Effective algorithms for real-time wind turbine condition monitoring and fault-detection

Reliable condition monitoring (CM) can be an effective means to significantly reduce wind turbine (WT) downtime, operations and maintenance costs and plan preventative maintenance in advance. The WT generator voltage and current output, if sampled at a sufficiently high rate (kHz range), can provide a rich source of data for CM. However, the electrical output of the WT generator is frequently shown to be complex and noisy in nature due to the varying and turbulent nature of the wind. Thus, a fully satisfactory technique that is capable to provide accurate interpretation of the WT electrical output has not been achieved to date. The objective of the research described in this thesis is to develop reliable WT CM using advanced signal processing techniques so that fast analysis of non-stationary current measurements with high diagnostic accuracy is achieved. The diagnostic accuracy and reliability of the proposed techniques have been evaluated using data from a laboratory test rig where experiments are performed under two levels of rotor electrical asymmetry faults. The experimental test rig was run under fixed and variable speed driving conditions to investigate the kind of results expected under such conditions. An effective extended Kalman filter (EKF) based method is proposed to iteratively track the characteristic fault frequencies in WT CM signals as the WT speed varies. The EKF performance was compared with some of the leading WT CM techniques to establish its pros and cons. The reported experimental findings demonstrate clear and significant gains in both the computational efficiency and the diagnostic accuracy using the proposed technique. In addition, a novel frequency tracking technique is proposed in this thesis to analyse the non-stationary current signals by improving the capability of a continuous wavelet transform (CWT). Simulations and experiments have been performed to verify the proposed method for detecting early abnormalities in WT generators. The improved CWT is finally applied for developing a new real-time CM technique dedicated to detect early abnormalities in a commercial WT. The results presented highlight the advantages of the improved CWT over the conventional CWT to identify frequency components of interest and cope with the non-linear and non-stationary fault features in the current signal, and go on to indicate its potential and suitability for WT CM.</div

Condition monitoring of permanent magnet synchronous generator for wind turbine applications

Wind energy has gained a considerable attention from industries and academia to increase the reliability and availability of wind turbines (WTs) and, consequently, to reduce wind energy cost. With this attention has come investments and new technologies from WT manufacturers as industrial solutions. Among these technologies the new arrivals, the variable speed generation systems based on permanent magnet synchronous generators (PMSGs) with full-scale power converters are an emerging and promising technology. Better designs of the WT components is of course one answer to the solution of this problem; the other is condition monitoring of the WT systems. This allows to reduce maintenance cost, hardware damaging and unscheduled downtime. In this context, this paper investigates the possibility to detect mechanical faults by analysing the electrical signals with the aim of improving the reliability of WTs based on PMSGs. Rotor eccentricity is used as an illustrative example to describe how the fault signature frequencies may occur in PMSG current signals, and potentially how they may deviate from a healthy state, firstly under steady state, constant speed test operation and secondly under transient, variable speed conditions. Simulation results yield valuable information for condition monitoring and effective algorithm development for fault detection

Stator winding fault diagnosis in synchronous generators for wind turbine applications

Wind turbine manufacturers have introduced to the market a variety of innovative concepts and configurations for generators to maximize energy capture, reduce costs and improve reliability of wind energy. For the purpose of improving reliability and availability, a number of diagnostic methods have been developed. Stator current signature analysis (SCSA) is potentially an effective technique to diagnose faults in electrical machines, and could be used to detect and diagnose faults in wind turbines. In this study, an investigation was conducted into the application of SCSA to detect stator inter-turn faults in an excited synchronous generator and a permanent magnet synchronous generator. It was found from simulation results that, owing to disruption of magnetic field symmetry and imbalance between the current flowing in the shorted turn and the corresponding diametrically opposite turn in the winding, certain harmonic components in the stator current clearly increased as the number of shorted turns increased. The findings are helpful to detect faults involving only a few turns without ambiguity, in spite of the difference in the configuration of the generators. As expected, because of the different type, configuration and operational condition of the two generators studied, detecting faults through the generator current signature requires a particular approach for each generator type

Effect of power converter on condition monitoring and fault detection for wind turbine

This paper investigates the impact of power electronics converter when attempting wind turbine condition monitoring system and fault diagnosis by the analysis of fault signatures in the electrical output of the turbine. A wind turbine model has been implemented in the MATLAB/Simulink environment. Fault signature analysis for electrical signals is presented. A signal processing algorithm based on a fast fourier transform is then used to potentially identify fault signatures. The results obtained with this model are validated with experimental data measured from a physical test rig. Through comparison between simulation data and experimental data it is concluded that the power converter has significantly reduced fault signatures from the electrical signal though not entirely extinguished them. It may still be possible to extract some fault information after the converter though this is much more challenging than upstream. Further work is needed to see whether it may be possible to modify the power converter particularly the filter design and the switching elements to avoid removing fault signatures from electrical signals without adding significant cost or compromising performance

Wind turbine simulation model for the study of combined mechanical and electrical faults

Wind turbine (WT) is a complex system comprised of many different components with different functions; it is a formidable challenge to understand the dynamic behaviour of a WT and the root cause of any disturbances, owing to the integration of aerodynamic, mechanical and electrical components in an environment subject to significant stochastic environment forcing. Therefore, there is a need for robust and reliable model to study the performance of WTs in terms of truly integrated electrical and mechanical responses. This paper presents a new model for WT by using MATLAB. This model can be simulated under various operating conditions to yield valuable information for condition monitoring and effective algorithm development for fault detection

Advanced algorithms for wind turbine condition monitoring and fault diagnosis

The work undertaken in this research focuses on advanced condition monitoring and fault detection methods for wind turbines (WTs). Fourier Transform (FFT) and Short Time Fourier transform (STFT) algorithms are proposed to effectively extract fault signatures in generator current signals (GCS) for WT fault diagnosis. With this aim, a WT model has been implemented in the MATLAB/Simulink environment to validate the effectiveness of the proposed algorithms. The results obtained with this model are validated with experimental data measured from a physical test rig. The detection of rotor eccentricity is discussed and conclusions drawn on the applicability of frequency tracking algorithms. The newly developed algorithms are compared with a published method to establish their advantages and limitations

Neural networks for wind turbine fault detection via current signature analysis

Cost-effective condition monitoring techniques are required to optimise wind turbine maintenance procedures. Current signature analysis investigates fault indications in the frequency spectrum of the electrical signal and is thereby able to detect mechanical faults without additional sensors. Due to the modern variable speed operation of wind turbines, fault frequencies are hidden in the non-stationary frequency spectra. In this work, artificial neural networks are applied to identify faults under transient conditions. The feasibility of the detection algorithm is demonstrated with a wind turbine SIMULINK model, which has been validated with experimental data. A framework is proposed for developing and training the algorithm for different rotational speeds. A simulation study demonstrates the ability of the algorithm not only to detect faults, but also to identify the strength of the faults as required for fault prognosis

Effect of power converter on condition monitoring and fault detection for wind turbine

This paper investigates the impact of power electronics converter when attempting wind turbine condition monitoring system and fault diagnosis by the analysis of fault signatures in the electrical output of the turbine. A wind turbine model has been implemented in the MATLAB/Simulink environment. Fault signature analysis for electrical signals is presented. A signal processing algorithm based on a fast fourier transform is then used to potentially identify fault signatures. The results obtained with this model are validated with experimental data measured from a physical test rig. Through comparison between simulation data and experimental data it is concluded that the power converter has significantly reduced fault signatures from the electrical signal though not entirely extinguished them. It may still be possible to extract some fault information after the converter though this is much more challenging than upstream. Further work is needed to see whether it may be possible to modify the power converter particularly the filter design and the switching elements to avoid removing fault signatures from electrical signals without adding significant cost or compromising performance

Condition monitoring of permanent magnet synchronous generator for wind turbine applications

Wind energy has gained a considerable attention from industries and academia to increase the reliability and availability of wind turbines (WTs) and, consequently, to reduce wind energy cost. With this attention has come investments and new technologies from WT manufacturers as industrial solutions. Among these technologies the new arrivals, the variable speed generation systems based on permanent magnet synchronous generators (PMSGs) with full-scale power converters are an emerging and promising technology. Better designs of the WT components is of course one answer to the solution of this problem; the other is condition monitoring of the WT systems. This allows to reduce maintenance cost, hardware damaging and unscheduled downtime. In this context, this paper investigates the possibility to detect mechanical faults by analysing the electrical signals with the aim of improving the reliability of WTs based on PMSGs. Rotor eccentricity is used as an illustrative example to describe how the fault signature frequencies may occur in PMSG current signals, and potentially how they may deviate from a healthy state, firstly under steady state, constant speed test operation and secondly under transient, variable speed conditions. Simulation results yield valuable information for condition monitoring and effective algorithm development for fault detection

Stator winding fault diagnosis in synchronous generators for wind turbine applications

Wind turbine manufacturers have introduced to the market a variety of innovative concepts and configurations for generators to maximize energy capture, reduce costs and improve reliability of wind energy. For the purpose of improving reliability and availability, a number of diagnostic methods have been developed. Stator current signature analysis (SCSA) is potentially an effective technique to diagnose faults in electrical machines, and could be used to detect and diagnose faults in wind turbines. In this study, an investigation was conducted into the application of SCSA to detect stator inter-turn faults in an excited synchronous generator and a permanent magnet synchronous generator. It was found from simulation results that, owing to disruption of magnetic field symmetry and imbalance between the current flowing in the shorted turn and the corresponding diametrically opposite turn in the winding, certain harmonic components in the stator current clearly increased as the number of shorted turns increased. The findings are helpful to detect faults involving only a few turns without ambiguity, in spite of the difference in the configuration of the generators. As expected, because of the different type, configuration and operational condition of the two generators studied, detecting faults through the generator current signature requires a particular approach for each generator type