Sliding mode control rotor flux MRAS based speed sensorless induction motor traction drive control for electric vehicles

Climate change has highlighted a need to transition to more sustainable forms of transportation. Electric vehicles (EVs) and hybrid electric vehicles (HEVs) offer a promising alternative to conventional gasoline powered vehicles. However, advancements in power electronics and advanced control systems have made the implementation of high performance traction drives for EVs and HEVs easy. In this paper, a novel sliding mode control model reference adaptive system (SMC-MRAS) speed estimator in traction drive control application is presented. However, due to the unpredictable operational uncertainties of the machine parameters and unmodelled non-linear dynamics, the proportional-integral (PI)-MRAS may not produce a satisfactory performance. The Proposed estimator eliminates the PI controller employed in the conventional MRAS. This method utilizes two loops and generates two different error signals from the rotor flux and motor torques. The stability and dynamics of the SMC law are obtained through the Lyapunov theory. The potential of the proposed SMC-MRAS methodology is simulated and experimentally validated for an electric vehicle application. Matlab-Simulink environment is developed and proposed scheme is employed on indirect vector control method. However, for the experimental validation, the dSPACE 4011 R & D controller board was utilized. Furthermore, the SMC-MRAS performance is differentiated with PI-MRAS for speed regulation performance, tracking and estimation error, as well as the fast minimization of the error signal. The results of the proposed scheme illustrate the enhanced speed estimation, load disturbance rejection ability and fast error dynamics

Smart control architecture for microgrid application

This research proposes non-linear control architectures dedicated towards improving transient response, reliability and computational burden for grid connected inverters applicable for ac micro-grids. Also this work proposes an optimization procedure applied to a small microgrid to reduce the billing cost for power incorporating battery degradation mechanism. Three works are discussed in this research that discusses methodologies to improve the operation of a three phase grid connected inverters. The first work discusses a globally stable estimation architecture for estimating the plant parameters for a grid connected inverter during its operation. Then a Lyapunov based control architecture is utilized and online parameter update scheme is used to optimize the controller performance. The second work discusses a Lyapunov based control architecture during a contingency that the grid voltage sensor fails. In this work an internal model based grid voltage estimation architecture has been proposed which successfully estimates the grid voltage and controls the grid current. The last work shows a methodology to optimally utilize a battery in a microgrid based on Markov Decision Process. Dynamic algorithm is used to solve the problem so that the cost is minimized at the end of the day. Furthermore, in this research detailed stability analysis of the first two works along with the controller design has been presented. Also in this work, battery degradation is modelled empirically and the overall cost function is obtained for the optimization of billing cost for a small microgrid. Detailed plant modeling, controller design, simulation and experimental results are presented for all of the proposed schemes --Abstract, page iv

Des nouvelles approches de commande et d’estimation non linéaires robustes dédiées aux entraînements électriques

The purpose of the research presented in this thesis is to propose a methodology for the control and observation of the induction motor (IM) based on the algorithms using the mean value theorem (MVT) and the transformation by sector non-linearity approach. In the first step, the different control techniques of electric drives were identified and analyzed. A robust state and estimation feedback control approach is then developed with variable parameters. In the field of low power, the removal of the mechanical speed sensor can be of economic interest and improve operational safety. We have presented two categories of methods that allow reconstructing and controlling the rotor speed with desired quantities under field-oriented control of the IM’s machine, the MVT observer and the robust MVT controller respectively. All the solutions have been validated by numerical simulation and affirmed by experimental tests to compare the accuracy and dynamics characteristics of the different methods with the MVT control. Finally, new robust control and estimation approaches with a novel representation for uncertain systems with varying parameters based on the MVT and sector nonlinear addressed to control the IM ‘s machine with FOC control. The results of the various simulation tests and the different experimental trials put into evidence the robustness and the success properties of the proposed algorithms. The thesis ends with a review of our contribution in terms of research

Non-intrusive efficiency estimation of inverter-fed induction machines

Motorised loads using induction machines use approximately 60% of the electricity globally. Most of these systems use three-phase induction motors due to their robustness and lower cost. They are often installed in continuously operating industrial plants/applications that require no operational interruptions. Whilst most of these induction machines are supplied from ideally sinusoidal supplies, applications are emerging where induction machines are fed from non-sinusoidal supplies. In particular, pulse width modulated inverters realize efficient control of induction machines in many automated industrial applications. From an energy management perspective, it is vital to continually assess the efficiency of induction machines in order to initiate replacement or economic repair. It is therefore of paramount importance that reliable and non-intrusive techniques for efficiency estimation of induction machines be investigated, that consider sinusoidal and non-sinusoidal supplies. This work proposes a non-intrusive efficiency estimation technique for inverter–fed induction motors that is based on harmonic regression analysis, harmonic equivalent circuit parameter estimation and harmonic loss analysis using limited measured data. Firstly, considerations for inverter-fed induction motor equivalent circuit modelling and parameter estimation techniques suitable for non-intrusive efficiency estimation are presented and the selection of one equivalent circuit for analysis is justified. Measured data is obtained from two different induction motors on a flexible 110kW test rig that utilises an HBM Gen 7i data acquisition system. By measuring voltage, current and input power at the supply terminals of the inverter-fed motor, the fundamental equivalent circuit parameters are estimated using population based incremental learning algorithm and compared with those obtained from the IEC 60034-2-1 Standard. The harmonic parameters are estimated using the bacterial foraging algorithm basing on the input impedance of the motor at each harmonic order. A finite harmonic loss analysis is carried out on the tested induction motors. The proposed techniques and harmonic loss analysis provide accurate efficiency estimates of within 1.5% error when compared to the direct method. Lastly, a related non-intrusive efficiency estimation technique is proposed that caters for a holistic loss contribution by all harmonics. The efficiency results from the proposed techniques are compared to those obtained from the IEC-TS 60034-2-3 Technical Specification and a direct method. The estimated efficiencies are comparable to those measured by the Technical Specification and a direct method within 2% error when tested on 37kW and 45kW PWM inverter-fed motors across the loading range. Furthermore, this work conducts a comprehensive non-intrusive rotor speed estimation comparative analysis in order to recommend the best technique(s), in terms of intrusiveness, accuracy and computational overhead. Errors of less than 1% have been reported in literature and experimental verification when using vibration analysis, Motor Current Signature Analysis (MCSA), Rotor Slot Harmonic (RSH) and Rotor Eccentricity Harmonic (REH) analysis techniques in inverter-fed IMs

Sensorless control of surface mounted permanent magnet machine using fundamental PWM excitation

This thesis describes the development of a sensorless control method for a surface mounted permanent magnet synchronous machine drive system. The saturation saliency in the machine is tracked from the stator current transient response to the fundamental space vector PWM (pulse width modulation) excitation. The rotor position and speed signals are obtained from measurements of the stator current derivative during the voltage vectors contained in the normal fundamental PWM sequence. In principle, this scheme can work over a wide speed range. However, the accuracy of the current derivative-measurements made during narrow voltage vectors reduces. This is because high frequency current oscillations exist after each vector switching instant, and these take a finite time to die down. Therefore, in this thesis, vector extension and compensation schemes are proposed which ensure correct current derivative measurements are made, even during narrow voltage vectors, so that any induced additional current distortion is kept to a minimum. The causes of the high frequency switching oscillations in the AC drive system are investigated and several approaches are developed to reduce the impact of these oscillations. These include the development of a novel modification to the IGBT gate drive circuit to reduce the requirement for PWM vector extension. Further improvements are made by modifications to the current derivative sensor design together with their associated signal processing circuits. In order to eliminate other harmonic disturbances and the high frequency noise appearing in the estimated position signals, an adaptive disturbance identifier and a tracking observer are incorporated to improve the position and speed signals. Experimental results show that the final sensorless control system can achieve excellent speed and position control performance

Advanced control architectures for grid connected and standalone converter systems

This dissertation proposes new control algorithms dedicated towards improving the reliability, computational burden and stability in grid-connected and stand-alone based power electronic converter systems applicable for ac microgrids. Two voltage sensorless control architectures, one for stand-alone applications and the other for grid-connected application are established in this thesis. The output voltage of a standalone single-phase inverter is controlled directly by controlling the output filter capacitor current without using a dedicated output voltage sensor. A method to estimate the output filter capacitance is also presented. For the grid connected converter, a novel closed loop estimation is presented to estimate the grid voltage. In addition to the estimation of the grid voltage, the proposed method also generates the unit vectors and frequency information similar to a conventional phase-locked loop structure. The voltage sensorless algorithm is then extended to LCL filter based grid connected converters thereby proposing a new indirect method of controlling the grid current. Furthermore, addressing the stability issues in current-controlled grid tied converters, this dissertation also analyzes the power angle synchronization control of grid-tied bidirectional converters for low voltage grids. The power flow equations for the low voltage grid are analyzed and compensators are designed to ensure the decoupled control of active and reactive power. It is demonstrated that the proposed compensators are immune to grid fluctuations and ensure stable operation controlling the desired power flow to and from the grid. Detailed plant modeling, controller design, simulation and experimental results are presented for all of the proposed schemes --Abstract, page iv

A Precise, General, Non-Invasive and Automatic Speed Estimation Method for MCSA Diagnosis and Efficiency Estimation of Induction Motors

[EN] Efficiency estimation and diagnosis via MCSA require precise knowledge of speed. In an industrial environment, speed must be obtained with a non-invasive, automatic and general method. Recent studies have shown that Sensorless Speed Estimation techniques based on detecting Rotational Frequency Sideband Harmonics (RFSHs) or Rotor Slot Harmonics (RSHs) are best suited to these purposes. RFSHs-based methods are easier to apply as they only depend on the number of poles. RSHs-based are much more accurate due to their wider bandwidth. Yet, their use is not trivial as they require to identify the RSHs family, assign to each RSH its order of the current harmonic (¿) and determine the number of rotor slots (R), a rarely known parameter. This paper ends with this trade-off between accuracy and applicability by proposing a novel RSHs-based technique that, for the first time in technical literature, eliminates the need to estimate the number of rotor slots and provides a reliable and automatic procedure to locate the RSHs family and determine their ¿ indices. Finally, the method is validated under all types of conditions and motor designs, by simulations, lab tests and with 105 industrial motors, highlighting its high accuracy (errors below 0.05 rpm), and applicability.This work was supported by the Universitat Politecnica de Valencia and the Spanish Ministry of Science, Innovation and Universities [FPU19/02698]Bonet-Jara, J.; Pons Llinares, J. (2023). A Precise, General, Non-Invasive and Automatic Speed Estimation Method for MCSA Diagnosis and Efficiency Estimation of Induction Motors. IEEE Transactions on Energy Conversion. 38(2):1257-1267. https://doi.org/10.1109/TEC.2022.32208531257126738

Performance of Induction Machines

Induction machines are one of the most important technical applications for both the industrial world and private use. Since their invention (achievements of Galileo Ferraris, Nikola Tesla, and Michal Doliwo-Dobrowolski), they have been widely used in different electrical drives and as generators, thanks to their features such as reliability, durability, low price, high efficiency, and resistance to failure. The methods for designing and using induction machines are similar to the methods used in other electric machines but have their own specificity. Many issues discussed here are based on the fundamental achievements of authors such as Nasar, Boldea, Yamamura, Tegopoulos, and Kriezis, who laid the foundations for the development of induction machines, which are still relevant today. The control algorithms are based on the achievements of Blaschke (field vector-oriented control) and Depenbrock or Takahashi (direct torque control), who created standards for the control of induction machines. Today’s induction machines must meet very stringent requirements of reliability, high efficiency, and performance. Thanks to the application of highly efficient numerical algorithms, it is possible to design induction machines faster and at a lower cost. At the same time, progress in materials science and technology enables the development of new machine topologies. The main objective of this book is to contribute to the development of induction machines in all areas of their applications

Advances in Rotating Electric Machines

It is difficult to imagine a modern society without rotating electric machines. Their use has been increasing not only in the traditional fields of application but also in more contemporary fields, including renewable energy conversion systems, electric aircraft, aerospace, electric vehicles, unmanned propulsion systems, robotics, etc. This has contributed to advances in the materials, design methodologies, modeling tools, and manufacturing processes of current electric machines, which are characterized by high compactness, low weight, high power density, high torque density, and high reliability. On the other hand, the growing use of electric machines and drives in more critical applications has pushed forward the research in the area of condition monitoring and fault tolerance, leading to the development of more reliable diagnostic techniques and more fault-tolerant machines. This book presents and disseminates the most recent advances related to the theory, design, modeling, application, control, and condition monitoring of all types of rotating electric machines

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. ii 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