Xiang Gong

Bearing Fault Diagnosis for Direct-Drive Wind Turbines via Current-Demodulated Signals

Bearing faults account for a large portion of all faults in wind turbine generators (WTGs). Current-based bearing fault diagnosis techniques have great economic benefits and are potential to be adopted by the wind energy industry. This paper models the modulation effects of bearing faults on the stator currents of a direct-drive wind turbine equipped with a permanent-magnet synchronous generator (PMSG) operating with a variable shaft rotating frequency. Based on the analysis, a method consisting of appropriate current frequency and amplitude demodulation algorithms and a 1P-invariant power spectrum density algorithm is proposed for bearing fault diagnosis of variable-speed direct-drive wind turbines using only one-phase stator current measurements, where 1P frequency stands for the shaft rotating frequency of a wind turbine. Experimental results on a direct-drive wind turbine equipped with a PMSG operating in a wind tunnel are provided to verify the proposed fault diagnosis method. The proposed method is demonstrated to have advantages over the method of directly using stator current measurements for WTG bearing fault diagnosis.

Wind Speed and Rotor Position Sensorless Control for Direct-Drive PMG Wind Turbines

This paper proposes a wind speed and rotor position sensorless control for wind turbines directly driving permanent magnetic generators (PMGs). A sliding-mode observer is designed to estimate the rotor position of the PMG by using the measured stator currents and the commanded stator voltages obtained from the control scheme of the machine-side converter of the PMG wind turbine. The rotor speed of the PMG (i.e., the turbine shaft speed) is estimated from its back electromotive force using a model adaptive reference system observer. Based on the measured output electrical power and estimated rotor speed of the PMG, the mechanical power of the turbine is estimated by taking into account the power losses of the wind turbine generator system. A back-propagation artificial neural network is then designed to estimate the wind speed in real time by using the estimated turbine shaft speed and mechanical power. The estimated wind speed is used to determine the optimal shaft speed reference for the PMG control system. Finally, a sensorless control is developed for the PMG wind turbines to continuously generate the maximum electrical power without using any wind speed or rotor position sensors. The validity of the proposed estimation and control algorithms are shown by simulation studies on a 3-kW PMG wind turbine and are further demonstrated by experimental results on a 300-W practical PMG wind turbine.

Imbalance Fault Detection of Direct-Drive Wind Turbines Using Generator Current Signals

Imbalance faults constitute a significant portion of all faults in wind turbine generators (WTGs). WTG imbalance fault detection using generator current measurements has advantages over traditional vibration-based methods in terms of cost, implementation, and system reliability. However, there are challenges in using current signals for imbalance fault detection due to low signal-to-noise ratio of the useful information in current signals and nonstationary characteristic frequencies of imbalance faults. This paper proposes a method of using generator stator currents for imbalance fault detection of direct-drive WTGs. In the proposed method, the variable shaft rotating frequency of a WTG is estimated from one phase stator current measured from the generator terminal by using a phase-locked loop method. The estimated shaft rotating frequency is then processed by using appropriate upsampling and variable-rate downsampling algorithms. Consequently, the variable characteristic frequencies of imbalance faults in the spectrum of the estimated shaft rotating frequency are converted to constant values. Therefore, the signatures of wind turbine imbalance faults can be clearly identified from power spectral density analysis of the converted shaft rotating frequency signal. Simulation and experimental results show that the proposed method is effective to detect various imbalance faults in direct-drive WTGs.

Current-Based Mechanical Fault Detection for Direct-Drive Wind Turbines via Synchronous Sampling and Impulse Detection

Online fault detection is an effective means to improve wind turbine reliability and performance and reduce wind turbine downtime and operating and maintenance costs. Current-based wind turbine fault detection techniques have received more and more attention in academia and industry due to their nonintrusive character and economic advantages. This paper presents a novel computationally efficient high-resolution wideband synchronous sampling algorithm for the mechanical fault detection of variable-speed direct-drive wind turbines (i.e., no gearbox) only using nonstationary generator stator current measurements. The proposed algorithm synchronously resamples the current signals such that the varying characteristic frequencies of the excitations generated by wind turbine faults in the current signals become constant values. An impulse detection algorithm is then proposed to detect the faults by identifying the excitations from the frequency spectra of the synchronously sampled stator current signals. Experimental studies are carried out to demonstrate the effectiveness of the proposed algorithms for the detection of rotor eccentricity and bearing faults of a direct-drive wind turbine operating in variable-speed conditions.

Current-based diagnosis for gear tooth breaks in wind turbine gearboxes

Gearbox faults constitute a significant portion of all faults and downtime in wind turbines (WTs). Current-based gearbox fault diagnosis has significant advantages over traditional vibration-based techniques in terms of cost, implementation, and reliability. This paper derives a mathematical model for a WT drive train consisting of a two-stage gearbox and a permanent magnet (PM) generator, from which the characteristic frequencies of gear tooth breaks in generator stator current frequency spectra are clearly identified. A adaptive signal resampling algorithm is proposed to convert the variable fault characteristic frequencies to constant values for WTs running at variable speeds. A fault detector is proposed for diagnosis of gear tooth breaks using statistical analysis on the fault signatures extracted from the stator current spectra. Experimental results on a real gearbox are provided to show the effectiveness of the proposed model and method for diagnosis of gear tooth breaks.

Wind turbine fault detection and isolation using support vector machine and a residual-based method

This paper proposes a novel scheme combining support vector machines (SVM) and a residual-based method for wind turbine fault detection and isolation (FDI). SVMs with radius basis function kernels are used for detecting and identifying sensor stuck and offset faults, where binary codes of fault types are used as the outputs of the SVMs to minimize the number of SVMs being used. The same output of a SVM may correspond to different types of faults and the final decision is made by all SVMs instead of one SVM. Moreover, a residual-based fault detection method using a time-variant threshold is developed to identify the abrupt change and scaling faults. Monte Carlo simulations are carried out in MATLAB to test the effectiveness and robustness of the proposed FDI methods using a wind turbine FDI benchmark model. Results show that the proposed methods can always detect the faults successfully within the required time limits.

Bearing fault detection for direct-drive wind turbines via stator current spectrum analysis

Bearing faults constitute a significant portion of all faults in wind turbine generators (WTGs). Current-based bearing fault detection has significant advantages over traditional vibration-based methods in terms of cost, implementation, and system reliability. This paper proposes a method based on stator current power spectral density (PSD) analysis for bearing fault detection of direct-drive WTGs. In the proposed method, appropriate interpolation/up-sampling and down-sampling algorithms are designed to convert the variable fundamental frequency of the stator current to a fixed frequency according to the estimated fundamental speed of the WTG. Consequently, the characteristic frequencies of bearing faults can be clearly identified form the resulting stator current PSD. Experimental results show that the proposed method can effectively detect bearing outer-race and inner-race defects for a direct-drive WTG.

Mechanical sensorless maximum power tracking control for direct-drive PMSG wind turbines

Wind turbine generators (WTGs) are usually equipped with mechanical sensors to measure wind speed and rotor position for system control, monitoring, and protection. The use of mechanical sensors increases the cost and hardware complexity and reduces the reliability of the WTG systems. This paper proposes a mechanical sensorless maximum power tracking control for wind turbines directly driving permanent magnetic synchronous generators (PMSGs). In the proposed algorithm, the PMSG rotor position is estimated from the measured stator voltages and currents by using a sliding mode observer (SMO). The wind turbine shaft speed is estimated from the PMSG back electromotive force (EMF) using a model adaptive reference system (MRAS) observer. A back propagation artificial neural network (BPANN) is designed to generate the optimal shaft speed reference in real time by using the estimated turbine shaft speed and the measured PMSG electrical power. A control system is developed for the PMSG wind turbine to continuously track the optimal shaft speed reference to generate the maximum electrical power without using any wind speed or rotor position sensors. The validity of the proposed control algorithm is shown by simulation studies on a 3-kW PMSG wind turbine and experimental results on a practical 300-W PMSG wind turbine.

Simulation investigation of wind turbine imbalance faults

This paper investigates the use of simulations to study wind turbine imbalance faults. The dynamics of a model wind turbine generator (WTG) are simulated in a combined environment of TurbSim, FAST (Fatigue, Aerodynamics, Structures, Turbulence), and Simulink in three different scenarios, i.e., normal operating conditions, blade imbalance, and aerodynamic asymmetry. The blade imbalance is simulated by scaling the mass density of one blade, which creates an uneven distribution of mass with respect to the rotor. The aerodynamic asymmetry is simulated by adjusting the pitch of one blade, which creates an uneven torque across the rotor. The time-domain simulation results of the WTG output electric power are transformed into the frequency domain using the Fast Fourier Transform (FFT). A power spectrum density (PSD)-based method is then developed to compare two imbalance fault scenarios with the normal operating conditions in the frequency domain. Results clearly show that both the blade imbalance and the aerodynamic asymmetry generate an excitation in the output electric power with the characteristic frequencies the same as the rotating frequencies of the wind turbine. This work provides preliminary results that are useful for online detection of imbalance faults for wind turbines.

Incipient bearing fault detection via wind generator stator current and wavelet filter

Bearing faults constitute a significant portion of all faults in rotating machines, including wind turbine generators (WTGs). Current-based bearing fault detection has significant advantages over traditional vibration-based methods in terms of cost, implementation, and system reliability. This paper proposes a new wavelet filter-based method for incipient bearing fault detection using electric machine stator currents. The proposed method can dramatically increase the signal-to-noise ratio (SNR) of the bearing fault related signals in the stator current samples. The normalized energy of the wavelet-filtered stator current signals is mainly related to bearing faults and is applied as the index for bearing fault detection. Experiments are carried out for an induction machine with developed bearing faults; the results show that the proposed method is effective to detect the bearing faults at an early stage.