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This paper analyzes effects of injection frequency selection on performance of position-sensorless algorithms for permanent magnet synchronous motors (PMSMs). The sensitivity with respect to the rotor position of the transfer function between the injected quantity (e.g. voltage) and its response (e.g. current) is explored over a wide frequency range. The paper then presents analytical results that optimize the selection of injection frequency in terms of motor and drive parameters. The frequency selection procedure is based on motor parameters that were obtained experimentally. 

The Power Engineering Group at the University of Puerto Rico-Mayaguez (UPRM) has implemented several strategies to improve its curriculum and meet new ABET accreditation criteria. Strategies include the revision of course contents, integration of laboratory practices to courses, and a more prominent role of undergraduate research and power electronics in the power engineering curriculum. Courses are being updated to include contemporary topics while keeping fundamental engineering principles. A recent grant by NSF will support on-going efforts to create laboratory practices that would connect the teaching of theoretical principles to actual implementations. Participation in the Center for Power Electronics Systems, a NSF Engineering Research Center, has given UPRM the opportunity to expand course offerings and undergraduate research in power engineering. An important outcome of this curriculum improvement is to motivate students to take an active role in the learning process. 

Equation error and output error are common formulations used in speed and parameter estimation for induction machines. This paper presents a study of the quality of the estimated speed and parameters using local sensitivity analysis. We studied parameter conditioning as a function of input signals and estimation methodology at nominal speed. Simulation results are used to show that output error formulation is better conditioned than equation error for speed and parameter estimation using PWM and six-step voltage inputs. 

This paper examines subset selection for nonlinear least squares parameter estimation, and applies the methodology to a test system previously studied in the power system literature, involving the on-line identification of a synchronous generator model with many parameters. Subset selection partitions the parameters into well-conditioned and ill-conditioned subsets. We show for the test system that fixing the ill-conditioned parameters to prior estimates (even if these prior estimates are substantially in error), and estimating only the remaining parameters, significantly improves the performance of the estimation algorithm and greatly enhances the quality of the estimated parameters. It is shown that attempts to estimate all of the model parameters, as done in the original work with this test system, can yield extremely unreliable results.