Introduction
As wind turbines have become an increasingly vital source of renewable energy, maintenance costs have emerged as a significant challenge for the industry. Mechanical faults are a major contributor to these costs, resulting in significant downtime and lost revenue. Numerous intrusive methods have been developed to diagnose these faults, requiring additional sensors and complex data acquisition systems.
Fortunately, a promising non-intrusive solution has emerged in the form of current signature analysis. By analyzing the generator's electrical signals, this technique can provide valuable insights into mechanical faults and reduce downtime. The question remains, however, whether generator electrical signals, particularly the current signal, can be effectively utilized for mechanical fault diagnostics. The answer is a resounding YES.
In this article, we will delve deeper into the topic and explore the reasons behind this assertion. We will discuss the historical background and fundamental principles of current signature analysis (CSA) and how it compares to traditional methods such as vibration signature analysis. By the end, you will have a clear understanding of the potential benefits of this innovative approach to mechanical fault diagnostics.
The History of the Current Signature Analysis
The first implementation of Current Signature Analysis for fault diagnostics can be traced back to the 1970s. This technique has been used to detect and diagnose electrical faults in rotating electric machines such as electric motors and generators. This method has been successfully used for the diagnosis of static, dynamic, and mixed eccentricity faults, turn-to-turn faults, motor bearing faults, imbalance faults, etc. More information about the current signature analysis can be found in [4-8].
The origin of current signature analysis for fault diagnostics is not attributed to a single person or investigator. Current signature analysis is a well-established electrical engineering technique that has been developed and improved over time by numerous researchers and engineers.
The basic principle of CSA is to monitor the current waveform of a rotating electrical machine during normal and faulty conditions and analyze the changes in the current waveform to determine the type of fault. Usually, fault characteristic features are extracted from the current signal in the frequency domain. In this case, if the signal is stationary, the traditional Fast Fourier Transform can be used.
The first implementation of CSA involved using analog oscilloscopes and filters to capture and analyze the current waveform. The analyzed waveform was then compared to reference waveforms to determine the type of fault.
Over the years, CSA has evolved with the advancement of technology and has become an important tool in predictive maintenance. Today, CSA is often performed using digital signal processing (DSP) techniques and specialized software, which allow for more accurate and efficient analysis of the current waveform.
The first publication on CSA can be traced back to the late 1970s. In 1978, two research papers were published in the IEEE Transactions on Industry Applications journal that described the use of CSA for fault detection in electrical machines.
The first paper, titled "Electric Current Signature Analysis as a Nonintrusive Motor Monitoring Technique," was authored by D. W. Novotny and T. A. Lipo. In this paper, the authors proposed the use of CSA for fault detection in induction motors. They demonstrated that the electrical current signals produced by a motor contain valuable information about the health and performance of the motor and that by analyzing these signals, it is possible to detect and diagnose faults such as broken rotor bars and bearing defects.
The second paper, titled "Detection of Abnormal Operation in Three-Phase Induction Motors by Stator Current Monitoring," was authored by J. T. Bialasiewicz and W. T. Thomson. In this paper, the authors described a method for detecting abnormal operation in three-phase induction motors using CSA. They showed that by analyzing the stator current signals produced by a motor, it is possible to detect and diagnose faults such as phase imbalance, rotor faults, and eccentricity.
These two seminal papers laid the foundation for the use of CSA as a non-intrusive technique for fault detection and diagnosis in electrical machines. Since then, numerous studies have been conducted on CSA, and it has become a widely used technique in the fields of electrical engineering and condition monitoring.
Fundamentals of Current Signature Analysis
The wind turbine is a highly complex type of electromechanical equipment comprising multiple components, including rotor blades, mechanical drivetrain, and induction generators. These components can be viewed as a kinematic chain, where any mechanical fault occurring in one of the components generates vibrations that propagate through the chain and eventually reach the induction generator.
By taking into account the fact that any wind turbine component experiencing mechanical faults coupled with a generator shaft produces anomalies in the generator air gap flux density, the correlation between mechanical fault vibration and induction generator stator and rotor currents can be determined. The changes in flux density can be described by considering two physical phenomena, namely [9]:
1. The presence of radial movement of the rotor center;
2. The apparition of shaft torque variations resulting in speed variation.
A mechanical fault can cause the air gap of the machine to vary, resulting in a combination of rotating eccentricities moving in both clockwise and counter-clockwise directions. Such air gap variation can directly affect the output of the generator, including power, current, and voltage.
The radial rotor displacement affects the permanence of the air gap, leading to a variation in the flux density in the air gap and, therefore, in the stator current. Additionally, the speed variation of the shaft also affects the electromagnetic field distribution. The changes in the air gap electromagnetic field will amplitude and frequency modulate induction generator electrical signals. Consequently, by using signal processing techniques in the frequency domain, as well as demodulation techniques, it is possible to extract features related to wind turbine mechanical faults from the generator current signals.
For example, a rotor blade mass imbalance fault produces additional pulsating torque on the shaft with a frequency of 1P, which can be detected through the induction generator current signal. Similarly, gearbox gear tooth faults generate oscillations on the shaft with gear mesh frequency. By identifying these specific frequencies in the electrical signals of the generators, potential mechanical faults can be detected, allowing for early maintenance and ultimately increasing the wind turbine's availability.
Successful implementation of CSA for mechanical fault diagnostics in wind turbines
Current signature analysis has been widely used for mechanical fault diagnostics in wind turbines, with numerous successful implementations reported in the literature. For example, in [1], the authors utilized current signature analysis to detect and diagnose faults in a wind turbine gearbox. The results showed that the proposed method could effectively detect and diagnose gear faults, such as gear tooth cracks, with a high degree of accuracy. In [2], the authors proposed a method based on current signature analysis to detect bearing faults in wind turbines. The method was validated through experiments and showed good performance in detecting and diagnosing bearing faults. Additionally, in [3], the authors used current signature analysis to detect faults in the blade pitch system of a wind turbine. The method was validated through experiments and demonstrated good performance in detecting and diagnosing blade pitch faults. These studies demonstrate the potential and effectiveness of current signature analysis for mechanical fault diagnostics in wind turbines.
It is noteworthy that the majority of publications are founded on theoretical or mathematical models, as well as laboratory-based experiments. However, the utilization of current signature analysis on an actual operating wind turbine is scarcely found in the literature.
Advantages of using current signature analysis
The following advantages of using current signature analysis have been identified:
1. Non-intrusive monitoring capability at a location remote from the equipment and can be used in hostile environments;
2. Rapid measurement can be performed as frequently as desired by relatively unskilled personnel;
3. The generator current signal is not affected by the current transducer or probe location;
4. Reduction of noise levels on the measured signal;
5. Generator current signals can be used for the diagnostics of electrical, mechanical, and aerodynamic faults in a wind turbine.
6. In the case of a doubly-fed induction generator, both current and rotor signals can be used for diagnostics purposes.
These benefits make current signature analysis an attractive tool for wind turbine maintenance and fault detection. Non-intrusive monitoring capabilities allow for remote monitoring, which is critical in hostile environments, such as offshore wind farms. Additionally, frequent measurements can be taken by relatively unskilled personnel, reducing the need for specialized training and expertise. The generator current signal is not affected by the current transducer or probe location, reducing measurement errors and improving diagnostic accuracy.
Reduced noise levels on the measured signal improve the reliability of the results, leading to increased machine availability and reduced downtime. Improved maintenance planning based on diagnostic results can save money and staff time. By utilizing the generator current signal, faults can be diagnosed in multiple wind turbine components, including electrical, mechanical, and aerodynamic faults.
In the case of a doubly-fed induction generator, both current and rotor signals can be used for diagnostic purposes. Overall, current signature analysis is a valuable tool for wind turbine maintenance and fault detection, offering numerous benefits to wind farm operators.
Challenges of using generator electrical signals
While signature analysis has many advantages, additional research is required to fully leverage its potential in real-world scenarios. Examining the effects of mechanical defects on generator current is a daunting task due to noise interference and the influence of other wind turbine components and operational factors. This complexity makes it even more challenging to identify mechanical issues based on electrical signals compared to using vibration signatures for detection. There are numerous factors contributing to the complexity of utilizing induction generator electrical signal signatures:
1. The rotation speed and torque variations of the mechanical drivetrain caused by stochastic aerodynamic loads on the rotor blades make extracting mechanical fault characteristic frequencies difficult. These frequencies also change over time, making the impulse signal and its envelope non-stationary. Traditional signal processing methods, such as FFT, envelop spectrum analysis and spectral correlation, may not be effective in these cases and can lead to inaccurate results.
2. Electromechanical interactions. The complex relationship between wind turbine components, known as nonlinear electro-mechanical coupling, greatly distorts the signals that are measured. This dynamic coupling causes the signals to be nonlinear, with amplitude and frequency variations that can be difficult to interpret.
3. The low signal-to-noise ratio of the signals. Normally, during induction generator operation, a stator current contains many harmonics from different sources. The largest components present in a power spectrum density of the current occur, at multiples of the supply frequency, and they are caused by saturation, winding distribution, and supply voltage. The frequency components produced by mechanical faults are relatively small compared with the rest of the current spectrum. The presented harmonics change the stator current spectrum and mask mechanical fault features. They are drowning in the noise, and their detection is not obvious.
4. The use of feedback control loops in the generator control system can make diagnosing mechanical faults more difficult. The control system can counteract the torque variations caused by a fault, making the fault's characteristics in the generator current signals less pronounced and harder to detect.
5. A situation when a wind turbine has more than one fault during operation is common. The presence of multiple or compound faults of different natures can change wind turbine dynamics considerably. Thus, diagnostics methods that usually work well for individual faults may have a limited capability for compound fault diagnostics. In this situation, to be able to detect and identify multiple faults, new fault diagnostics methods and techniques have to be developed.
6. A wind turbine with a doubly-fed induction generator uses a partial-scale power converter to control the rotor currents. Because the rotor windings are energized by the PWM voltage source, some parasitic harmonics related to the PWM source will be introduced in the rotor windings. These harmonics will be reflected in rotor MMF, and as a result in air-gap flux density. Variations in the rotor power supply change the stator current spectrum and mask mechanical fault features.
7. The vibration signal generated by a mechanical fault in a wind turbine can be weakened during transmission from the origin of the fault to the induction generator. This is due to factors such as gear train damping, transmission error, bearing clearance, and eccentricities. This can make it harder to identify mechanical faults when the faulty component is located farther away from the generator.
8. The identification of mechanical faults in generator current signals can be made more complex by various measurement and manufacturing errors. These errors can add noise and variability to the signals, making it more difficult to detect and diagnose faults.
Overall, the complexity and variability of wind turbine operations and the numerous factors that affect generator current signals make it challenging to accurately identify mechanical faults. However, advancements in signal processing and fault diagnosis techniques, such as machine learning and artificial intelligence, may offer promising solutions. Additionally, improvements in measurement technologies and fault detection algorithms can help mitigate some of the challenges posed by noise and other sources of variability. Further research and development are needed to fully leverage the potential of current signature analysis in practical wind turbine applications.
Conclusion
To summarize, this article has examined the use of current signature analysis for mechanical fault diagnostics, exploring its historical background and fundamental principles. While current signature analysis has demonstrated its potential as a viable alternative to traditional vibration signature analysis in wind turbines, further research is required before it can be widely accepted in the industry. As such, ongoing investigation is needed to fully understand and validate the efficacy of this method.
About the Author: Iurii Storozhenko received his specialist degree (analog of Master's Degree) in Electrical Engineering in 2004, from Zapolyarny State University. From 2007 to 2015, he was with "Zapolyarnaya Building Company", Norilsk, Russia, as a Deputy Chef Power Electrical Engineer. Currently, he is pursuing a Ph.D. degree at the University of Calgary, Canada, where his research focuses on a wide range of topics, including wind energy, fault diagnostics, health condition monitoring, signal processing, and mathematical modeling.
References:
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3. Wang, Y., Wu, J., & Yang, J. (2017). A novel fault diagnosis method for blade pitch system of wind turbine based on current signature analysis. Energy Procedia, 142, 98-104. doi: 10.1016/j.egypro.2017.12.327
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