Modern wind turbines operate in continuously transient conditions, with varying speed, torque, and power based on the stochastic nature of the wind resource. This variability affects not only the operational performance of the wind power system, but can also affect its integrity under service conditions. Condition monitoring continues to play an important role in achieving reliable and economic operation of wind turbines. This paper reviews the current advances in wind turbine condition monitoring, ranging from conventional condition monitoring and signal processing tools to machine-learning-based condition monitoring and usage of big data mining for predictive maintenance. A systematic review is presented of signal-based and data-driven modeling methodologies using intelligent and machine learning approaches, with the view to providing a critical evaluation of the recent developments in this area, and their applications in diagnosis, prognosis, health assessment, and predictive maintenance of wind turbines and farms.

This paper presents a review of the recent trends and the current state of the art in the application of fiber optic fiber Bragg gratings (FBG) sensing technology to condition the monitoring (CM) and testing of practical electric machinery and the associated power equipment. FBG technology has received considerable interest in this field in recent years, with research demonstrating that the flexible, multi-physical, and electromagnetic interference (EMI) immune in situ sensing of a multitude of physical measurands of CM interest is possible and cannot be obtained through conventional sensing means. The unique FBG sensing ability has the potential to unlock many of the electric machine CM and design validation restrictions imposed by the limitations of conventional sensing techniques but needs further research to attain wider adoption. This paper first presents the fundamental principles of FBG sensing. This is followed by a description of recent FBG sensing techniques proposed for electric machinery and associated power equipment and a discussion of their individual benefits and limitations. Finally, an outlook for the further application of this technique is presented. The underlying intention is for the review to provide an up-to-date overview of the state of the art in this area and inform future developments in FBG sensing in electric machinery.

This paper presents a novel study of the wide-band spectral signatures in the controller signals of doubly fed induction generators (DFIGs) for the identification of rotor electrical faults. The aim is to advance the understanding of diagnostic information obtainable from the readily available DFIG controller signals. Analytical equations defining the controller signals possible spectral contents are derived to enable characterization of spectral signatures and their correlation to operating conditions and rotor faults. The equations are verified in a DFIG harmonic model study and also validated by undertaking a range of experiments on a laboratory DFIG test-rig. It is shown that the calculated, simulated and experimental results are in good agreement with regards to representing fault induced signatures in the examined DFIG controller signals spectra. Furthermore, it is shown that wide-band rotor electrical fault related spectral signatures in the controller signals carry considerable diagnostic potential for recognition of rotor electrical faults.

this study explores the potential for using FBG strain sensing to enable recognition of the shaft misalignment condition in electric machine drivetrains through observation of machine frame distributed relative strain. The sensing principles, design and installation methods of the proposed technique are detailed in the paper. The scheme was applied on a purpose built wind turbine generator representative laboratory test rig and its performance evaluated in an extensive experimental study involving a range of healthy and misaligned shaft operating conditions. The obtained experimental data demonstrate the reported method’s capability to enable recognition of generator shaft misalignment conditions and thus its health monitoring. Finally, it is shown that the thermal variation of the generator frame structure inherent to its operation, combined with the FBG sensor intrinsic thermo-mechanical cross sensitivity, has no detrimental impact on the fidelity and usability of the observed strain measurements.

This paper presents a novel study of stator electrical faults detection using the wide-band spectral signatures in the controller signals of doubly fed induction generators (DFIGs). The possible wide-band spectral content of the controller signals are first defined with a set of analytical equations in order to characterize the wide-band spectral signatures and their correlation to operating conditions including stator electrical faults. A DFIG harmonic model and laboratory test-rig are used to validate the derived equations. The calculated, simulated and experimental results are in good agreement. Furthermore, it is shown that the wide-band stator electrical fault related spectral signatures carry considerable diagnostic potential for recognition of stator electrical faults, and therefore, can potentially be used to advance controller embedded fault detection techniques.

This paper presents the development of a doubly fed induction machine (DFIG) harmonic model in MATLAB/Simulink, which is used to examine the spectral content of DFIG controller signals and improve the understanding of their behavior and spectral nature. The reported DFIG harmonic model has the capability of representing the effects of higher order time and space harmonics and thus, allows detailed analysis of the controller signals embedded spectral effects. The model consists of a wound rotor induction machine (WRIM) harmonic model coupled with a stator flux oriented controller (SFOC) model. The WRIM space harmonic effects are represented using the conductor distribution function approach to enable the calculation of winding inductances as harmonic series. In addition, analytical expressions are derived to define the possible spectral content in the controller signals of DFIGs. Both the reported DFIG harmonic model and the analytical equations are validated by comparison with measurements taken from a purpose built vector controlled DFIG laboratory test-rig. The findings confirm the capability of the developed DFIG harmonic model in representing the controller signals embedded spectral effects, as well as the accuracy of the reported analytical expressions, and enable a much improved understanding of the spectral nature of the DFIG controller signals.