367 research outputs found
On the identifiability, parameter identification and fault diagnosis of induction machines
PhD ThesisDue to their reliability and low cost, induction machines have been widely utilized in a large
variety of industrial applications. Although these machines are rugged and reliable, they are
subjected to various stresses that might result in some unavoidable parameter changes and
modes of failures. A common practice in induction machine parameter identification and fault
diagnosis techniques is to employ a machine model and use the external measurements of
voltage, current, speed, and/or torque in model solution. With this approach, it might be possible
to get an infinite number of mathematical solutions representing the machine parameters,
depending on the employed machine model. It is therefore crucial to investigate such possibility
of obtaining incorrect parameter sets, i.e. to test the identifiability of the model before being
used for parameter identification and fault diagnosis purposes. This project focuses on the
identifiability of induction machine models and their use in parameter identification and fault
diagnosis.
Two commonly used steady-states induction machine models namely T-model and inverse Γ-
model have been considered in this thesis. The classical transfer function and bond graph
identifiability analysis approaches, which have been previously employed for the T-model, are
applied in this thesis to investigate the identifiability of the inverse Γ-model. A novel algorithm,
the Alternating Conditional Expectation, is employed here for the first time to study the
identifiability of both the T- and inverse Γ-models of the induction machine. The results
obtained from the proposed algorithm show that the parameters of the commonly utilised Tmodel
are non-identifiable while those of the inverse Γ-model are uniquely identifiable when
using external measurements. The identifiability analysis results are experimentally verified by
the particle swarm optimization and Levenberg-Marquardt model-based parameter
identification approaches developed in this thesis.
To overcome the non-identifiability problem of the T-model, a new technique for induction
machine parameter estimation from external measurements based on a combination of the
induction machine’s T- and inverse Γ-models is proposed. Results for both supply-fed and
inverter-fed operations show the success of the technique in identifying the parameters of the
machine using only readily available measurements of steady-state machine current, voltage
and speed, without the need for extra hardware.
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A diagnosis scheme to detect stator winding faults in induction machines is also proposed in
this thesis. The scheme uses time domain features derived from 3-phase stator currents in
conjunction with particle swarm optimization algorithm to check characteristic parameters of
the machine and detect the fault accordingly. The validity and effectiveness of the proposed
technique has been evaluated for different common faults including interturn short-circuit,
stator winding asymmetry (increased resistance in one or more stator phases) and combined
faults, i.e. a mixture of stator winding asymmetry and interturn short-circuit. Results show the
accuracy of the proposed technique and it is ability to detect the presence of the fault and
provide information about its type and location.
Extensive simulations using Matlab/SIMULINK and experimental tests have been carried out
to verify the identifiability analysis and show the effectiveness of the proposed parameter
identification and fault diagnoses schemes. The constructed test rig includes a 1.1 kW threephase
test induction machine coupled to a dynamometer loading unit and driven by a variable
frequency inverter that allows operation at different speeds. All the experiment analyses
provided in the thesis are based on terminal voltages, stator currents and rotor speed that are
usually measured and used in machine control.Libya, through the Engineering Faculty of Misurata-
Misurata Universit
Development of new methods for nonintrusive induction motor energy efficiency estimation
Induction motors (IMs) are the most widely used motors in industries. They constitute about 70% of the total motors used in industries and are the largest energy consumers in industrial applications. As a result of the increasing need for energy savings and demand-side management, the development of methods for accurate energy efficiency estimation has become a crucial area of research. While several methods have been proposed for induction motor efficiency determination, majority of the methods cannot be easily applied in the field owing to the intrusive nature of the test procedures involved. This PhD work presents some novel methods for nonintrusive efficiency estimation of induction motors operating on-site using limited motor terminal measurements and nameplate data. The first method is developed for induction motors operating on sinusoidal supply source (line-fed). The method uses a modified inverse Đ“-model equivalent circuit with series core loss arrangement to mitigate the inherent problems of higher computational burden and parameter redundancy associated with the conventional equivalent circuit method. Furthermore, a new method is presented for estimating the friction and windage loss using the airgap torque and motor nameplate data. The proposed Nonintrusive Field Efficiency Estimation (NFEE) technique was validated experimentally on four different induction motors for both balanced and unbalanced voltage supply conditions. The results demonstrate the accuracy of the proposed NFEE method and confirm its advantage over the conventional equivalent circuit method. In addition to the problem of unbalanced voltage supply, the presence of harmonics significantly affects the operation of induction motors. The second novel approach for estimating efficiency proposed in this PhD work extends the NFEE method to cover for non-sinusoidal supply condition. The method considers the variation of core loss, rotor bar resistance and leakage inductance due to time harmonics and skin effects. Finally, the efficiency estimations are compared to the IEC/TS 60034-2-3 in the case of a balanced non-sinusoidal supply condition. This allows not only the efficiency comparison but also the loss segregation analysis on the various components of the motor losses. In the case of an unbalanced supply, the efficiency results are compared to measured values obtained based on the direct input-output method. In both the first and second methods, a robust Chicken Swarm Optimization (CSO) algorithm has been used for the first time in conjunction with a simplified inverse Đ“-model EC to correctly determine the induction motor parameters and hence its losses and efficiency while inservice. As Variable Frequency Drives (VFDs) continue to dominate industrial process control, there is a need for stakeholders to quantify the converter-fed motor losses over a wide range of operating frequency and loading conditions. Although there is an increase in legislative activities, particularly in Europe, towards the classification and improvement of energy efficiency in electric drive systems, the handful of available standards for quantifying the harmonic losses are still undergoing validation. One of such standards is the IEC/TS 60034-2-3, which has been lauded as a step in the right direction. However, its limitation to rated motor frequency has been identified as one of its main weaknesses. Therefore, the third method proposed in this research demonstrates how the IEC/TS 60034-2-3 loss segregation methodology at nominal frequency can be extended over the constant-torque region of an induction motor (IM). The methodology has been validated by testing two motors using a 2-level voltage source inverter (VSI) in an open-loop V/F control mode. The results provide good feedback to the relevant IEC standards committee as well as guidance to stakeholders
Parameter estimation for VSI-Fed PMSM based on a dynamic PSO with learning strategies
© 1986-2012 IEEE.A dynamic particle swarm optimization with learning strategy (DPSO-LS) is proposed for key parameter estimation for permanent magnet synchronous machines (PMSMs), where the voltage-source inverter (VSI) nonlinearities are taken into account in the parameter estimation model and can be estimated simultaneously with other machine parameters. In the DPSO-LS algorithm, a novel movement modification equation with variable exploration vector is designed to effectively update particles, enabling swarms to cover large areas of search space with large probability and thus the global search ability is enhanced. Moreover, a Gaussian-distribution-based dynamic opposition-based learning strategy is developed to help the pBest jump out local optima. The proposed DPSO-LS can significantly enhance the estimator model accuracy and dynamic performance. Finally, the proposed algorithm is applied to multiple parameter estimation including the VSI nonlinearities of a PMSM. The performance of DPSO-LS is compared with several existing PSO algorithms, and the comparison results show that the proposed parameters estimation method has better performance in tracking the variation of machine parameters effectively and estimating the VSI nonlinearities under different operation conditions
An invertible dependence of the speed and time of the induction machine during no-load direct start-up
Novel expression for time–speed curve of IM during no-load direct start-up
An invertible dependence for speed–time curve of IM during no-load direct start-up
Simulation results
Experimental results
Conclusion
Disclosure statement
References
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AbstractFormulae display:MathJax Logo?
In this paper, an invertible dependence of the speed and time of the induction machine during no-load direct start-up is presented. Namely, based on the parameters of the induction machine equivalent circuit as well as on the basic, well-known, equation for machine torque, the analytical expression for the induction machine time-speed dependence during direct start-up is derived. On the other hand, in order to obtain inverse i.e. speed-time dependence, the derived time-speed expression is rearranged in one nonlinear equation. As the derived nonlinear equation does not have an analytical solution, a novel iterative procedure, based on the usage of Lambert W function, is proposed for its solving. The results obtained by using the developed expressions for speed-time or time-speed curves are compared with the corresponding results obtained by using expressions known in the literature as well as with the results obtained by using a numerical time-domain computation method. Moreover, the results obtained by using the developed expressions have been compared with the corresponding experimental results to demonstrate the accuracy of the derived expressions. The Matlab code developed for solving the presented iterative procedure, as well as the Matlab code for induction machine speed-time curve determination, is also provided
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Qualitative Adaptive Identification for Powertrain Systems. Powertrain Dynamic Modelling and Adaptive Identification Algorithms with Identifiability Analysis for Real-Time Monitoring and Detectability Assessment of Physical and Semi-Physical System Parameters
A complete chain of analysis and synthesis system identification tools for detectability
assessment and adaptive identification of parameters with physical interpretation
that can be found commonly in control-oriented powertrain models is
presented. This research is motivated from the fact that future powertrain control
and monitoring systems will depend increasingly on physically oriented system
models to reduce the complexity of existing control strategies and open the
road to new environmentally friendly technologies. At the outset of this study
a physics-based control-oriented dynamic model of a complete transient engine
testing facility, consisting of a single cylinder engine, an alternating current dynamometer
and a coupling shaft unit, is developed to investigate the functional
relationships of the inputs, outputs and parameters of the system. Having understood
these, algorithms for identifiability analysis and adaptive identification of parameters with physical interpretation are proposed. The efficacy of the recommended
algorithms is illustrated with three novel practical applications. These are,
the development of an on-line health monitoring system for engine dynamometer
coupling shafts based on recursive estimation of shaft’s physical parameters, the
sensitivity analysis and adaptive identification of engine friction parameters, and
the non-linear recursive parameter estimation with parameter estimability analysis
of physical and semi-physical cyclic engine torque model parameters. The
findings of this research suggest that the combination of physics-based control oriented
models with adaptive identification algorithms can lead to the development
of component-based diagnosis and control strategies. Ultimately, this work
contributes in the area of on-line fault diagnosis, fault tolerant and adaptive control
for vehicular systems
Parameter estimation for condition monitoring of PMSM stator winding and rotor permanent magnets
Winding resistance and rotor flux linkage are important to controller design and condition monitoring of a surface-mounted permanent-magnet synchronous machine (PMSM) system. In this paper, an online method for simultaneously estimating the winding resistance and rotor flux linkage of a PMSM is proposed, which is suitable for application under constant load torque. It is based on a proposed full-rank reference/variable model. Under constant load torque, a short pulse of id 0 is transiently injected into the d-axis current, and two sets of machine rotor speeds, currents, and voltages corresponding to id = 0 and id 0 are then measured for estimation. Since the torque is kept almost constant during the transient injection, owing to the moment of system inertia and negligible reluctance torque, the variation of rotor flux linkage due to injected id 0 can be taken into account by using the equation of constant torque without measuring the load torque and is then associated with the two sets of machine equations for simultaneously estimating the winding resistance and rotor flux linkage. Furthermore, the proposed method does not need the values of the -axis inductances, while the influence from the nonideal voltage measurement, which will cause an ill-conditioned problem in the estimation, has been taken into account and solved by error analysis. This method is finally verified on two prototype PMSMs and shows good performance. © 1982-2012 IEEE
Global identification of electrical and mechanical parameters in PMSM drive based on dynamic self-learning PSO
A global parameter estimation method for a PMSM drive system is proposed, where the electrical parameters, mechanical parameters and voltage-source-inverter (VSI) nonlinearity are regarded as a whole and parameter estimation is formulated as a single parameter optimization model. A dynamic learning estimator is proposed for tracking the electrical parameters, mechanical parameters and VSI of PMSM drive by using dynamic self learning particle swarm optimization (DSLPSO). In DSLPSO, a novel movement modification equation with dynamic exemplar learning strategy is designed to ensure its diversity and achieve a reasonable tradeoff between the exploitation and exploration during the search process. Moreover, a nonlinear multi-scale based interactive learning operator is introduced for accelerating the convergence speed of the Pbest particles; meanwhile a dynamic opposition-based learning (OBL) strategy is designed to facilitate the gBest particle to explore a potentially better region. The proposed algorithm is applied to parameter estimation for a PMSM drive system. The results show that the proposed method has better performance in tracking the variation of electrical parameters, and estimating the immeasurable mechanical parameters and the VSI disturbance voltage simultaneously
Nonintrusive Method for Induction Motor Equivalent Circuit Parameter Estimation using Chicken Swarm Optimization (CSO) Algorithm
This paper presents a nonintrusive method for estimating the parameters of an Induction Motor (IM) without the need for the conventional no-load and locked rotor tests. The method is based on a relatively new swarm-based algorithm called the Chicken Swarm Optimization (CSO). Two different equivalent circuits implementations have been considered for the parameter estimation scheme (one with parallel and the other with series magnetization circuit). The proposed parameter estimation method was validated experimentally on a standard 7.5 kW induction motor and the results were compared to those obtained using the IEEE Std. 112 reduced voltage impedance test method 3. The proposed CSO optimization method gave accurate estimates of the IM equivalent circuit parameters with maximum absolute errors of 5.4618% and 0.9285% for the parallel and series equivalent circuits representations respectively when compared to the IEEE Std. 112 results. However, standard deviation results in terms of the magnetization branch parameters, suggest that the series equivalent circuit model gives more repeatable results when compared to the parallel equivalent circuit.
Keywords: Induction motor, Chicken Swarm Optimization, parameter estimation, equivalent circuit, objective functio
Modeling induction machine winding faults for diagnosis
International audienceMonitoring and diagnosis of electrical machine faults is a scientific and economic issue which is motivated by objectives for reliability and serviceability in electrical drives. This book provides a survey of the techniques used to detect the faults occurring in electrical drives: electrical, thermal and mechanical faults of the electrical machine, faults of the static converter and faults of the energy storage unit. Diagnosis of faults occurring in electrical drives is an essential part of a global monitoring system used to improve reliability and serviceability. This diagnosis is performed with a large variety of techniques: parameter estimation, state observation, Kalman filtering, spectral analysis, neural networks, fuzzy logic, artificial intelligence, etc. Particular emphasis in this book is put on the modeling of the electrical machine in faulty situations. Electrical Machines Diagnosis presents original results obtained mainly by French researchers in different domains. It will be useful as a guideline for the conception of more robust electrical machines and indeed for engineers who have to monitor and maintain electrical drives. As the monitoring and diagnosis of electrical machines is still an open domain, this book will also be very useful to researchers
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