Abstract
This thesis is concerned with the modelling of electrical machines for use in variable-speed drives. Even when the structure of the model of the machine is known values have to be assigned to the parameters. In addition, it is usual for only some of the state variables to be measured, any others needed being estimated using the model. The present work is a study of methods of making on-line estimates of the model parameters, using a reduced number of measured states. To offer high level dynamic torque control the non-measured state variables must be indirectly estimated to a high degree of accuracy throughout the complete range of operating conditions. The state estimator is generally classified with respect to the degree of structural complexity. At one end of the spectrum the model is constructed with a very high level of complexity in order to describe fully the system during any operating conditions. Because of this structural accuracy, the model parameters can be fixed prior to running the machine under normal conditions. However, this scheme suffers from a high computational burden in the state estimation process, and requires sophisticated commissioning strategies in order to permit the complete identification of the relatively large parameter set. The alternative is to use a simpler model structure and update the parameters with sufficient speed on-line in order to compensate for the inherently larger structural error. In this thesis the latter method is considered and preferred, as it has a greater robustness to unforeseen system behaviour and is more compatible with existing control strategies. As a consequence of the simple estimator the identification scheme has to compensate for the considerable structural errors. To this end the strategy of full parameter set identification is described. Work has also been done, and is presented, concerning on-line parameter identification using genetic optimisation techniques, which are shown to be well suited to this type of problem. The first studies of modelling and parameter extraction were concerned with dc machines, for these were thought to be simpler to model and to understand. DC machines are also different, in that the simple model includes mechanical parameters, and so represents a more complete system than the models of the ac machine studied later. A number of standard, enhanced and novel parameter identification methods are analysed and implemented on a practical machine and drive test bench. Also included were state estimators, intended to permit speed-sensorless control; however, the limitations on the experimental rig, based as it was around a commercially available drive, meant that some of the testing had to be done by running the estimator off-line, using data recorded from actual runs as the input. The thesis is primarily concerned with the induction machine and specifically the parameters required to permit field and speed sensorless rotor field orientated vector control. Sufficient work was done to allow a preliminary experimental comparison of a number of algorithms. At this stage it appears that several of these could be developed into successful drives, the precise choice depending on the specific application.