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

Numerical estimation and experimental verification of optimal parameter identification based on modern optimization of a three phase induction motor

The parameters of electric machines play a substantial role in the control system which, in turn, has a great impact on machine performance. In this paper, a proposed optimal estimation method for the electrical parameters of induction motors is presented. The proposed method uses the particle swarm optimization (PSO) technique. Further, it also considers the influence of temperature on the stator resistance. A complete experimental setup was constructed to validate the proposed method. The estimated electrical parameters of a 3.8-hp induction motor are compared with the measured values. A heat run test was performed to compare the effect of temperature on the stator resistance based on the proposed estimation method and the experimental measurements at the same conditions. It is shown that acceptable accuracy between the simulated results and the experimental measurements has been achieved

Online estimation of equivalent model for cluster of induction generators: a MVMO-based approach

This paper presents an approach based on the hybrid variant of the mean-variance mapping optimization algorithm (MVMO-SH) for the estimation of an Equivalent Model for a cluster of induction generators (IGs) from the on-line system response to a system frequency disturbance. Numerical results, obtained by using a small-size test system, demonstrate the viewpoint and effectiveness of the proposed approach

Alienor method applied to induction machine parameters identification

This paper presents an identification method to estimate simultaneously the electrical and mechanical induction machine (IM) parameters by using only the measured current and the corresponding phase voltage. This identification method is based on the output error and uses the multidimensional Alienor global optimization method as a minimization technique. Alienor method is essentially based on converting multivariable problem to monovariable one. To improve the Alienor method performance, the reducing transformation is proposed and compared with the genetic algorithm (GA). Firstly, the identification method is verified using the simulated data. Secondly, the validation is then confirmed by measured data from one machine. The corresponding computed transient and steady state currents agree well with the measured data. The results obtained show the superiority of the proposed Alienor method versus GA in terms of computing time

Novel measurement based load modeling and demand side control methods for fault induced delayed voltage recovery mitigation

The continuous increase in electric energy demand and limitations in the reinforcement of generation and transmission systems, have progressively led to a greater utilization of power systems and transmission lines. As a result, system conditions may arise where voltage collapse phenomena have a high probability to occur, either due to the accidents in the system structure, or to load becoming particularly heavy. Recently, Workshop on Residential Air Conditioner (A/C) Stalling of Department of Energy (DOE) reported that fault-induced delayed voltage recovery (FIDVR) is now a national issue since residential A/C penetration across U.S. is at an all time high and growing rapidly. The unique characteristics of air conditioner load could cause short-term voltage instability, fast voltage collapse, and delayed voltage recovery. In order to study and mitigate FIDVR problem, a systematic load modeling methodology utilizing novel parameter identification technique and an online demand side control scheme based on load shedding strategy are developed in this dissertation. As load characteristics change from traditional incandescent light bulbs to power electronics-based loads, and as the characteristics of motors change with the emergence of high-efficiency, low-inertia motor loads, it is critical to understand and model load responses to ensure stable operations of the power system during different contingencies. Developing better load models, therefore, has been an important issue for power system analysis and control. It is necessary to take advantage of the state-of-the-art techniques for load modeling and develop a systematic approach to establish accurate, aggregate load models for bulk power system stability studies. In this dissertation, a systematic methodology is provided to derive aggregate load models at the high voltage level (transmission system level) using measurement-based approach. A novel parameter identification technique via hybrid learning is also developed for deriving load model parameters accurately and efficiently. According to NERC\u27s definition, FIDVR is defined as the phenomenon whereby system voltage remains at significantly reduced levels for several seconds after a fault in transmission, subtransmission, or distribution has been cleared. Various studies have shown that FIDVR usually occurs in the areas dominated by induction motors with constant torque. These motors can stall in response to sustained low voltage and draw excessive reactive power from the power grid. Since no under voltage or stall protection is equipped with A/Cs, they can only be tripped by thermal protection which takes 3 to 20 seconds. Severe FIDVR event could lead to fast voltage collapse. In this dissertation, a novel online demand side control method utilizing motor kinetic energy is developed for disconnecting stalling motors at the transmission level to mitigate FIDVR and fast voltage collapse

Parameter Identification And Fault Detection For Reliable Control Of Permanent Magnet Motors

The objective of this dissertation is to develop new fault detection, identification, estimation and control algorithms that will be used to detect winding stator fault, identify the motor parameters and optimally control machine during faulty condition. Quality or proposed algorithms for Fault detection, parameter identification and control under faulty condition will validated through analytical study (Cramer-Rao bound) and simulation. Simulation will be performed for three most applied control schemes: Proportional-Integral-Derivative (PID), Direct Torque Control (DTC) and Field Oriented Control (FOC) for Permanent Magnet Machines. New detection schemes forfault detection, isolation and machine parameter identification are presented and analyzed. Different control schemes as PID, DTC, FOC for Control of PM machines have different control loops and therefore it is probable that fault detection and isolation will have different sensitivity. It is very probable that one of these control schemes (PID, DTC or FOC) are more convenient for fault detection and isolation which this dissertation will analyze through computer simulation and, if laboratory condition exist, also running a physical models