Short-circuit fault diagnosis of the DC-Link capacitor and its impact on an electrical drive system

The reliability of a motor control based on a variable speed drive is an important issue for industrial applications. Most of these machines are inverter based induction motors and are used in specific and complex industrial installations. Unlike the induction motor, the feeding part is very delicate and sensitive to faults. In order to improve system performance, it is therefore very important for a researcher to know the impact of a fault on the whole of his drive system. This paper discusses the short-circuit fault of the DC-link capacitor of an inverter fed induction motor. The simulation results of this type of faults are presented and its impact on the behavior of the rectifier, the inverter as well as the induction motor analyzed and interpreted

Converter fault diagnosis and post-fault operation of a doubly-fed induction generator for a wind turbine

Wind energy has become one of the most important alternative energy resources because of the global warming crisis. Wind turbines are often erected off-shore because of favourable wind conditions, requiring lower towers than on-shore. The doubly-fed induction generator is one of the most widely used generators with wind turbines. In such a wind turbine the power converters are less robust than the generator and other mechanical parts. If any switch failure occurs in the converters, the wind turbine may be seriously damaged and have to stop. Therefore, converter health monitoring and fault diagnosis are important to improve system reliability. Moreover, to avoid shutting down the wind turbine, converter fault diagnosis may permit a change in control strategy and/or reconfigure the power converters to permit post-fault operation. This research focuses on switch fault diagnosis and post-fault operation for the converters of the doubly-fed induction generator. The effects of an open-switch fault and a short-circuit switch fault are analysed. Several existing open-switch fault diagnosis methods are examined but are found to be unsuitable for the doubly-fed induction generator. The causes of false alarms with these methods are investigated. A proposed diagnosis method, with false alarm suppression, has the fault detection capability equivalent to the best of the existing methods, but improves system reliability. After any open-switch fault is detected, reconfiguration to a four-switch topology is activated to avoid shutting down the system. Short-circuit switch faults are also investigated. Possible methods to deal with this fault are discussed and demonstrated in simulation. Operating the doubly-fed induction generator as a squirrel cage generator with aerodynamic power control of turbine blades is suggested if this fault occurs in the machine-side converter, while constant dc voltage control is suitable for a short-circuit switch fault in the grid-side converter.Wind energy has become one of the most important alternative energy resources because of the global warming crisis. Wind turbines are often erected off-shore because of favourable wind conditions, requiring lower towers than on-shore. The doubly-fed induction generator is one of the most widely used generators with wind turbines. In such a wind turbine the power converters are less robust than the generator and other mechanical parts. If any switch failure occurs in the converters, the wind turbine may be seriously damaged and have to stop. Therefore, converter health monitoring and fault diagnosis are important to improve system reliability. Moreover, to avoid shutting down the wind turbine, converter fault diagnosis may permit a change in control strategy and/or reconfigure the power converters to permit post-fault operation. This research focuses on switch fault diagnosis and post-fault operation for the converters of the doubly-fed induction generator. The effects of an open-switch fault and a short-circuit switch fault are analysed. Several existing open-switch fault diagnosis methods are examined but are found to be unsuitable for the doubly-fed induction generator. The causes of false alarms with these methods are investigated. A proposed diagnosis method, with false alarm suppression, has the fault detection capability equivalent to the best of the existing methods, but improves system reliability. After any open-switch fault is detected, reconfiguration to a four-switch topology is activated to avoid shutting down the system. Short-circuit switch faults are also investigated. Possible methods to deal with this fault are discussed and demonstrated in simulation. Operating the doubly-fed induction generator as a squirrel cage generator with aerodynamic power control of turbine blades is suggested if this fault occurs in the machine-side converter, while constant dc voltage control is suitable for a short-circuit switch fault in the grid-side converter

Current-, force-, and vibration-based techniques for induction motor condition monitoring

The aim of this research was to discover the best indicators of induction motor faults, as well as suitable techniques for monitoring the condition of induction motors. Numerical magnetic field analysis was used with the objective of generating reliable virtual data to be analysed with modern signal processing and soft-computing techniques. In the first part of the research, a fuzzy system, based on the amplitudes of the motor current, was implemented for online detection of stator faults. Later on, from the simulation studies and using support vector machine (SVM), the electromagnetic force was shown to be the most reliable indicator of motor faults. Discrete wavelet transform (DWT) was applied to the stator current during the start-up transient, showing how the evolution of some frequency components allows the identification and discrimination of induction motor faults. Predictive filtering was applied to separate the harmonic components from the main current signal. The second part of the research was devoted to the development of a mechanical model to study the effects of electromagnetic force on the vibration pattern when the motor is working under fault conditions. The third part of this work, following the indications given by the second part, is concerned with a method that allows the prediction of the effect of the electromechanical faults in the force distribution and vibration pattern of the induction machines. The FEM computations show the existence of low-frequency and low-order force distributions acting on the stator of the electrical machine when it is working under an electrical fault. It is shown that these force components are able to produce forced vibration in the stator of the machine. This is corroborated by vibration measurements. These low-frequency components could constitute the primary indicator in a condition monitoring system. During the research, extensive measurements of current, flux and vibration were carried out in order to supply data for the research group. Various intentional faults, such as broken rotor bars, broken end ring, inter-turn short circuit, bearing and eccentricity failures, were created. A real dynamic eccentricity was also created. Moreover, different supply sources were used. The measurements supported the analytical and numerical results.reviewe

Variable selection for wind turbine condition monitoring and fault detection system

With the fast growth in wind energy, the performance and reliability of the wind power generation system has become a major issue in order to achieve cost-effective generation. Integration of condition monitoring system (CMS) in the wind turbine has been considered as the most viable solution, which enhances maintenance scheduling and achieving a more reliable system. However, for an effective CMS, large number of sensors and high sampling frequency are required, resulting in a large amount of data to be generated. This has become a burden for the CMS and the fault detection system. This thesis focuses on the development of variable selection algorithm, such that the dimensionality of the monitoring data can be reduced, while useful information in relation to the later fault diagnosis and prognosis is preserved. The research started with a background and review of the current status of CMS in wind energy. Then, simulation of the wind turbine systems is carried out in order to generate useful monitoring data, including both healthy and faulty conditions. Variable selection algorithms based on multivariate principal component analysis are proposed at the system level. The proposed method is then further extended by introducing additional criterion during the selection process, where the retained variables are targeted to a specific fault. Further analyses of the retained variables are carried out, and it has shown that fault features are present in the dataset with reduced dimensionality. Two detection algorithms are then proposed utilising the datasets obtained from the selection algorithm. The algorithms allow accurate detection, identification and severity estimation of anomalies from simulation data and supervisory control and data acquisition data from an operational wind farm. Finally an experimental wind turbine test rig is designed and constructed. Experimental monitoring data under healthy and faulty conditions is obtained to further validate the proposed detection algorithms

ROBUST FAULT ANALYSIS FOR PERMANENT MAGNET DC MOTOR IN SAFETY CRITICAL APPLICATIONS

Robust fault analysis (FA) including the diagnosis of faults and predicting their level of severity is necessary to optimise maintenance and improve reliability of Aircraft. Early diagnosis of faults that might occur in the supervised process renders it possible to perform important preventative actions. The proposed diagnostic models were validated in two experimental tests. The first test concerned a single localised and generalised roller element bearing fault in a permanent magnet brushless DC (PMBLDC) motor. Rolling element bearing defect is one of the main reasons for breakdown in electrical machines. Vibration and current are analysed under stationary and non-stationary load and speed conditions, for a variety of bearing fault severities, and for both local and global bearing faults. The second test examined the case of an unbalance rotor due to blade faults in a thruster, motor based on a permanent magnet brushed DC (PMBDC) motor. A variety of blade fault conditions were investigated, over a wide range of rotation speeds. The test used both discrete wavelet transform (DWT) to extract the useful features, and then feature reduction techniques to avoid redundant features. This reduces computation requirements and the time taken for classification by the application of an orthogonal fuzzy neighbourhood discriminant analysis (OFNDA) approach. The real time monitoring of motor operating conditions is an advanced technique that presents the real performance of the motor, so that the dynamic recurrent neural network (DRNN) proposed predicts the conditions of components and classifies the different faults under different operating conditions. The results obtained from real time simulation demonstrate the effectiveness and reliability of the proposed methodology in accurately classifying faults and predicting levels of fault severity.the Iraqi Ministry of Higher Education and Scientific Researc

The Untilisation of Information Available in a Sensorless Control System of an AC Induction Motor for Condition Monitoring

Induction motor driven mechanical transmission systems are widely utilised in many applications across numerous sectors including industry, power generation and transportation. They are however subject to common failure modes primarily associated with faults in the driven mechanical components. Notably, gearboxes, couplings and bearings can cause significant defects in both the electrical and mechanical systems. Condition monitoring (CM) undertakes a key role in the detection of potential defects in the early development stages and in turn avoiding catastrophic operational and financial consequences caused by unplanned breakdowns. Meanwhile, variable speed drives (VSDs) have been increasingly deployed in recent years to achieve accurate speed control and higher operational efficiency. Among the different speed control designs, sensorless VSDs deliver improved dynamic performance and obviate speed measurement devices. This solution however results in heightened noise levels and continual changes in the power supply parameters that potentially impede the detection of minute fault features. This study addresses the gap identified through a systematic review of the literature on the monitoring of mechanical systems utilise induction motors (IM) with sensorless VSDs. Specifically, existing techniques prove ineffective for common mechanical faults that develop in gearboxes and friction induced scenarios. The primary aim of this research centres on the development of a more effective and accurate diagnostic solution for VSD systems using the data available in a VSD. An experimental research approach is based to model and simulate VSD systems under different fault conditions and gather in-depth data on changes in electrical supply parameters: current, voltage and power. Corresponding techniques including model based methods and dynamic signature analysis methods were developed for extracting the changes from noise measurements. An observer based detection technique is developed based on speed and flux observers that are deployed to generate power residuals. Both static and dynamic techniques are incorporated for the first time in order to detect the mechanical misalignment and lubrication degradation, each with different degrees of severities. The results of this study demonstrate that observer based approaches utilising power residual signalling can be effective in the identification of different faults in the monitoring of sensorless VSD driven mechanical systems. Specifically, the combination between dynamic and static components of the power supply parameters and control data has proved effective in separating the four types of common faults: shaft misalignment, lubricant shortage, viscosity changes and water contamination. The static data based approach outperforms the dynamic data based approach in detecting shaft misalignments under sensorless operating modes. The dynamic components of power signals, however, records superior results in the detection of different oil degradation problems. Nevertheless, static components of torque related variables, power and voltage can be used jointly in separating the three tested lubricant faults

Electromagnetic flux monitoring for detecting faults in electrical machines

The ability of the electromagnetic flux measured in various locations of a 35-kW cage induction motor to provide useful information about faults was investigated. The usefulness of this monitoring parameter was assessed in comparison with some other electrical parameters used for fault detection, such as stator phase current and circulating currents between the parallel branches of the stator winding. The following faults were investigated in this thesis: a turn-to-turn short circuit in the stator winding; rotor cage-related faults (breakage of rotor bars); static and dynamic eccentricity, and bearing fault. The relevant fault signatures of the studied electrical parameters were obtained from measurements and/or from numerical electromagnetic field simulations in steady state. These signatures were analysed and compared in order to deduce the most appropriate quantity for the detection of a specific fault. When and where possible, the accuracy of different fault signatures issuing from numerical electromagnetic field simulations was validated by experiments. This investigation is essential since, following a good agreement, it may be assumed that if a monitoring system cannot detect and diagnose an artificial fault from the virtual measurement signals, it is hardly likely to work with real electrical machines, either. In this respect, the numerical methods of analysis limited the present study to such faults that affect the electromagnetic field of a machine. On the exclusive basis of data obtained from simulations, a study of the modifications brought by various stator winding designs to some of the asymmetrical air-gap electromagnetic flux density harmonics responsible for the detection of various faults was carried out. The analysis of a core fault (insulation fault in the stator lamination) artificially implemented in the numerical electromagnetic model of the machine in terms of finding a suitable parameter to sense such a fault was also studied in this work.reviewe