envelope signal is able to provide a richer fault indication than the original signal. The efficacy of the developed OT method is established using a simulation analysis followed by experimental validation using measurements obtained from a laboratory test-rig and a wind turbine bearing.

Multi-stage optimal scheduling of multi-microgrids using deep-learning artificial neural network and cooperative game approach

This article proposes a two-stage system for the daily energy management of micro-grids (MGs) in the presence of wind turbines, photovoltaic (PV) panels, and electrical energy storage systems (ESSs). Each MG uses historical data to predict its consumers' load demand, wind speed, and solar irradiance in the first stage. In the second stage, the cooperative game method is used to determine the MG's daily dispatch and energy transaction. The paper develops a prediction model using artificial neural network (ANN) and rough neuron water cycle (RNWC) algorithms, called deep learning artificial neural network (DLANN), which is a combination of technology from the artificial neural network and WC algorithm in order to predict uncertain parameters. The above model is implemented in the 33bus power distribution system; the simulation results show that the DLANN method provides more accurate predictions than the ANN method. The results also show that a MG can achieve energy cost savings through an alliance of MGs using the cooperative game approach. Furthermore, analysis of the impact of the ESS on the operation of the MG shows that the absence of the ESS will reduce the power output of the wind turbine.

Starvation and relubrication mechanisms in grease lubricated oscillating bearings

Oscillating rolling element bearings can be found in various industrial applications. A popular example are the rotor blade bearings of wind turbines, which allow the rotor blades to turn around their longitudinal axis. These bearings are predominantly grease lubricated and expected to be running in a state of low velocity starvation. Elastohydrodynamic Lubrication (EHL) films in starved contacts are susceptible to the conditions of inlet lubricant supply. If the bearings are running under moderate starvation no early failure due to wear is expected. If the inlet conditions increasingly lead to a drying out of the contact, resulting in a heavily starved contact, metal-to-metal contact can lead to false brinelling and subsequent bearing failure. Using two different greases bearing experiments are used to gain an understanding of the mechanism of wear initiation. It is found that starvation seems to be a major contribution to wear appearing in the investigated operating conditions (2°-45° osc. angle, 0,2–5 Hz osc. frequency). The degree of starvation in the contact depends on the balance between lubricant displacement by the rolling element and the replenishment of lubricant during operation. The following conclusions could be drawn: Replenishment is highly dependent on the operating conditions such as the oscillation frequency, the oscillation amplitude, and lubricant rheology. For small oscillation angles the ability of the grease to release base oil with high mobility into the contact seams to be essential. For greater oscillation angles, after crossing a limiting angle, a secondary replenishment mechanism seems to become active. This prevents early wear initiation. At critical operating parameters, which mainly include high oscillation frequencies and medium oscillation angles, severe wear is initiated after just a few hundred oscillation cycles. By modifying an existing starvation factor the influences could be visualised and compared to the experimental results.

Fault diagnosis of wind turbine bearing based on CNN-XGBoost

When a wind turbine bearing fails, because the fault signal shows the characteristics of instability and non-linearity,it is difficult to extract the fault features artificially, resulting in a lower accuracy of the wind turbine bearing fault classification. In order to realize the automatic extraction of wind turbine bearing faults and improve the accuracy of fault classification, this paper proposes a wind turbine bearing fault identification method based on the combination of convolutional neural network and limit gradient lifting algorithm. First, convert the original bearing fault signal into a grayscale image. Then, the gray image is input to the convolutional neural network to automatically extract the feature of the fault. Finally, the limit gradient boosting algorithm optimized by the grid search algorithm is used as a classifier to classify the fault. The simulation results show that the method used in this paper can achieve a high rate of correctness of wind turbine bearing fault identification, indicating that this method is effective for wind turbine bearing fault diagnosis.

Operating state prediction for wind turbine generator bearing based on ULSSVM and QPSO

Aiming at the problem of operating state prediction of generator bearing, a prediction method based on quantum particle swarm optimization (QPSO) and united least squares support vector machine (ULSSVM) is proposed. Firstly, the time least squares support vector machine (TLSSVM) model is established in accordance with the change law of characteristic parameters over time. Space least squares support vector machine (SLSSVM) model is established in accordance with the law of mutual influence between characteristic parameters. Secondly, the QPSO algorithm is used to optimize the parameters of each least squares support vector machine (LSSVM) model. When the difference between the predicted value and the measured value reaches the minimum, the optimal LSSVM parameter set is output. Then the improved Dempster-Shafer (D-S) theory is used to determine the weights of TLSSVM and SLSSVM. A united model of time LSSVM and space LSSVM is established. The characteristic parameters are predicted. The prediction results and the reference matrix are fused and reduced in dimension. Finally, the generator bearing operating status is predicted based on the location of the prediction results. The results show that the proposed method is helpful to realize the operating state prediction of the wind turbine bearing.

Dynamic security assessment for the power system in the presence of wind turbines

Dynamic security is an essential requirement for operating a modern power system. Due to the global increase in load demand, modern power systems witness several dramatic changes in terms of size and implementation of new renewable sources. At the same time, the deregulation process in power operation policy is being pushed to operate closer to its security boundary limits. Based on the combined Decision Tree (DT) algorithm, namely Random Forest (RF) and advance attribute selection technique, this paper presents an approach to address these challenges related to dynamic security assessment (DSA) in the modern power system. The performance of study approach is demonstrated on a modified version of IEEE 9 and 14-bus test system models with presence of two wind turbines (WTs) type WTG 3. Results show the superiority of RF compared to other DT algorithms that are used in this study. In addition, the attribute selection technique could significantly affect the number of attributes required for DSA. This makes DT classifier more effectiveness in the online application. Thus, this approach can provide control center with vital information with high accuracy results and less attributes about security state direction that will help operator to take the right and fast steps to remedy problems and prevent a blackout from occurring.

Multi-Scale Wind Turbine Bearings Supervision Techniques Using Industrial SCADA and Vibration Data

Timely damage diagnosis of wind turbine rolling elements is a keystone for improving availability and eventually diminishing the cost of wind energy: from this point of view, it is a priority to integrate high-level practices into the real-world operation and maintenance of wind farms. On this basis, the present study is devoted to the formulation of reliable methodologies for the supervision of wind turbine bearings, which possibly can be integrated in the industrial practice. For this reason, this study is a collaboration between a company (ENGIE Italia), the University of Perugia and the Politecnico di Torino. The analysis is based on the exploitation of the data types which are available to wind farm managers from industrial control systems: SCADA (Supervisory Control And Data Acquisition) and TCM (Turbine Condition Monitoring). Due to the intrinsic sampling time difference between SCADA and TCM data (a few minutes the former, up to the millisecond for the latter), the proposed methodology is designed as multi-scale. At first, historical SCADA data are processed and the behavior of the oil filter pressure is analyzed for all the wind turbines in the farm: this provides preliminary advice for identifying presumably healthy wind turbines from those suspected of damage. A second step for the SCADA analysis is then represented by the study of the temperature trends of the bearings through a Support Vector Regression: the incoming damage is individuated from the analysis of the mismatch between measurements and estimates provided by the normal behavior model. Finally, the healthy units are selected as the reference and the faulty as the target for the analysis of TCM vibration data in the time domain: statistical features are computed on independent chunks of the signals and, using a Novelty Index, it was possible to distinguish the damaged wind turbines with respect to the reference ones. In light of the interest in application of the proposed methodology, good practice criteria in selecting and managing the data are discussed as well.

Four-Objective Optimal Scheduling of Energy Hub Using a Novel Energy Storage, Considering Reliability and Risk Indices

Energy hub as a novel model which has provided more efficiency, flexibility, and reliability. Also, due to using different multi carriers as renewable and non-renewable sources and storage devices in energy hubs, optimal operation scheduling becomes more challenging. In this paper, the day-ahead scheduling of energy hub is studied in the presence of wind turbine, electrical, thermal, and a novel water-based storage called Pico hydel energy storage. A useful method using the Monte-Carlo and k-mines clustering method is developed to estimate the uncertainty associated with wind speed, load, and market price. On the other hand, Conditional Value at Risk and Second-Order Stochastic Dominance (SSD) method are applied as a risk-averse method. To find the best operation performance of energy hub, multiple objectives consist of economic, emission pollution, electrical, and load profile deviation are defined. As well, the demand response program for gas, electricity, water, and thermal are implemented to improve the energy hub performance. Mixed-integer non-linear program formulation for the day-ahead scheduling of energy hub has been solved by executing CPLEX solver of GAMS optimization software in five different cases. Simulation results show that the hub's operation cost and emission reduce up to 4.8% and 5.6% by implementing four objective functions in the presence of the Pico hydel energy storage and demand response programing.

A Signal Based “W” Structural Elements for Multi-scale Mathematical Morphology Analysis and Application to Fault Diagnosis of Rolling Bearings of Wind Turbines

A Signal Based “W” Structural Elements for Multi-scale Mathematical Morphology Analysis and Application to Fault Diagnosis of Rolling Bearings of Wind Turbines

A Multi-Objective Reconfiguration Scheme for Reliability and Energy Usage Enhancement of Distribution Systems in the Presence of Wind Turbines Using the MOHSA Optimization Algorithm

The stochastic nature of the active power generated by wind turbines (WTs) has posed new challenges to the reliability assessment, load flow analysis, and reconfiguration process of distribution systems penetrated with WTs. In this paper, a new optimization approach has been presented to find the best configuration of the distribution system along with optimal locations of WTs. This is aimed at maximizing the reliability of the system, while energy losses are minimized. To this end, based on the graph theory and the system topology, a simple technique is proposed for finding minimal cut sets that could be used for calculating reliability indices. Furthermore, the multiple flow directions created in the presence of WTs cause difficulties in calculating reliability indices and in performing load flow analysis. Accordingly, a new concept called the independent reliability zone is introduced to overcome these problems. In this paper, the multi-objective harmony search algorithm, due to its suitability for multi-objective problems, is employed as a solution for solving the proposed optimization problem. The effect of the proposed approach has been evaluated on both test and real systems. The results show that using the proposed approach, a more practical solution is achieved for low-cost and reliable 24-hour planning of the system in the presence of uncertainties in DGs.

Application of Projection Pursuit Analysis Method Based on Kernel Function in Fault Diagnosis for Rolling Bearing

In view of nonlinear and non-Gaussian characteristics of fault feature for rolling bearing of wind turbine, the projection pursuit analysis method based on kernel function is put forward to make pattern recognition for all bearing running states. It elaborates the principle of projection pursuit method, and plan and steps for pattern recognition, then has a simulation and test by MATLAB software. The test results show that the projection pursuit analysis method based on kernel function is an effective method for fault diagnosis, and it can quickly and efficiently identify rolling bearing running states.