Review of failures and condition monitoring in wind turbine generators (2010)

XIX International Conference on Electrical Machines - ICEM 2010, Rome

Review of Failures and Condition Monitoring in

Wind Turbine Generators

Z. Daneshi-Far, G. A. Capolino, H. Henao

Abstract – Increasing wind power generation quantity in power systems needs obviously reliable operation. Therefore, accurate condition monitoring and fault diagnosis are almost mandatory. This paper aims to report recent works on condition monitoring and fault diagnosis for wind turbine generators. Wind turbines are subjected to different sort of electromechanical systems which extract kinetic power of the wind and convert it into electrical power. By increasing the amount of installed wind turbines, it becomes mandatory to force wind power generation to be a reliable source of energy. Of course, obtaining a reliable power from wind Φ

failures, thus before stating condition monitoring and fault diagnosis methods it is necessary to identify what kind of failures can be found in the real world. As a result, the gearbox is one of the most critical components of the wind turbine in which failures could stop generation for a long time. Recently, several condition monitoring and fault diagnosis techniques have been introduced in order to minimize downtime and maintenance cost while increasing energy availability and life time service of the wind farms. Vibration sensors have been used for long time in wind turbine condition monitoring systems to collect data of the generator health. Actually, using such sensors is expensive, difficult to install and sometimes impossible to be implemented in already installed wind turbines. Therefore, researchers have been focused on using electrical signature analysis from sensors in order to detect wind turbines faults and generator health in a cheap, easy to install and accessible way.

Index Terms - Wind turbine, Condition monitoring, Fault diagnosis, Predictive maintenance, Failure in wind turbines,

Vibration, Gearbox, Shaft, Bearings, Spectral methods,

Wavelet analysis.

W

I. INTRODUCTION

ind power generation is highly increasing in all the

world and especially in France. Europe aims for 20%

renewable power generation by 2020 in order to

follow the Kyoto protocol. France planed to increase

renewable power generation to 23% by 2020 from which

wind generation targets would be 19GW off-shore wind

turbines and 6GW on-shore which means increasing about

six times of installed wind energy capacity on 2009 [1]. At

the beginning of 2009, wind power generation in France has

been 3,404MW which cover 3.9% of electric power

consumption of the country. At the end of 2009, it has been

increased to 4,492MW which means 32% wind power

generation increase in the year 2009 while wind power

generation ratio in world is 31% for 2009 [2].

Wind turbines are subject to different sort of failures.

Therefore, before stating condition monitoring and fault

diagnosis methods in wind turbines, the different kinds of

failures as well as their downtime consequences have to be

reviewed [3]-[7]. Wind turbines are complex

This work has been supported by the research funds of the Regional Council of Picardie, Amiens, France. Z. Daneshi-Far, G. A. Capolino and H. Henao are with the Laboratory

of Innovative Technologies – Power Group, University of Picardie, 80000 Amiens, France (e-mails: Zahra.Daneshifar@,

Gerard.Capolino@, Humberto.Henao@ ).

978-1-4244-4175-4/10/$25.00 ©2010 IEEE

turbine generators which are subject to many types of failures, leads to the need of high performances condition monitoring systems.

Wind turbine condition monitoring systems allow collecting data from the main components of a wind turbine such as the generator, the gearbox, the main bearing, the shaft and the yaw system. The purpose is to minimize downtime and maintenance cost while increasing the energy availability and the life time service of wind turbine components. An ideal condition monitoring system is supposed to monitor all the components by using a minimum number of sensors.

The selection of suitable monitoring parameters in wind turbines is a matter of fact in which several works have been performed [8]. The main existing monitoring systems are

using vibrations as indexes to detect fault in the global system [9], [10]. In this way, several vibration sensors should be installed on the generator, the gearbox, and the

bearings. Therefore, the vibration-based monitoring system is costly and it is difficult to be implemented considering that it is designed with numerous vibration sensors [11]-[13]. Consequently, some recent publications have recommended

the usage of the electrical machine parameters in order to

recognize both early electrical and mechanical faults in the

wind turbine while it is generating and without stopping it

[14].

Recently, lot of works has been done in order to use

electrical parameters for monitoring the electrical machine

health [15]-[18]. In wind turbines, because of the noisy

environment due to the presence of power electronics,

signal to noise ratio of measured signals is low and the usage

of proposed techniques is often more problematic than in lab

environment [8], [19]. Several contributions have been

proposed to overcome this drawback by using proper signal

processing techniques [20]-[26]. Some of them focus only

on the electrical machine fault diagnosis [20]-[22] while

other focus on both bearings and gearboxes [23]. Other

contributions are dealing with detection of electrical and

mechanical faults from the generator electrical

measurements [24]-[26].

Several reviews on wind turbine condition monitoring have been already published [27]-[29]. Two of them [27], [28] which are almost the same and the other review [29] which is a valuable work, but the point is that they are not covering recent contributions while the other which has been recently published [30] covers condition monitoring of each

wind turbine components. In this paper, more references have been added with special focus on electrical

measurement for condition monitoring and signal analysis electrical power and the frequency converter in case of methods for wind turbine condition monitoring and fault variable speed wind turbines (Fig. 2) [34].

diagnosis.

II. WIND TURBINE STRUCTURE

Wind turbines are complex electromechanical systems

which extract kinetic power of air and convert it into Hub

Main

bearing

Main shaft

Gearbox

Generator

Coupling

electrical power. This energy conversion could be divided in two main parts. Firstly, the rotor extracts the kinetic power of the wind and it converts it into mechanical power. Then, the generator converts the mechanical power into electrical power (Fig. 1).

Fig. 1. Energy conversion flow on wind turbines.

The rotor blades are designed to extract a maximum power from the wind, but it is not possible to convert all the air kinetic energy into electrical energy since the air would stand behind the turbine and it reduces the resulting wind speed. Therefore, the mechanical power of the wind turbine could be obtained from total power of the wind by the power efficiency coefficient Cp which takes into account losses of the gear and the generator [31], [32]:

PM=CPPW

(1)

PW=

1

πR23

2

VρAIR

(2)

where PM is the mechanical power of the wind, CP is the

power efficiency coefficient, PW is the wind power, R is the rotor radius, V is the wind speed and ρAIR is the air density. According to the operation and the control strategy of wind turbines, a great variety of possible configurations are proposed [33]. They can be categorized as:

Fixed speed wind turbines which operate in a narrow range of rotational speed and variable speed wind turbines for which the rotational speed is optimized according to incoming wind speed in order to achieve maximum aerodynamic efficiency over a wide range of wind speed.

Fixed pitch where blade angles are fixed and variable pitch wind turbines.

Wind turbines equipped either with squirrel-cage induction generators or wound rotor induction generators (DFIG) with frequency converter on the rotor side (indirect drives) and multi pole synchronous generators with fully frequency converter and without gearbox (direct drives).

Directly connected to the power network and connected via frequency converter.

A wind turbine is composed of the rotor which extracts kinetic power of air to produce mechanical torque, the main bearing, the gearbox which converts high torque-low speed rotational power to high speed-low torque rotational power, the generator which converts the mechanical torque into

Nacelle

Fig. 2. Wind turbine components [33].

III. WIND TURBINE FAILURES

Wind turbines are subjected to different sorts of failures. Some of them are more frequent than others but in order to compare them it is necessary to consider the downtime they could force for the whole system. Therefore, wind turbine

failures statistics should be studied by considering both failure frequencies and downtimes. Unfortunately, the access to wind turbine failures statistics is not always permitted by the manufacturer and it is completely understandable. Thus, this paper refers to the latest available data [4] for wind turbines failures on 2004 (Fig. 3). Fig. 3. Distributions of number of failures compare to down time per component for Swedish wind power plants between 2000-2004 [4].

As shown, the distribution of failures and downtimes for each component of wind turbines are compared and it is clear that most of the failures are linked to the electrical system, the different sensors, the blade pitch and the control system respectively. In case of downtimes per component, the gearbox and the control system have the highest rate compared to the other components.

Some recent works report that the gearbox with average downtime of 256 hours per failure and 6,057 hours downtime per year has the highest downtime and it is well known that it is the most critical component of the wind turbine [3], [4]. A further analysis shows that the control system has almost the same situation as the gearbox in term of downtime per failure (184.6 hours) and average downtime per year (5,724 hours).

The number of failures per operational year is another

noticeable issue in wind turbine failures statistics. It has been shown that the number of failures in the first operational year is much lower than in the second one [3], [4]. Then, the rate of failures is approximately constant for eight years and it drop at 11th year. Then, in the 12th year there is an important peak and after it decreases to go upward till the 19th year.

As the gearbox is the most critical part in indirect drive wind turbines, it might be supposed that direct drive wind turbines have fewer failures than the others. In fact, a study on different types of wind turbines shows that direct drives wind turbines do not have less failures than indirect drives [5]. An investigation related to the generators and obvious. Therefore, accurate condition monitoring and fault diagnosis are almost mandatory. Condition monitoring systems select and survey measurable parameters from any wind turbine which will change as the health or the condition of machine operation changes [36]. When a change is detected, detailed analysis of the measurement will be provided and the diagnosis of the problem will be performed. Wind turbine condition monitoring systems allow collecting data from the main components of a wind turbine such as the generator, the gearbox, the main bearing, the shaft and the yaw system. The purpose is to minimize downtime and maintenance costs while increasing the energy availability and the life time service of wind turbine converters reliabilities in wind turbines has been done in [5] and it establishes that (Fig. 4):

Power converter failures in direct drive wind turbines are more important than in indirect drive but it is far smaller than of the gearbox.

The failure rate of the electric system is notable in direct drives and considering all electrics failure together, their failure rate is significantly more important than the gearbox failure rate in indirect drive. Generator failures rate in direct drives are twice of in indirect drives.

Fig. 4. Comparison of the failure rates in different wind turbine concepts

[5].

Therefore, total failure rate in direct drive wind turbines is not less than in indirect drives. A complete comparison of direct drive and indirect drive wind turbines have been done in [35] based on their cost and annual energy efficiency. The condition monitoring requirement in wind turbines have been analyzed in several works. In a recent analysis [6], the life cycle cost (LCC) analysis with wind turbine condition monitoring existence has been presented. This LCC analysis has been done for several strategies showing that condition monitoring systems are completely profitable. Moreover, it has been indicated that the gearbox is most critical component of the wind turbine and when a failure occur in a gearbox it would stop the power generation for 256 hours in average. So, in order to increase a gearbox lifecycle, early failure detection is important [7]. Some of the gearbox failures lead to long time repair if it is not predicted by condition monitoring.

IV. CONDITION MONITORING

By increasing the wind power generation quantity in power systems, the need of a reliable operation becomes components.

Wind turbines are complex electromechanical systems and their maintenance is usually costly and it depends on the maintenance method. Some maintenance methods as corrective maintenance could take more time than others. Generally, the maintenance is defined either as predictive or corrective. The predictive maintenance is performed before failure while the corrective maintenance is performed after it [4], [6]. The predictive maintenance methods are classified in two different types: condition based maintenance and scheduled based maintenance. Each type of maintenance method has its advantages and its drawbacks as shown in table I [4].

TABLE I

COMPARISON OF MAINTENANCE METHODS [4]

High risk in consequential Low maintenance

damages resulting in costs during

extensive downtimes. Corrective operation. No maintenance scheduling is maintenance Maximum lifetime possible. use of Spare parts logistics is

components. complicated. Long delivery periods for

parts are likely.

Preventive

scheduled

Scheduled.

Higher maintenance costs. maintenance

maintenance. Components will not be used

Easy spare part for maximum lifetime. logistics.

Full lifetime use of the remaining lifetime of the components. components is required.

Preventive Low expected High effort for condition condition- downtime. monitoring hardware and based maintenance Scheduled software is required. maintenance. Cost of another layer in the Easy spare part

system. logistics. Identification of appropriate condition threshold values is

difficult.

Therefore, to provide reliable energy and more effective operation in wind turbines, the condition-based predictive maintenance is mandatory. Because of the variable load in wind turbines condition monitoring, signals are highly non-stationary. Several condition monitoring methods in power production [15]-[18] are available but applying them to wind turbine could be problematic because they are not adapted to non-stationary conditions. Actually, many proposed wind

turbine condition monitoring techniques need signals from numerous mechanical sensors which are costly and difficult

to implement in already installed wind turbines. Some other signal analysis methods used in induction To achieve an accurate and reliable condition monitoring machine fault diagnosis [16] such as zoom-FFT algorithm system for wind turbines, it is necessary to select measurable (ZFFT), maximum covariance method for frequency parameters as well as to choose a suitable signal processing tracking (MCFT) are discussed and they can be adapted for method. Each wind turbine manufacturer use different wind turbine diagnosis application [17]. parameters for its condition monitoring system. Some

B. Wavelet analysis

examples are given for condition monitoring parameters of

The wavelet analysis is a function that divides a signal different manufacturer in [29], [37]. Among all these

alternatives, electrical sensors installed around the generator into different scale components and that assigns a frequency are highly recommended in recent works [20]-[26] as they to each component. The continuous wavelet transform of the are non-invasive and easy to implement compared to the output power is proposed to monitor bearing failures in the

gearbox and the generator [23]. This technique is also used mechanical ones.

As a wind turbine is an electromechanical system, to detect the rotor mass unbalance in a synchronous electrical failures have almost the same importance as generator and rotor electrical unbalance in an induction mechanical failures. In [38] fault detection of power converter for a variable speed wind turbine has been

proposed. Conventional condition monitoring systems of wind turbines use acoustic and vibration analysis of mechanical parts to detect main bearing [12], blade [13] and drive-train failures [19]. Nevertheless, a potential approach for detecting drive-train mechanical faults using generator electrical signals has been introduced [14], [39]. Since the generator provides electromechanical coupling, the monitoring of its output power could lead to electrical and mechanical fault detection [23]-[26]. The use of the

generator output power for condition monitoring compared to conventional vibration, temperature and oil lubrication monitoring has the following advantages [25]: Reduced number of sensors. Electrical measurements are cheaper than mechanical measurements. Generator power output is already available and can be easily accessed. Both mechanical and electrical signatures are contained in generator output power. The effectiveness of diagnosis based on the measurement of generator currents in double-fed induction generators has been also validated to characterize both stator and rotor electrical faults [21], [22], [40], [41]. Because of the non-stationary nature of signals, the selection of a proper signal

processing method is important to have an accurate

condition monitoring system. Inaccurate signal analysis

leads to a condition monitoring system to several with false alarms which makes the fault detection unreliable. Several signal processing have been proposed for fault detection from various measurements and the most important are mentioned in this section. A. Spectral methods

One of the most known methods in condition monitoring and fault diagnosis is the fast Fourier transform (FFT) which is used in fixed speed wind turbines [17]. In fact, it is not useful in variable speed wind turbine diagnosis because of non-stationary nature of signals. Spectrogram which shows the spectral density of a signal varying with time can be computed from time signal using the short time Fourier transform (STFT) also known as windowed Fourier transform. This type of signal processing has been proposed to analyze short-circuits in stator coils

and rotor unbalance in wind turbine under strong load transient conditions [8].

generator [24].

The conventional continuous wavelet transform involves more intensive computation than the discrete wavelet transform. However, in order to preserve its superior status, a wavelet-based adaptive filter is designed to track the energy in the output power in prescribed fault related frequencies rather than at all frequencies. By using this last technique, the output power and the rotational speed are proposed to detect mechanical and electrical perturbations in wind turbines [25].

Other works based on wavelet transforms are presented to detect both electrical and mechanical faults in wind turbine systems [14]. C. Empirical Mode Decomposition

The empirical mode decomposition (EMD) is a practical method used to decompose a non-stationary and non-linear signal into a finite number of intrinsic modes without any previous knowledge about the signal. It is shown that this technique is potentially an interesting tool to detect modes associated to twice the slip frequency in the output power of induction generator. These frequencies

would appear in a drive train mechanical or electrical fault [26].

V. CONDITION MONITORING SYSTEMS

In wind turbine systems, high power output requires high

levels of torque and consequently high gear-mesh forces.

Because of the low speed of the turbine, the various gearbox

components are usually supported by rolling element bearings. These bearings are subject to significant radial loads and need to be carefully monitored to detect any degradation. Recently, some condition monitoring systems (CMS) have been developed for wind turbines. With these

CMS, wind turbines are instrumented with many accelerometers externally mounted around the generator, the

gearbox and the most critical rolling element bearings (Fig. 5) [42]-[44]. The gearbox has generally a planetary first stage and one or two additional parallel shaft stages and it produces many gear mesh frequencies and their harmonics which are modulated between them. Each bearing produces other characteristic frequencies related to outer race ball pass, inner race ball pass, cage and element spin. All these frequency components are modified by mechanical faults affecting each of them.

Generally, condition monitoring techniques are defined around of vibration analysis, oil analysis, thermography,

strain measurements, acoustic monitoring, electrical effects, process parameters, visual inspection, performance

monitoring and self diagnostic sensors [29], [35], [45]. The current monitoring systems are usually applied to gearbox, generator and main bearing which cause more down time to the whole system. that by using voltage, current and stray flux sensors at the generator stage itself will allow to reduce the number of mechanical sensors in order to detect both mechanical and electrical faults in wind turbine systems.

Fig. 5. Vibration sensors placement in wind turbine [42].

One of the most serious problems in the wind turbines is the mechanical failures which could be caused by improper lubrication especially for gears and bearings [7]. Therefore, oil monitoring from both oil existence and quality point of view is another important measurement in wind turbines. The strain monitoring is usually done in the laboratory for blade life time testing. But using new fiber optic sensing technologies makes it possible for measurement of dynamic strain, impact events and vibration of the blade during the operation [42].

To receive data from installed sensors, there is usually a main unit installed in the turbine which selects data from sensors installed on the different components such as the gearbox, the generator, the main bearing and the yaw system (Fig. 6). Then, the unit sends the collected data via different communication methods to the main server where collected data would be stored and analyzed by using convenient signal processing techniques [9], [10].

Fig. 6. Measurement points in a 2.3MW nacelle [9]: main bearing (left) –

gearbox and generator (right).

VI. CONCLUSION

By increasing the number of wind parks connected to power systems, the need of their reliable operations requests accurate condition monitoring and fault diagnosis methods. With the existing technology, the condition monitoring of a whole wind turbine needs signals from several sensors which are costly and difficult to install in already mounted wind turbines. On the other hand, sensors failures can also reduce the wind turbine reliability.

The main problem with existing condition monitoring systems is that numerous sensors are installed in the turbine which makes the monitoring system complex and expensive. It should be considered that sensors failures are about 14% of the total wind turbine failures. In the future, it is expected

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VIII. BIOGRAPHIES

Zahra Daneshi-Far was born in Tehran, Iran. She received M.Sc. in Electrical Engineering from Khajeh Nasir Toosi University of Technology (KNTU), Tehran, Iran, in 2007. Now she is working toward her Ph.D. in Electrical Engineering in the Laboratory of Innovative Technologies, University of Picardie, Amiens, France.

Her main research interests include modeling, condition monitoring, diagnostic and predictive maintenance in wind turbines, power systems and power electrical drives.

Gérard-André Capolino was born in Marseille, France. He received the BSc in Electrical Engineering from Ecole Centrale de Marseille, Marseille, France in 1974, the MSc from Ecole Supérieure d'Electricité, Paris, France in 1975, the PhD from University Aix-Marseille I, Marseille, France in 1978 and the D.Sc. from Institut Polytechnique de Grenoble, Grenoble, France in 1987. He held tenure positions in University of Yaoundé I, Yaounde, Cameroon, University of Burgundy, Dijon, France and Mediterranean Institute of Technology in Marseille, France. In 1994, he joined the University of Picardie, Amiens, France as a Full Professor and he is now Director of the European Master in Advanced Power Electrical Engineering (MAPEE) recognized by the European Commission in 2004. His research interests have been focussed on modelling and control of induction machines for at least 15 years. For the last 20 years, he has been involved in condition monitoring and fault detection of AC electrical machinery for which he has developed many innovative techniques. He has published more than 400 papers in scientific journals and conference proceedings since 1975.

Dr. Capolino has been the recipient of the 2008 IEEE-IES Dr.-Ing. Eugene Mittelmann Achievement Award and will be the recipient of the 2011 IEEE-PELS Diagnostics Achievement Award.

Humberto Henao received the M.Sc. in Electrical Engineering from Universidad Tecnologica de Pereira, Pereira, Colombia in 1983, the M.Sc. in Power Systems Planning from Universidad de los Andes, Bogota, Colombia in 1986, the Ph.D. in Electrical Engineering from Institut Polytechnique de Grenoble, Grenoble, France in 1990. From 1987 to 1994, he was consultant for companies as Schneider Electric and ALSTOM in the Modeling and Control Systems Laboratory (UMCS), Mediterranean Institute of Technology, Marseille, France. In 1994, he joined the Ecole Supérieure d’Ingénieurs en Electrotechnique et Electronique, Amiens, France as an Associate Professor. In 1995, he joined the University of Picardie, Amiens, France and is now a Full Professor in the Department of Electrical Engineering.

Dr. Henao main research interests are modeling, simulation, condition monitoring and diagnosis/prognosis of electrical machines and electrical drives.

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