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Table of Contents
An Integrated Self-Sensing Approach for Active Magnetic Bearings
µ-Analysis Applied to Self-Sensing Active Magnetic Bearings
Modelling of Broadband Powerline Communication Channels
System Identification of Classic HVDC Systems
An Integrated Self-Sensing Approach for Active Magnetic Bearings by E. O. Ranft, G. van Schoor and C. P. du Rand
Abstract: Self-sensing permits active magnetic bearings (AMBs) to consolidate the actuation and sensing functions into a single electromagnetic transducer. Eliminating the position sensing device as well as interfacing reduce potential system failure points, costs, and complexity. Self-sensing performance at present faces technical challenges such as magnetic cross-coupling, saturation, eddy currents, and system robustness. This work proposes an integrated self-sensing approach to collectively address mechanisms that contribute to modelling errors and position estimation inaccuracy. The self-sensing approach is based on the amplitude modulation technique and comprises a coupled reluctance network model (RNM) that is embedded in a nonlinear multiple input multiple output parameter estimator. The estimator employs a frequency-shifted model that is solved at a lower frequency to increase system performance. Furthermore, the RNM incorporates air gap fringing, complex permeability, and magnetic material nonlinearity terms. Magnetic saturation is accounted for using current scaling weights in the position estimation scheme. Basic functionality of the integrated self-sensing approach is demonstrated using an experimentally verified transient simulation model of the magnetic bearing. Verification and refinement of the RNM is accomplished through an iteration process using finite element method (FEM) results and experimental measurements. The simulation results show that the 40 node RNM can be accurate compared to an 80 000 node FEM analysis. Evaluation of the system stability margin indicates that the robustness of the magnetic bearing control is suitable for unrestricted long-term operation.
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µ-Analysis Applied to Self-Sensing Active Magnetic Bearings by P.A. van Vuuren and G. van Schoor
Abstract: The stability margin of a two degree-of-freedom self-sensing active magnetic bearing (AMB) is estimated by means of µ-analysis. The specific self-sensing algorithm implemented in this study is the direct current measurement method. Detailed black-box models are developed for the main subsystems in the AMB by means of discrete-time system identification. In order to obtain models for dynamic uncertainty in the various subsystems in the AMB, the identified models are combined to form a closed-loop model for the self-sensing AMB. The response of this closed-loop model is then compared to the original AMB’s response and models for the dynamic uncertainty are empirically deduced. Finally, the system’s stability margin for the modelled uncertainty is estimated by means of µ-analysis. The results show that µ-analysis is ill-equipped to estimate the stability margin of a nonlinear system.
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Modelling of Broadband Powerline Communication Channels by C.T. Mulangu, T.J. Afullo, and N.M. Ijumba
Abstract: This paper develops a new PLC model and investigates the impact of the load, line length, and diameter of the transmission line on the channel transfer function over the frequency range of 1-20 MHz. The results show that with 42 degrees of freedom, the proposed model leads to an average RMSE value of approximately 5.2 dB. With the same conditions, the Phillips model leads to an average RMSE value of approximately 1.62 dB.
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System Identification of Classic HVDC Systems by L. Chetty and N.M. Ijumba
Abstract: Determining models from observations and studying the models’ properties is essentially the functionality of science. Models attempt to link observations into some pattern. System identification is the art of building mathematical models of dynamic systems based on observed data from the systems. This paper presents a system identification methodology that can be utilized to derive the classic HVDC plant transfer functions. The model development and verification was performed using the PSCAD/EMTDC software. The calculated results illustrated excellent response matching with the system results. The derived HVDC plant transfer functions can be utilized to perform small signal stability studies of HVDC-HVAC interactions and its use can also be extended to facilitate the analytical design of HVDC control systems.
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