2013年4月电工技术学报Vol.28 No. 4 第28卷第4期TRANSACTIONS OF CHINA ELECTROTECHNICAL SOCIETY Apr. 2013
An Overview of Condition Monitoring and Fault Diagnostic for Wind Energy Conversion System
Hang Jun1 Zhang Jianzhong1 Cheng Ming1 Wang Wei1 Zhang Ming2
（1. School of Electrical Engineering Southeast University Nanjing 210096 China
2. Nanjing Electric Power Company Nangjing 210096 China）
Abstract Wind power generation is one of the most mature technologies in renewable energy and has been widely applied in the worldwide scale, where high reliability and economy have been important requirements for wind energy conversion system(WECS). Hence, the strategies to improve reliability and economy of WECS have been an interesting area for rearchers. In this paper, the current situation, fault features and maintenance difficulties of WECS are introduced, and one primary effective way to improve reliability and economy, namely condition monitoring and fault diagnosis (CMFD) approach, is applied to WECS. Moreover, the state of the art in CMFD of WECS is overviewed, focusing on the main failure components, such as generator, gearbox, blade, etc. Finally, in accordanc
e with the current problems of the CMFD technology, some possible development trends of CMFD for WECS are discusd.
Keywords: Wind turbine, condition monitoring, fault diagnosis, gearbox, generator, drive train
1 Introduction
With rious energy crisis and incread environmental concern, wind energy has become one of the fastest-growing renewable energy sources in the world, which is still required to continue becau many countries need to implement urgent targets for sustainability and pollutant emissions reduction. The US intends to generate 20% of the country’s electricity from wind power generation, i.e. over 300GW, by 2030[1]. Danish plans for 25GW of wind generation over the coming four years[2]. China aims for 15% renewable power generation by 2020[3]. Wind power is growing towards a major utility source, nowadays the reliability and power quality of WECS is emphasized for the cost-effective utilization.
Therefore wind energy conversion system(WECS) with low reliability where many faults often occur will need high costs of operation and maintenance (OM). For a wind turbine (WT) with over 20 years of operating life, the OM and part costs are estimated to be 10%～5% of the total income of a wind fxxx24
arm[4]. According to General Electric (GE) energy, a $5,000 bearing replacement can easily become into a $250,000 project involving cranes, rvice crew, gearbox replacements, and generator rewinds, not to mention the downtime loss of power generation[5].
Larger WT may reduce the OM cost per unit power, but the cost per failure is incread. The not only increa the OM cost, but also reduce the efficiency of WECS[6].
There are mainly two ways to reduce the costs of WECS.One way is to reduce manufacturing and installation cost, and another important way is to reduce the OM costs. However, the more effective and practical method is condition monitoring and fault diagnosis(CMFD) becau it achieves the cost- effective utilization compared with the other way[7].
CMFD system with integrated fault detection algorithms can allow early warnings of faults to
Supported by the national nature science foundation of China (5113 7001 and 50977011) and by the specialized rearch fund for the doctoral program of higher education of China (20090092120042). Received March 2, 2012; Received in revid form September 20, 2012.
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桃花心木读后感电 工 技 术 学 报 2013年4月
prevent major component failures. Many faults can be detected while the defective component is still in operation, necessary repair actions can be planned in time. Furthermore, condition monitoring can also obrve the operating state of WT under extreme conditions, such as icing or tower oscillation, and then take appropriate measures to prevent damage to WT. In this way, overall OM costs and downtimes of WECS can be greatly reduced, so the curity, reliability and competitiveness of WECS can be greatly improved [8-11].
Becau of the importance of CMFD for WECS, it is very necessary to know the rearch achievements and the latest progress. This paper briefly introduces the development trends of wind power generation, the main fault components of WECS and the existing CMFD methods, as well as some problems need to be rearched.
2 WT and Fault Type
The WT is compod of many subsystems, such as rotor blades and pitch system, hub, structure (tower, foundation and nacelle), drive train, gearbox, generator, electrical system, control system, nsors, mechanical brakes hydraulic system, yaw system, etc, as showed in Fig.1 and each subsys
tem is constituted
of veral components.
Fig.1 Structure of WT
The function of WT is to transform the kinetic energy in the wind into electric energy. During the working process of WT, the wind speed is constantly changing, where there is strong gusty wind, wind wheel and blades will be impacted by impulsive load.
The time-varying load will transfer to each component of the transmission chain, causing great influe
nce to its working life, and make many sorts of fault appear in the WT [12].
WT is subjected to different sorts of failures. Some of them occur more frequently than others and WT failures statistics might be studied by considering both failure frequencies and downtimes. Therefore, it is not always easy to obtain WT failures statistics. This paper refers to the latest available data of WT [12,13]. Tab. 1 shows annual frequencies of failure and downtimes between 2000 and 2004 for Swedish wind power plants. It shows the percentage breakdown of the number of failures that occurred between 2000 and 2004 for Swedish wind power plants and it is also shown that most failure components are the electric system, nsors, and blades/pitch. And the most troublesome components are the gearbox, the control system and the electric system.
Tab.1 Downtimes and failure frequencies for Swedish光纤是什么
wind power plants 2000～2004
Component
Distribution of
downtime (2000～2004)(%)
Distribution of
failure (2000～2004)(%)
胆囊结石症状Hub 0.0 0.3 Blades/Pitch 9.4 13.4 Generator 4.5 5.5 Electric system 7.2 17.5 Control system 9.2 12.9 Drive train
慈悲是什么意思
1.2
1.1
Sensors 2.7 14.1 Gears 19.4 9.8
Mechanical brake
1.2 1.2
Hydraulics 4.4
13.3 Yaw system 13.3
6.7
Structure 1.2 1.5 Entire unit
1.7
2.7
Total 100.0 100.0
There is another study concerning Danish and German wind power plants [14], which shows the same
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杭 俊等 风力发电系统状态监测和故障诊断技术综述 263
tendency as the above. Fig.2 shows the failure rate in the two power plants during the period 1994~2004, where principal components with the higher failure rate are the electrical control or system subasmblies (grid or electrical system, yaw system and mechanical or pitch control system).
According to the above statistics and analysis, the following review will be focud on the failures
occurring in WT and its subsystem.
Fig.2 Failure rates for danish and german wind power plants
3 CMFD of WT防水合同
As the wind power generation develops very quickly, which gains more and more attention recently, this paper aims to review the recent condition monitoring and fault diagnostic techniques with the focus on WT. This ction summarizes the monitoring and diagnostic methods for the major subsystems in WT which are reported in recent works. 3.1 The process of CMFD
In order to implement CMFD technology, it is important to know the general process of CMFD. Fig. 3 shows the general steps involved in a CMFD process for a WECS. The signal-measurement block measures physical quantities which reflect the obrvers/estimators. After the signals are obtained, fault features prent in the signals are extracted in the feature extraction block. The extracted information is then fed to a fault-decision algorithm that compares it with prerecorded information about the signals at a fault decision step. Finally, the faults
are detected.
Fig.3 General process of CMFD for WECS
3.2 Generator
A generator is the core component of wind power system and it is responsible for transforming rotating mechanical energy into electrical energy. In the actual application, two main kinds of generator are often ud, doubly fed induction generator(DFIG) and permanent magnet synchronous generator(PMSG).
The WT generator may easily subject to failures in bearing, stator, and rotor. It is reported that percentage failure by components in induction machines is typically: bearing related(40%), stator related(38%), rotor related(10%), and others(12%)[15].
绞股蓝的功效与作用
There are many techniques available, which are ud to obrve the condition of induction generator or synchronous generator. Some of the technologies ud for monitoring include nsors, which are ud to measure speed, current, voltage, torque, vibration, temperature, flux density and so on. The nsors are together coupled with algorithms and architectures, which are applied to monitor of the machine conditions [16]. The most popular methods for condition monitoring of induction generator or synchronous generator utilize the spectral components of the stator or rotor quantities. The stator, rotor or rotor modulating signals include voltage and current. They are ud to detect turn faults, broken rotor bars, bearing failures and air gap eccentricities for DFIG
and PMSG [15,17-22].
For a WT, one of main issues is variable-speed operation as put forward in [23]. Therefore, many condition monitoring techniques, which are being applied to
264 电工技术学报 2013年4月
generators, bad on steady-state analysis are not suitable for WT generators under such situation. So it prompts the u of non-stationary techniques for fault detection, such as wavelet, state obrver and so on.
For turn faults, a transient technique that is a combination of extended Parks vector, wavelet analysis and statistics or adaptive algorithm, is applied to the detection of turn faults occurring in a DFIG through the analysis of current signatures. This technique is not affected by variation of DFIG speed[23,24]. In Ref.[25], a CMFD scheme bad on a state obrver is developed to detect stator inter-turn fault of a DFIG, which can detect the time of faults and gives an accurate diagnosis of the fault position and level. And the applied exponential adaptive obrver with a time varying gain is also able to provide a good estimation of level under a wide range of speed variation. In Ref.[26], a real-time inter-turn fault diagnostic system bad on the so-called floating-space-vector method is int
roduced and ud for a variable speed PMSG, the calculated results show the robustness, accuracy, and proper speed respon of the technique.
Short circuit of coils is a critical electrical fault. In order to rule out interference of speed change, spectral methods are ud to detect shorted coil for PMSG Ref.[27], where a CMFD signal(S det) is derived by taking the energy in band around the drive train’s torsional natural frequency and normalizing for the variable energy input from the wind. The authors in Ref.[28] propo two methods applied to diagnosis of short coil. The first one is that the criterion, bad on the torque-speed characteristic of the synchronous machine, is propod to detect the shorted coil. The cond approach is that discrete wavelet transform (DWT) is ud to reduce the noi of the stator winding current signal, power signal and continuous wavelet transform(CWT) is ud to extract fault features correctly from the highly variable wind turbine signals, finally the experiments are carried out to verify the effectiveness of the propod methods.
3.3 Gearbox
Gearbox is one of the most important units in the drive train of WT and is compod of shaft, cabinet, gear and so on. It transforms low-speed revolutions from the rotor to high-speed revolutions.
Becau gearbox operates under the harsh environment, speed variation and load variation, it is very prone to get faults in long term, mainly including gear fault and bearing fault. The common gear faults are broken teeth, tooth surface fatigue, plywood, etc; bearing faults are wear, crack, flake surface etc. Tab.2 shows distribution of fault type of gearbox for Swedish wind power plants according to the statistical data in Ref.[12].
Tab.2 The fault type of gearbox
Component
Number
of
failure
[n]
Average
downtime[h]
Number of
failure,cau:
wear[n]
Average
downtime,
cau:wear
茶余[h] Bearings 41 562 36 601 Gearwheels 3 272 2 379 Shaft 0 0 0 0 Scaling 8 52 4 30 Oil system 13 26 5 36 Other 44 230 19 299
There are many rearchers concern on detection
of WT gearbox fault in recent years, where vibration measurement and spectrum analysis have been a prominent prevalent technique. In Ref.[29], a vibration condition monitoring system about gearbox
is put forward, and fast fourier transform(FFT)and power spectrum are ud for diagnosis for fixed-s
peed operation. The document[30] prents a study on vibration spectrum analysis bad gearbox fault classification using wavelet neural network. For variable-speed wind turbine operation, a neural network bad diagnostic framework for gearbox is developed bad on the wavelet analysis of vibration signals in Ref.[31].
For CMFD of gearbox, the signals of WT generator electrical terminals have been investigated.
In Ref.[32] the authors dealt with the demodulation of
the current signal of an induction machine driving a multistage gearbox for its fault detection. Amplitude demodulation and frequency demodulation are applied
to the current for detecting the rotating shaft frequencies and gear mesh frequencies(GMFs), respectively. DWT is applied to the demodulated current signal for denoising and removing the intervening neighboring features. Spectrum of a particular level, which compris the GMFs, is ud
第28卷第4期杭俊等风力发电系统状态监测和故障诊断技术综述 265
for gear fault detect. In Ref.[28], diagnosis of gear eccentricity is studied using current and power signals, where wavelet transforms is applied to deal with the variable-speed operation, the DWT is e
mployed to deal with the noi-rich signals from WT measurements and CWT is ud to extract time-frequency fault features. The techniques are very uful to monitor gearbox as it involves a non-stationary technique.
In Ref.[33], the stator current is analyzed via wavelet packet decomposition to detect bearing defects. The propod method can accommodate the rotational speed dependence of the bearing defect frequencies. The wavelet packet decomposition also provides a good treatment of non-stationary stator current. In Ref.[34], amplitude demodulation of three pha stator currents is adopted, such as Hilbert transform(HT), Concordia transform(CT), to detect bearing fault. The results show that HT method is well suite for non-stationary performance. A free parameter approach for bearing CMFD is discusd in Ref.[35], which extracts the amplitude and frequency modulations of faulty vibration signals. The amplitude demodulation is inherent, and the fault frequency can be detected from the spectrums of the vibration signals.
3.4 Blade
WT blade is a vital component. Due to external conditions, internal stress and fatigue, the crack and damage may gradually take place as time goes by, thus resulting in deterioration of WT, which lead t
o loss in energy capture efficiency. In other words, it is important to monitor the WT blades so that operation can be better ensured.
There are a few works about the problem. In Ref.[36], four different algorithms, transmittance function, resonant comparison, operational detection shape and wave propagation, are ud for detecting damage on WT blades. The methods are all bad on measuring the vibration respon of the blade when it is excited using piezoceramic actuator patches bonded to the blade, or a scanning lar doppler vibrometer. In Ref.[37], the wind wheel unbalance fault due to mass unbalance of the blades is studied on the test WT. Power output and vibration signals obtained are procesd by spectrum and time-frequency analysis, and the faults features are obtained to detect unbalance fault of the blade.
Blade fault diagnostics have been studied bad on strain measurement techniques such as fiber-optic Bragg grating (FBG) and Acoustic emission (AE)[36,38-41]. For the blades of small WT, a piezoelectric impact nsor is ud[42], as well as AE nsor for fault detection is ud[43].
In the practical application, a condition-monitoring package onto existing WT without requiring additional nsors is preferred. In Ref.[44], unbalance and defects in the blades of a small WT are d
etected by measuring the power spectrum density at the generator terminals. In this ca, bicoherence, a normalized bispectrum, is ud. It is able to monitor small physical changes in the machine. The technique overcomes problems of the bispectrum which is not convenient for detection purpos[18]. In Ref. [39], a continuous wavelet transform-bad approach is ud to detect blade damage; its advantage is that it requires no additional nsors. However, WT blades experience some faults and damages that could not be monitored by using the WT generator terminals, and they are particularly expod to lightning strikes. So, a lightning protection system is equipped[45]. However, lightning is a stochastic phenomenon in nature, a complete protection against its damage is not achievable. In Ref.[46], a method for lightning impact localization and classification using a fiber optic current nsor network that helps to detect damages caud by lightning and to monitor the blades is prented.
3.5 Drive train
Drive train basically consists of a shaft and bearings and occasionally a clutch between the gearbox and the generator. The shaft goes into the nacelle from the hub, where the blades are connected, and connects to the gearbox. The shaft rotates with low speed and needs to be geared up, by the gearbox. In reality, the probability of this failure is quiet small, as shown in Tab. 2. There are radial or
axial vibrations of shaft excited by other
