Malgorzata Sumislawska

Dielectric Characteristics of Electric Vehicle Traction Motor Winding Insulation Under Thermal Aging

The electric motor is the heart of the electric vehicle. It is crucial that any occurring faults are detected promptly so that a catastrophic failure is avoided. At the same time, deep knowledge of the degradation mechanisms is required to allow maximum performance at minimum cost. This paper focuses on this balance. Statistical results from measurements of unaged and accelerated aged winding insulation samples provide information about the degradation processes, enabling steps toward a reliable prognosis model of the motors remaining life.

Modelling of an Air Handling Unit: A Hammerstein-bilinear Model Identification Approach

The paper focuses on modelling and system identification of an air handling unit (AHU), which is part of larger heating ventilation and air conditioning (HVAC) system dedicated for clean room manufacturing. The aim is to use the model for subsequent control optimisation and further for control system design. The underlying physical relations of an AHU are investigated through a white-box model. Based on the white-box model considerations a discrete-time bilinear black-box model is proposed. The AHU cooling/heating capacity is altered by means of water valves. The valve introduces a Hammerstein nonlinearity on the system input, which needs to be additionally identified together with the dynamic bilinear model representing the heating/cooling coil of the AHU. The discrete-time model coefficients are then estimated based on real measurements.

The Impact of Thermal Degradation on Properties of Electrical Machine Winding Insulation Material

Interturn stator short circuits can develop quickly leading to serious damage of an electric machine. However, degradation mechanisms of winding insulation material are not yet fully understood. Therefore, the main contribution of this paper is the analysis of the impact of thermal ageing on the electrical properties of the thin-film winding insulation. The insulation samples have been aged thermally at 200 °C-275 °C and for 100-1600 h. After ageing, impedance spectroscopy measurements were undertaken on the samples and equivalent-circuit model (ECM) parameters fitted for each measurement. This allows the impact of thermal ageing on ECM parameters to be analyzed, giving insight into the changes in the electrical properties of the insulation. Finally, high voltage was applied to the samples aiming to identify the breakdown-voltage (BV) characteristics of the insulation material.

Design of Unknown Input Reconstruction Filter Based on Parity Equations for Errors-in-Variables Case

Abstract This paper presents a novel approach to the problem of unknown input estimation in the presence of measurement noise. The observer developed here is based on the parity equations concept, hence, in contrast to the Kalman filter-based approaches, it is orthogonal (independent) to the system state vector. Therefore, due to the reduction of the number of estimated signals, an increased accuracy of the input estimation is achieved. This makes the scheme advantageous in cases when the accuracy of the unknown input estimate is crucial and knowledge about the system states is not required. By increasing the order of the parity space, which is a tuning parameter of the proposed algorithm, the influence of measurement noise can be reduced. An errors-in-variables case is considered, i.e. both measured input and output signals are affected by noise. A Lagrange multiplier method is used to obtain an analytical solution for the filter parameters. The proposed technique is suitable for both minimum-phase and nonminimum-phase systems.

Equivalent circuit model estimation of induction machines under elevated temperature conditions

This paper explores performing identifiability analysis on the equivalent circuit model (ECM) parameters of an electric machine (EM) using its impedance response. When modelling the ECM of an EM for room temperature conditions, some of the ECM parameters can be obtained from the manufacturers data. However, as the temperature of an EM increases this significantly changes the underlying physics (resistivity, capacitance and inductance) of machine parameters, therefore the manufacturers data become inaccurate for equivalent circuit modelling purposes. ECM parameters need to be obtained from the frequency response under the different temperature conditions. To achieve this a nonlinear optimisation scheme with constraints is proposed for the purpose of ECM parameter identification, whereby a temperature-dependent ECM is derived. This work has important applications in EM design and condition monitoring and provides a valuable precursor towards developing age-dependent models.

The impact of thermal degradation on electrical machine winding insulation

Inter-turn stator short circuits can develop quickly leading to serious damage of an electric machine. However, degradation mechanisms of winding insulation are not yet fully understood. The main contribution of this article is analysis of the impact of thermal ageing on the electrical properties of winding insulation. The insulation samples have been aged thermally at 200-275 °C and for 100-1600 hours. After aging, impedance spectroscopy measurements were undertaken on the samples and equivalent circuit model (ECM) parameters fitted for each measurement. This allows the impact of thermal ageing on ECM parameters to be analysed, giving insight into the changes of the electrical properties of the insulation. Ultimately this will lead to development of new methods for remaining life prediction of electrical machine windings.

Investigation of Model Order Reduction Techniques: A Supercapacitor Case Study

This paper presents several different model order reduction techniques to refine an equivalent circuit high order model of a supercapacitor. The presented model order reduction techniques are: truncation based, projection based and system identification based (data based). Upon application of these techniques to the high order model, it has been found that a reduced model with sufficient accuracy can be obtained to act as a surrogate of the real system. This is evident by the ability to reduce a 60 th order supercapacitor model to 4 th order whilst preserving accuracy.

Unknown Input Reconstruction Observer for Hammerstein-Wiener Systems in the Errors-in-Variables Framework

Abstract In this paper an approach for reconstructing an unknown input in the case when input and output signals are both subject to measurement uncertainties, i.e. errors-in-variables framework, is presented. The algorithm is applicable to Hammerstein-Wiener systems, i.e. systems composed from a dynamic linear system followed and preceded by a memoryless nonlinearity. It is based on a parity equations concept and forms an extension of the idea developed previously by the authors for linear systems. The only requirement is that the function used to describe the component static output nonlinearity must be strictly monotonic. The order of the parity space can be treated as a tuning parameter allowing for a trade-off between the smoothness of the reconstructed unknown input and a phase lag to be obtained. An analytical solution of the overall problem is obtained by using a Lagrange multiplier method.

Design of Parity Equations Using Right Eigenstructure Assignment

System uncertainties, such as modelling errors, external disturbances etc. can impede the fault detection process. Therefore a need arises for robust fault diagnosis, where complete disturbance decoupling can be achieved. The main contribution of this paper is the design of a parity relation for robust fault detection using right eigenstructure assignment. Up to date the left eigenstructure assignment technique has been used for the design of first order disturbance decoupled parity relations. In many cases complete disturbance decoupling is not possible when using left eigenstructure assignment. Therefore, the proposed method utilises the right eigenstructure assignment technique for the purpose of robust fault detection. The novelty of the developed scheme is to replace the traditional asymptotically convergent state observer by a state observer, which converges in a predefined time. The proposed method is demonstrated to be equivalent to a parity relation of a user predefined order.

A comparative Study of Model-Based and Data-Based Model Order Reduction Techniques for Nonlinear Systems

In this paper a comparative study of three nonlinear model order reduction techniques using a case study of a transmission line model is presented. The investigated model order reduction techniques are: quadratic approximation, trajectory piecewise linear approximation and data-based identification of bilinear model. The performance of model order reduction techniques has been evaluated in terms of their accuracy and computational cost. The original 100 th order nonlinear model is reduced to 12 th and 20 th order models by using three different MOR techniques yet preserving simulation accuracy.