Reliability analysis of wind turbine gearboxes: past, progress and future prospects

As a type of clean and renewable energy source, wind power is growing fast as more and more countries lay emphasis on it. At the end of 2011, the global wind energy capacity reached 238 GW, with a cumulative growth of more than 20% per year, which is certainly a respectable figure for any industry. There is an exigent need to reduce the costs of operating and maintaining wind turbines while they became one of the fastest growing sources of power production in the world today. Gearbox is a critical component in the transmission system of wind turbine generator. Wind turbine gearbox operates in the extreme conditions of heavy duty, low speed and non-stationary load and speed, etc., which makes it one of the components that have high failure rate. To detect the fault of gearbox, many methods have been developed, including vibration analysis, acoustic emission, oil analysis, temperature monitoring, and performance monitoring and so on. Vibration analysis is widely used in fault diagnosis process and many efforts have been made in this area. However, there are many challenging problems in detecting the failure of wind turbine gearbox. The gearbox transforms low-speed revolutions from the rotor to high-speed revolutions, for example, from 20 rpm to 1500 rpm or higher. Usually one or more planetary gear stages are adopted in a gearbox design because the load can be shared by several planet gears and the transmission ratio can get higher. One disadvantage with the planetary gear stage is that a more complex design makes the detection and specification of gearbox failure difficult. The existing fault diagnosis theory and technology for fixed-shaft gearbox cannot solve the issues in the fault diagnosis of planetary gearbox. The planetary stage of wind turbine gearbox consists of sun gear, ring gear and several planet gears. The planet gears not only rotate around their own centers but also revolve around the sun gear center, and the distance between each planet gear to the sensor varies all the time. This adds complexity to vibration signals and results in difficulty in finding the fault-related features. The paths through which the vibration propagates from its origin to the sensors are complex, and the gears of other stage vibrate at the same time. This makes fault features be buried in noises. Further, the extreme conditions of heavy duty, low speed, and non-stationary workload lead to evidently non-stationary phenomena in the collected vibration. Methods to assess fault severity of a gearbox should be developed so as to realize fault prognosis and estimate of the remaining useful life of gearbox. Finally, other issues like signal analysis based on multi-sensor data fusion are also considered. This paper gives a comprehensive investigation on the state-of-the-art development in the wind turbine gearbox condition monitoring and health evaluation. The general situation of wind energy industry is discussed, and the research progresses in each aspects of wind turbine gearbox are reviewed. The existing problems in the current research are summarized in the end.

As an renewable and clean energy of the world, wind energy has gained more and more attention and its fault diagnosis becomes more and more important. The gearbox, as the kernel component of the wind turbine system, it’s robust conditions have a great influence on the whole wind turbines system. Wind turbine gearbox has complex structure, which is usually composed of solar planetary gearbox and cylindrical gearbox. In the process of operation, various kinds of faults easily occur, resulting in serious losses. Once the wind turbine gearbox is not functioning as smoothly as it could be, it may result in large economic losses for the company and owner. At the same time, the failure rate of wind turbine gearbox has always been high because of complicated mechanic structure and special motion. Therefore, the tasks of reducing the downtime and increasing the productivity of wind turbine gearbox are urgent. This paper reviewed some research results of faults diagnosis on wind turbines gearbox, such as time-frequency analysis method, vibration based methods, nondestructive testing methods, etc. Meanwhile, this paper finds out some key problems and the channel of the resolution of the issue in order to supply some information for the further research of wind turbines gearbox.

While the global demand for energy continues to grow, the world’s depots of fossil fuels decrease. Logically, scientists have researched in different ways of creating sustainable energy. Dams, solar power and wind energy are all different ways of extracting power from nature. Having neither mountains nor many hours of annual sunshine, Denmark is, however, a flat country with many kilometers of coast line and calm weather, and therefore is perfectly suited for wind energy. Because of these facts, Denmark has always been a pioneer within the wind industry, building the world’s first off-shore wind energy park in 1991. In recent years, wind turbines have grown dramatically in size and capacity. Scientists predict that in order to make wind energy a more suitable alternative to fossil fuels, the wind turbines have to become even larger. The major problem with these super wind turbines, however, is the weight of the nacelle, of which a heavy part is the gear box. In order to avoid having a gear box in the wind turbine, a direct driven generator has been proposed, where superconductors play an important role. Energy losses due to Joule heating will be eliminated and high temperature superconductors are capable of working in high magnetic fields (several Teslas). The alternative to superconductors are permanent magnets, but the required amount would become expensive and inaccessible, while superconductors can be made from easily accessible elements. However, using superconductors also pose considerable challenges; low temperature cooling systems are required and the superconductors need to be of high quality. High temperature superconductors is a relatively new field of study, with the first discoveries made in the 1980s. Having found materials with superconducting properties at relatively high temperatures, the issue of optimizing other properties arose. A pinning center is an irregularity in the crystal, which destroys the superconducting properties. Here the magnetic field lines can penetrate without causing further damage to the superconductor. In 2005 Misko et al. found that a gradient in density of pinning centers could increase the critical current density (Jc) in a magnetic field. Pinning centers had been known to optimize superconductors, but with this new information, scientists have been equipped with a new tool to make superconductors more suitable for wind turbines. Since Misko et al. first predicted that different pinning densities throughout the superconductor could improve superconductors; many attempts have been made in order to show this property experimentally. Different methods to create the desired pinning patterns experimentally include Molecular Beam Epitaxy and E-beam Lithography. These methods are, however, both very expensive, making them less attractive for industrial production. This project investigates different options of creating simple gradients in the pinning center density.

This paper proposes a novel intelligent fault diagnosis method to automatically identify different health conditions of wind turbine (WT) gearbox. Unlike traditional approaches, where feature extraction and classification are separately designed and performed, this paper aims to automatically learn effective fault features directly from raw vibration signals while classify the type of faults in a single framework, thus providing an end-to-end learning-based fault diagnosis system for WT gearbox without additional signal processing and diagnostic expertise. Considering the multiscale characteristics inherent in vibration signals of a gearbox, a new multiscale convolutional neural network (MSCNN) architecture is proposed to perform multiscale feature extraction and classification simultaneously. The proposed MSCNN incorporates multiscale learning into the traditional CNN architecture, which has two merits: 1) high-level fault features can be effectively learned by the hierarchical learning structure with multiple pairs of convolutional and pooling layers; and 2) multiscale learning scheme can capture complementary and rich diagnosis information at different scales. This greatly improves the feature learning ability and enables better diagnosis performance. The proposed MSCNN approach is evaluated through experiments on a WT gearbox test rig. Experimental results and comprehensive comparison analysis with respect to the traditional CNN and traditional multiscale feature extractors have demonstrated the superiority of the proposed method.

Type Synthesis of Gear-box in Wind Turbine

Data-driven condition monitoring reduces downtime of wind turbines and increases reliability. Wind turbine operation and maintenance (O&M) cost is a significant factor that calls for automated fault detection systems in wind turbines. In this manuscript, the anomaly detection problem for wind turbine gearbox is formulated based on adaptive threshold and twin support vector machine (TWSVM). In this work, SCADA data from wind farms located in the U.K. is considered with samples from twelve months before failure, and from one month before failure. Gearbox oil and bearing temperatures are used as two univariate time-series for analyzing adaptive threshold. The effectiveness of the proposed method is compared with standard classifiers like support vector machines (SVM), k-nearest neighbors (KNN), multi-layer perceptron neural network (MLPNN), and decision tree (DT). Anomaly detection of wind turbine gearbox using TWSVM and adaptive threshold results in an accurate performance, thus increasing the reliability. The missed failure and false positive rate that indicate the proposed methodology’s ability is also investigated to discriminate between false alarms, and comparison with previous studies shows superior performance.

This research provides an overview of Iraq's renewable energy prospects. One of the most important sorts of renewable energy resources in the world is wind energy. Wind energy is considered environmentally beneficial and very economical. These benefits are cited as the primary reasons for using wind turbines to generate power across the world. An assessment of the country's profile has been made, taking into consideration existing energy generation, crude oil production at current levels, gas flares that generate CO2 emissions, as well as industry, human activities, and grid electricity distribution. Iraq has power shortages, and several obstacles must be solved in order to satisfy projected increases in electrical demand. This analysis discovered that solar, wind, and biomass energy are now underutilized, but that they might play a key part in Iraq's sustainable energy future. Wind energy, for example, has become an important aspect in reducing pollution and improving air quality. The usage of tiny wind turbines to generate power under Iraqi meteorological conditions is examined in this study. The research evaluates the wind system that is necessary to provide electric power for highway services including lighting and parking. According to the study's findings, wind turbines can be used effectively for roadway illumination. The efficiency of a maximum wind turbine is determined by the blade design. For the planned use, the wind speed reported in Iraqi winter is pretty acceptable. Alternative and sustainable energy, wind energy, Iraqi climate are all index terms. The Iraqi government recently joined the Paris Climate Agreement and is now starting to encourage the participation of small and large consumers to generate electricity from renewable energy sources. This article analyzes the hybrid electrical system of solar and wind energy for the city of Dohuk, the northern part of Iraq, to find out the feasibility of this system compared to the local electrical network. First, access to solar and wind energy resources in Dohuk were ensured.

Nowadays, wind energy has become one of the most concerning renewable energy sources due to its low damage to nature and low cost. Wind turbines are usually deployed in remote areas and harsh environments, so they (especially gearboxes) are more likely to suffer faults. Therefore, it is necessary to vigorously develop wind turbine gearbox fault detection method to provide more accurate fault alarms. This paper proposes a fault detection method based on dilated convolutional neural network(DCNN). Compared to the traditional convolutional neural network, the dilated convolution can maintain a larger receiving field to speed up the monitoring speed, and is more suitable for real-time fault monitoring. Finally, an experimental wind turbine gearbox is used to verify the effectiveness of the method.

Introduction: To date, a variety of studies have been published on the topic of long term energy system transition. Most studies on future energy systems, however, have a shorter time frame or adopt a supranational focus (e.g. the Energy Roadmap, 2011 or the World Energy Outlook, 2015). It then constitutes a sincere challenge to perform a national energy system transition study with as time horizon 2050 and covering a far-reaching transformation of the energy system. VITO, together with the FPB (Federal Planning Bureau) and ICEDD, performed a study to scrutinise the transition of the Belgian national energy system towards a future mix entirely based on renewable energy sources. The focus on renewable energy sources and on building a national energy system completely running on renewable energy can be traced back to three main concerns: – Climate change: Renewable energy sources (RES) are a major instrument in the fight against climate change as RES do not release (net) greenhouse gas emissions. – Security...

Advanced control of large-scale wind turbines: Structural load reduction and lifetime control

During recent years, wind energy has moved from an emerging technology to a nearly competitive technology. This fact, coupled with an increasing global focus on environmental concern and a political desire of a certain level of diversification in the energy supply, ensures wind energy an important role in the future electricity market. For this challenge to be met in a cost-efficient way, a substantial part of new wind turbine installations is foreseen to be erected in big onshore or offshore wind farms. This fact makes the production, loading and reliability of turbines operating under such conditions of particular interest.

Health monitoring of wind turbine gearboxes has gained considerable attention as wind turbines become larger in size and move to more inaccessible locations. To improve the reliability, extend the lifetime of the turbines, and reduce the operation and maintenance cost caused by the gearbox faults, data-driven condition motoring techniques have been widely investigated, where various sensor monitoring data (such as power, temperature, and pressure, etc.) have been modeled and analyzed. However, wind turbines often work in complex and dynamic operating conditions, such as variable speeds and loads, thus the traditional static monitoring method relying on a certain fixed threshold will lead to unsatisfactory monitoring performance, typically high false alarms and missed detections. To address this issue, this paper proposes a reliable monitoring model for wind turbine gearboxes based on echo state network (ESN) modeling and the dynamic threshold scheme, with a focus on supervisory control and data acquisition (SCADA) vibration data. The aim of the proposed approach is to build the turbine normal behavior model only using normal SCADA vibration data, and then to analyze the unseen SCADA vibration data to detect potential faults based on the model residual evaluation and the dynamic threshold setting. To better capture temporal information inherent in monitored sensor data, the echo state network (ESN) is used to model the complex vibration data due to its simple and fast training ability and powerful learning capability. Additionally, a dynamic threshold monitoring scheme with a sliding window technique is designed to determine dynamic control limits to address the issue of the low detection accuracy and poor adaptability caused by the traditional static monitoring methods. The effectiveness of the proposed monitoring method is verified using the collected SCADA vibration data from a wind farm located at Inner Mongolia in China. The results demonstrated that the proposed method can achieve improved detection accuracy and reliability compared with the traditional static threshold monitoring method.

The implementation of journal bearings as planetary bearings has enabled higher torque densities of planetary gear stages in wind turbines. Since journal bearings are generally more compact than rolling element bearings they enable more planetary gears to be fitted into a planetary gear stage compared to one of similar size with rolling element bearings. Additionally, when designed and operated correctly, journal bearings operate reliable with potentially unlimited fatigue lifetime. These advantages have led to a technology shift in recent years from rolling element bearings towards journal bearings as planetary bearings in wind turbine gearboxes. At the beginning of their service life journal bearings are subjected to a running-in phase either on a test rig or directly in the field. During the running-in phase the contour and surface roughness of the bearing adapt mainly due to abrasive wear in dependence of the loads the bearing experiences. The bearings must operate within their associated system environment (e.g. gearbox) so that the bearing properties can be established ideally. For optimal running-in of the bearings special load procedures can be executed in a controlled environment. Running-in procedures often include gradually increasing loads until nominal load is achieved. For journal bearings in wind turbines this approach is not always applicable, since a deliberate running-in is a time-consuming process and therefore not necessarily part of standard end of line tests of wind turbine gearboxes. Wear simulation tools enable a derivation of ideal running-in procedures for example in terms of a reduced procedure duration and energy input. Such a simulation enables the assessment of changes in the microgeometry and surface roughness due to abrasive wear under the influence of operation under mixed friction conditions. With a methodically defined running-in process, the desired contour is creat ed quickly without exceeding the bearings fatigue limits. Thus, this work presents a method for the derivation of running-in procedures for journal bearings based on abrasive wear simulations.

A prognostic method for fault detection in wind turbine drivetrains

Wind farms use several wind turbines to generate electricity and provide a renewable source of energy. However, due to large forces as a result of hourly and seasonal variations in wind speed and direction, wind turbines experience stochastic loading that often lead to failures of wind turbine components such as the gearbox and generator. Wind turbine failures result in costly repairs and loss in revenue, while stochastic loading makes it difficult to predict the actual condition of a wind turbine. Consequently, it is challenging to schedule maintenance actions to avoid wind turbine component failures. In this paper, we present a stochastic discrete event system specification (STDEVS) model of a wind turbine component. In particular, we consider the wind turbine gearbox, which is one of the components that is often prone to failure, and model its stochastic degradation process. We implement a wind farm simulation with the STDEVS gearbox model and report on the impact of gearbox failures and maintenance scheduling on the performance of a wind farm over a 20-year period.