Stator turn fault detection by 2nd harmonic in instantaneous power for a triple redundant fault-tolerant PM drive

Fast and reliable detection of stator faults is of key importance for fail-safe and fault tolerant machine drives in order to immediately trigger appropriate fault mitigation actions. The paper presents a detailed analytical and experimental analysis of the behavior of a closed loop controlled permanent magnet machine drive under inter-turn fault conditions. It is shown that significant 2nd harmonic components in the dq voltages, currents, instantaneous active power (IAP) and reactive power (IRP) are generated during turn fault conditions. The analyses further show that the increase of the 2nd harmonic in IAP and IRP during fault conditions is comparatively higher than that of voltage and current, making them ideal candidates as turn fault indicators. A turn fault detection technique based on 2nd harmonic in IAP and IRP is implemented and demonstrated for a triple redundant, fault tolerant permanent magnet assisted synchronous reluctance machine (PMA SynRM) drive. The effectiveness of the proposed detection technique over the whole operation region is assessed, demonstrating fast and reliable detection over most of the operating region under both motoring and generating mode

Real-Time Machine Learning Based Open Switch Fault Detection and Isolation for Multilevel Multiphase Drives

Due to the rapid proliferation interest of the multiphase machines and their combination with multilevel inverters technology, the demand for high reliability and resilient in the multiphase multilevel drives is increased. High reliability can be achieved by deploying systematic preventive real-time monitoring, robust control, and efficient fault diagnosis strategies. Fault diagnosis, as an indispensable methodology to preserve the seamless post-fault operation, is carried out in consecutive steps; monitoring the observable signals to generate the residuals, evaluating the observations to make a binary decision if any abnormality has occurred, and identifying the characteristics of the abnormalities to locate and isolate the failed components. It is followed by applying an appropriate reconfiguration strategy to ensure that the system can tolerate the failure. The primary focus of presented dissertation was to address employing computational and machine learning techniques to construct a proficient fault diagnosis scheme in multilevel multiphase drives. First, the data-driven nonlinear model identification/prediction methods are used to form a hybrid fault detection framework, which combines module-level and system-level methods in power converters, to enhance the performance and obtain a rapid real-time detection. Applying suggested nonlinear model predictors along with different systems (conventional two-level inverter and three-level neutral point clamped inverter) result in reducing the detection time to 1% of stator current fundamental period without deploying component-level monitoring equipment. Further, two methods using semi-supervised learning and analytical data mining concepts are presented to isolate the failed component. The semi-supervised fuzzy algorithm is engaged in building the clustering model because the deficient labeled datasets (prior knowledge of the system) leads to degraded performance in supervised clustering. Also, an analytical data mining procedure is presented based on data interpretability that yields two criteria to isolate the failure. A key part of this work also dealt with the discrimination between the post-fault characteristics, which are supposed to carry the data reflecting the fault influence, and the output responses, which are compensated by controllers under closed-loop control strategy. The performance of all designed schemes is evaluated through experiments

Real-Time Machine Learning Based Open Switch Fault Detection and Isolation for Multilevel Multiphase Drives

Due to the rapid proliferation interest of the multiphase machines and their combination with multilevel inverters technology, the demand for high reliability and resilient in the multiphase multilevel drives is increased. High reliability can be achieved by deploying systematic preventive real-time monitoring, robust control, and efficient fault diagnosis strategies. Fault diagnosis, as an indispensable methodology to preserve the seamless post-fault operation, is carried out in consecutive steps; monitoring the observable signals to generate the residuals, evaluating the observations to make a binary decision if any abnormality has occurred, and identifying the characteristics of the abnormalities to locate and isolate the failed components. It is followed by applying an appropriate reconfiguration strategy to ensure that the system can tolerate the failure. The primary focus of presented dissertation was to address employing computational and machine learning techniques to construct a proficient fault diagnosis scheme in multilevel multiphase drives. First, the data-driven nonlinear model identification/prediction methods are used to form a hybrid fault detection framework, which combines module-level and system-level methods in power converters, to enhance the performance and obtain a rapid real-time detection. Applying suggested nonlinear model predictors along with different systems (conventional two-level inverter and three-level neutral point clamped inverter) result in reducing the detection time to 1% of stator current fundamental period without deploying component-level monitoring equipment. Further, two methods using semi-supervised learning and analytical data mining concepts are presented to isolate the failed component. The semi-supervised fuzzy algorithm is engaged in building the clustering model because the deficient labeled datasets (prior knowledge of the system) leads to degraded performance in supervised clustering. Also, an analytical data mining procedure is presented based on data interpretability that yields two criteria to isolate the failure. A key part of this work also dealt with the discrimination between the post-fault characteristics, which are supposed to carry the data reflecting the fault influence, and the output responses, which are compensated by controllers under closed-loop control strategy. The performance of all designed schemes is evaluated through experiments

Analysis of incipient fault signatures in inductive loads energized by a common voltage bus

Recent research has demonstrated the use of electrical signature analysis (ESA), that is, the use of induction motor currents and voltages, for early detection of motor faults in the form of embedded algorithms. In the event of multiple motors energized by a common voltage bus, the cost of installing and maintaining fault monitoring and detection devices on each motor may be avoided, by using bus level aggregate electrical measurements to assess the health of the entire population of motors. In this research an approach for detecting commonly encountered induction motor mechanical faults from bus level aggregate electrical measurements is investigated. A mechanical fault indicator is computed processing the raw electrical measurements through a series of signal processing algorithms. Inference of an incipient fault is made by the percentage relative change of the fault indicator from the Âhealthy baseline, thus defining a Fault Indicator Change (FIC). To investigate the posed research problem, healthy and faulty motors with broken rotor bar faults are simulated using a detailed transient motor model. The FIC based on aggregate electrical measurements is studied through simulations of different motor banks containing the same faulty motor. The degradation in the FIC when using aggregate measurements, as compared to using individual motor measurements, is investigated. For a given motor bank configuration, the variation in FIC with increasing number of faulty motors is also studied. In addition to simulation studies experimental results from a two-motor setup are analyzed. The FIC and degradation in the FIC in the case of load eccentricity fault, and a combination of shaft looseness and bearing damage is studied through staged fault experiments in the laboratory setup. In this research, the viability of using bus level aggregate electrical measurements for detecting incipient faults in motors energized by a common voltage bus is demonstrated. The proposed approach is limited in that as the power rating fraction of faulty motors to healthy motors in a given configuration decreases, it becomes far more difficult to detect the presence of incipient faults at very early stages

Detection of faults in a scaled down doubly-fed induction generator using advanced signal processing techniques.

The study ventures into the development of a micro-based doubly fed induction generator (DFIG) test rig for fault studies. The 5kW wound rotor induction machine (WRIM) that was used in the test rig was based on a scaled-down version of a 2.5MW doubly fed induction generator (DFIG). The micromachine has been customized to make provision for implementing stator inter-turn short-circuit faults (ITSCF), rotor ITSCF and static eccentricity (SE) faults in the laboratory environment. The micromachine has been assessed under the healthy and faulty states, both before and after incorporating a converter into the rotor circuit of the machine. In each scenario, the fault signatures have been characterised by analyzing the stator current, rotor current, and the DFIG controller signals using the motor current signature analysis (MCSA) and discrete wavelet transform (DWT) analysis techniques to detect the dominant frequency components which are indicative of these faults. The purpose of the study is to evaluate and identify the most suitable combination of signals and techniques for the detection of each fault under steady-state and transient operating conditions. The analyses of the results presented in this study have indicated that characterizing the fault indicators independent of the converter system ensured clarity in the fault diagnosis process and enabled the development of a systematic fault diagnosis approach that can be applied to a controlled DFIG. It has been demonstrated that the occurrence of the ITSCFs and the SE fault in the micro-WRIM intensifies specific frequency components in the spectral plots of the stator current, rotor current, and the DFIG controller signals, which may then serve as the dominant fault indicators. These dominant components may be used as fault markers for classification and have been used for pattern recognition under the transient condition. In this case, the DWT and spectrogram plots effectively illustrated characteristic patterns of the dominant fault indicators, which were observed to evolve uniquely and more distinguishable in the rotor current signal compared to the stator current signal, before incorporating the converter in the rotor circuit. Therefore, by observing the trends portrayed in the decomposition bands and the spectrogram plots, it is deemed a reliable method of diagnosing and possibly quantifying the intensity of the faults in the machine. Once the power electronic converter was incorporated into the rotor circuit, the DFIG controller signals have been observed to be best suited for diagnosing faults in the micro-DFIG under the steady-state operating condition, as opposed to using the terminal stator or rotor current signals. The study also assessed the impact of undervoltage conditions at the point of common coupling (PCC) on the behaviour of the micro-DFIG. In this investigation, a significant rise in the faulted currents was observed for the undervoltage condition in comparison to the faulty cases under the rated grid voltage conditions. In this regard, it could be detrimental to the operation of the micro-DFIG, particularly the faulted phase windings, and the power electronic converter, should the currents exceed the rated values for extended periods

Advanced Fault Detection Methods for Permanent Magnets Synchronous Machines

The trend in recent years of transport electrification has significantly increased the demand for reliability and availability of electric drives, particularly in those employing Permanent Magnet Synchronous Machines (PMSM), often selected due to their high efficiency and energy density. Fault detection has been identified as one of the key aspects to cover such demand. Stator winding faults are known to be the second most common type of fault, after bearing fault. An extensive literature review has shown that, although a number of methods has been proposed to address this type of fault, no tool of general application, capable of dealing effectively with fault detection under transient conditions unrelated to the fault, has been proposed up to date. This thesis has made contributions to modelling, real-time emulation and stator winding fault detection of PMSM. Fault detection has been carried out through model-based and signal-based methods with a specific aim at operation during transient conditions. Furthermore, fault classification methods already available have been implemented with features computed by proposed signal-based fault detection methods. The main conclusion drawn from this thesis is that model-based fault detection methods, particularly those based on residuals, appear to be better suited for transient conditions analysis, as opposed to signal-based fault detection methods. However, it is expected that a combination of the two (model/signal) would yield the best results

Design and Control of a Unique Hydrogen Fuel Cell Plug-In Hybrid Electric Vehicle

The University of Waterloo Alternative Fuels Team (UWAFT) is a student team that designs and builds vehicles with advanced powertrains. UWAFT uses alternatives to fossil fuels because of their lower environmental impacts and the finite nature of oil resources. UWAFT participated in the EcoCAR Advanced Vehicle Technology Competition (AVTC) from 2008 to 2011. The team designed and built a Hydrogen Fuel Cell Plug-In Hybrid Electric Vehicle (FC-PHEV) and placed 3rd out of 16 universities from across North America. UWAFT design projects offer students a unique opportunity to advance and augment their core engineering knowledge with hands-on learning in a project-based environment. The design of thermal management systems for powertrain components is a case study for design engineering which requires solving open ended problems, and is a topic that is of growing importance in undergraduate engineering courses. Students participating in this design project learn to develop strategies to overcome uncertainty and to evaluate and execute designs that are not as straightforward as those in a textbook. Electrical and control system projects require students to introduce considerations for reliability and robustness into their design processes that typically only focus on performance and function, and to make decisions that balance these considerations in an environment where these criteria impact the successful outcome of the project. The consequences of a failure or unreliable design also have serious safety implications, particularly in the implementation of powertrain controls. Students integrate safety into every step of control system design, using tools to identify and link together component failures and vehicle faults, to design detection and mitigation strategies for safety-critical failures, and to validate these strategies in real-time simulations. Student teams have the opportunity to offer a rich learning environment for undergraduate engineering students. The design projects and resources that they provide can significantly advance student knowledge, experience, and skills in a way that complements the technical knowledge gained in the classroom. Finding ways to provide these experiences to more undergraduate students, either outside or within existing core courses, has the potential to enhance the value of program graduates

A hybrid intelligent technique for induction motor condition monitoring

The objective of this research is to advance the field of condition monitoring and fault diagnosis for induction motors. This involves processing the signals produced by induction motors, classifying the types and estimating the severity of induction motors faults. A typical process of condition monitoring and fault diagnosis for induction motors consists of four steps: data acquisition, signal analysis, fault detection and post-processing. A description of various kinds of faults that can occur in induction motors is presented. The features reflecting faults are usually embedded in transient motor signals. The signal analysis is a very important step in the motor fault diagnosis process, which is to extract features which are related to specific fault modes. The signal analysis methods available in feature extraction for motor signals are discussed. The wavelet packet decomposition results consist of the time-frequency representation of a signal in the same time, which is inherently suited to the transient events in the motor fault signals. The wavelet packet transform-based analysis method is proposed to extract the features of motor signals. Fault detection has to establish a relationship between the motor symptoms and the condition. Classifying motor condition and estimating the severity of faults from the motor signals have never been easy tasks and they are affected by many factors. AI techniques, such as expert system (ES), fuzzy logic system (FLS), artificial neural network (ANN) and support vector machine (SVM), have been applied in fault diagnosis of very complex system, where accurate mathematical models are difficult to be built. These techniques use association, reasoning and decision making processes as would the human brain in solving diagnostic problems. ANN is a computation and information processing method that mimics the process found in biological neurons. But when ANN-based methods are used for fault diagnosis, local minimums caused by the traditional training algorithms often result in large approximation error that may destroy their reliability. In this research, a novel method of condition monitoring and fault diagnosis for induction motor is proposed using hybrid intelligent techniques based on WPT. ANN is trained by improved genetic algorithm (IGA). WPT is used to decompose motor signals to extract the feature parameters. The extracted features with different frequency resolutions are used as the input of ANN for the fault diagnosis. Finally, the proposed method is tested in 1.5 kW and 3.7 kW induction motor rigs. The experimental results demonstrate that the proposed method improves the sensitivity and accuracy of the ANN-based methods of condition monitoring and fault diagnosis for induction motors.EThOS - Electronic Theses Online ServiceGBUnited Kingdo