The subject of this paper is gains selection of an extended induction machine speed observer. A high number of gains makes manual gains selection difficult and due to nonlinear equations of the observer, well-known methods of gains selection for linear systems cannot be applied. A method based on genetic algorithms has been proposed instead. Such an approach requires multiple fitness function calls; therefore, using a quality index based on simulations makes gains selection a time-consuming process. To find a fitness function that evaluates, in a short time, quality indices based on poles placement have been proposed. As the observer is nonlinear, equations describing the observer dynamics have been linearized. The relationship between poles placement and real dynamic properties has been shown. A series of studies has been performed to investigate the influence of the operating point of the machine on the dynamics of the observer. It has been proven that rotor speed has a significant impact on the placement of the poles and the observer may lose stability after a rotation direction change. A method of gains modification to maintain symmetrical properties of the observer for both directions has been presented. Experimental studies of the observer during machine reverse in the open and closed-loop control system have been performed. The results show that the observer can be implemented in a sensorless drive, using the proposed gains selection method.

This paper presents the theoretical analysis and experimental verification of a direct fault harmonic identification approach in a converter-fed electric drive for automated diagnosis purposes. On the basis of the analytical model of the proposed real-time direct fault diagnosis, the fault-related harmonic component is calculated using recursive DFT (RDFT) and Goertzel DFT (GDFT), applied instead of the full spectrum calculations required in the most popular FFT algorithm. The simulation model of an inverter sensorlessly controlled induction motor drive is linked with the induction machine rotor fault model for testing the sensitivity of the GDFT- and RDFT-based fault diagnosis to state variable estimation errors. According to the presented simulation results, the accuracy of the direct identification of a fault-related harmonic is sensitive to the quality of fault harmonic frequency estimation. The sensitivity analysis with respect to RDFT and GDFT algorithms is included. Based on the experimental setup with a sensorlessly controlled induction motor drive with the investigated rotor fault, fault diagnosis algorithms were implemented in the microprocessor by integration with the control system in one microcontroller and experimentally verified. The RDFT and GDFT approach has shown accurate and fast direct automated fault identification at a significantly decreased number of arithmetical operations in the microcontroller, which is convenient for the frequency-domain fault diagnosis in electric drives and supports fault-tolerant control system implementation.

Properties of state observers depend on proper gains selection. Each method of state estimation may require the implementation of specific techniques of finding those gains. The aim of this study is to propose a universal method of automatic gains selection and perform its verification on an induction machine speed observer. The method utilizes a genetic algorithm with fitness function which is directly based on the impulse response of the observer. System identification using least-squares estimation is implemented to determine the dynamic properties of the observer based on the estimation error signal. The influence of sampling time as well as signal length on the system identification has been studied. The results of gains selection using the proposed method have been compared with results obtained using the approach based on the placement of the poles of linearized estimation error equations. The introduced method delivers results comparable with analytical methods and does not require prior preparation specific to the implemented speed observer, such as linearization.

This paper presents an analysis of a proposed flying start method for sensorless electric drives based on an induction machine. The method introduces two stages. In the first stage, the rotor speed is estimated using the step response of the machine, which enables coarse estimation of the speed and direction of rotation, helping to reduce the initial slip. The second stage introduces an intermediate control system to facilitate the machine restart and assist the speed observer in converging. Simulation and experimental studies demonstrate that the method successfully restarts the machine with an unknown initial rotor speed without exceeding the nominal current. The auxiliary control system fully excites a 5.5 kW machine within 300 ms during the flying start, after which it is possible to transition to the target control system.

Sensorless induction machine drives rely on state estimators whose gains affect stability, dynamic response, and robustness; however, these gains are typically tuned offline by experts using iterative simulations and specialized tools. This paper proposes a fully automated, online gain-selection algorithm executed directly on the drive’s microcontroller using a genetic algorithm. The method utilizes a versatile fitness function based on pole placement of a linearized estimator model. To ensure safe deployment, an on-target verification stage has been employed that validates the candidate gains for stability and adequate dynamic performance across a wide range of rotor speed prior to deployment in the target control system. The approach is generic to observers for which a linearized dynamic representation can be obtained, and its feasibility is demonstrated on two representative cases: a simple flux observer and a higher-order extended speed observer, confirming scalability under resource constraints typical of production microcontrollers. Results indicate that the proposed scheme achieves reliable gain selection on embedded hardware without external computing infrastructure.