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

An Assessment on the Non-Invasive Methods for Condition Monitoring of Induction Motors

The ability to forecast motor mechanical faults at incipient stages is vital to reducing maintenance costs, operation downtime and safety hazards. This paper synthesized the progress in the research and development in condition monitoring and fault diagnosis of induction motors. The motor condition monitoring techniques are mainly classified into two categories that are invasive and non-invasive techniques. The invasive techniques are very basic, but they have some implementation difficulties and high cost. The non-invasive methods, namely MCSA, PVA and IPA, overcome the disadvantages associated to invasive methods. This book chapter reviews the various non-invasive condition monitoring methods for diagnosis of mechanical faults in induction motor and concludes that the instantaneous power analysis (IPA) and Park vector analysis (PVA) methods are best suitable for the diagnosis of small fault signatures associated to mechanical faults. Recommendations for the future research in these areas are also presented

Mathematical modelling of rolling element bearings fault for the diagnosis in the gearbox-induction machine

Inner race fault in bearing suspension is relatively the common fault in induction motors coupled with a gearbox, their detection is feasible by vibration monitoring of characteristic bearing frequencies. However, vibration signals have numerous drawbacks like signal background noise due to external excitation motion, sensitivity due to the installation position and their invasive measurement nature. For this reason, it is necessary to apply an extremely efficient method known as stator current signal analysis which offers significant savings and implementation advantages over traditional vibration monitoring. This paper represents a mathematical model for electromechanical systems and for rolling-element bearing faults to study the influence of mechanical defects on electrical variables (stator current). The novelty in this work involves three contributions: modelling of rolling bearing faults by external forces applied on the electromechanical system; Physical representation of rolling bearing fault allowing the modeling of the studied system functionality and, the influence of mechanical fault (inner race) in the electrical variables (stator current). Simulation results at the end of this paper demonstrate the effectiveness of the proposed mathematical model to detect gearbox’ bearing fault based on the electrical stator current signal with high sensitivity using fast Kurtogram approach

Tooth defect detection in planetary gears by the current signature analysis: numerical modelling and experimental measurements

Monitoring transmission systems is a huge scientific focus to prevent any anomaly and malfunctioning damaging the system. Several methods were used to investigate the gears behaviour and mainly its state. And until the last century, vibrations signals were the most performing technique in this field. However, nowadays, other alternatives are considered more accurate and accessible such as controlling the motor current signals to study the behaviour of the mechanical system. Within this context, this paper aims to study the electromechanical interaction between a double stage of planetary gearboxes driven by an asynchronous machine. The model used is based on a Park transformation for modelling the asynchronous machine and a torsional model to describe the dynamic behaviour of the double-stage planetary gearbox. Through this approach, the numerical simulations illustrate the impact of the tooth gear defect on the signature of the motor current. The results obtained from the simulations will be presented in the time domain and the frequency domain using the fast Fourier transform and the Hanning window to highlight the mechanical frequencies in the phase current spectrum. This work will be distinguished by validating the numerical results using experimental measurements, which will be displayed in order to justify the sensitivity of the model developed.The authors would like to acknowledge the help provided by the project “Dynamic behaviour of gear transmissions in nonstationary conditions”, ref. DPI2017-85390-P, funded by the Spanish Ministry of Science and Technology. They would like to thank the University of Cantabria cooperation project for the doctoral training to Sfax University students

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 and fault detection of inverter-fed rotating machinery

Condition monitoring of rotating machinery is crucial in industry. It can prevent long term outages that can prove costly, prevent injury to machine operators, and lower product quality. Induction motors, often described as the workhorse of industry, are popular in industry because of their robustness, efficiency and the need for low maintenance. They are, however, prone to faults when used improperly or under strenuous conditions. Gearboxes are also an important component in industry, used to transmit motion and force by means of successively engaging teeth. They too are prone to damage and can disrupt industrial processes if failure is unplanned for. Reciprocating compressors are widely used in the petroleum and the petrochemical industry. Their complex structure, and operation under poor conditions makes them prone to faults, making condition monitoring necessary to prevent accidents, and for maintenance decision-making and cost minimization. Various techniques have been extensively investigated and found to be reliable tools for the identification of faults in these machines. This thesis, however, sets out to establish a single non-invasive tool that can be used to identify the faults on all these machines. Literature on condition monitoring of induction motors, gearboxes, and reciprocating compressors is extensively reviewed. The time, frequency, and time-frequency domain techniques that are used in this thesis are also discussed. Statistical indicators were used in the time domain, the Fourier Transform in the frequency domain, and Wavelet Transforms in the time-frequency domain. Vibration and current, which are two of the most popular parameters for fault detection, were considered. The test rig equipment that is used to carry to the experiments, which comprised a modified Machine Fault Simulator -Magnum (MFS-MG), is presented and discussed. The fault detection strategies rely on the presence of a fault signature. The test rig that was used allows for the simulation of individual or multiple concurrent faults to the test machinery. The experiments were carried out under steady-state and transient conditions with the faults in the machines isolated, and then with multiple faults implemented concurrently. The results of the fault detection strategies are analysed, and conclusions are drawn based on the performances of these tools in the detection of the faults in the machinery

A brief status on condition monitoring and fault diagnosis in wind energy conversion systems

International audienceThere is a constant need for the reduction of operational and maintenance costs of Wind Energy Conversion Systems (WECSs). The most efficient way of reducing these costs would be to continuously monitor the condition of these systems. This allows for early detection of the degeneration of the generator health, facilitating a proactive response, minimizing downtime, andmaximizing productivity. Wind generators are also inaccessible since they are situated on extremely high towers, which are normally 20 mor more in height. There are also plans to increase the number of offshore sites increasing the need for a remote means of WECS monitoring that eliminates some of the difficulties faced due to accessibility problems. Therefore and due to the importance of conditionmonitoring and fault diagnosis in WECS (blades, drive trains, and generators), and keeping in mind the need for future research, this paper is intended as a brief status describing different types of faults, their generated signatures, and their diagnostic schemes