Huynh Van Khang

Eddy-Current Loss and Temperature Rise in the Form-Wound Stator Winding of an Inverter-Fed Cage Induction Motor

Temperature rise of the form-wound multi-conductor stator winding of a 1250-kW cage induction motor was analyzed. Eddy currents in the winding significantly affect the temperature rise. The eddy current was modeled using time-discretized finite-element analysis (FEA). The resistive losses in each of the bars obtained from the FEA are used as input for thermal modeling. The distance from the air gap to the topmost bar in the stator has a significant effect on the eddy-current loss as well as temperature rise in the winding. An acceptable distance for winding design was recommended. By using magnetic slot wedges the eddy-current losses can be reduced.

Parameter Identification of a Synchronous Reluctance Motor by using a Synchronous PI Current Regulator at a Standstill

This paper proposes an estimation algorithm for the electrical parameters of synchronous reluctance motors (SynRMs) by using a synchronous PI current regulator at standstill. In reality, the electrical parameters are only measured or estimated in limited conditions without fully considering the effects of the switching devices, connecting wires, and magnetic saturation. As a result, the acquired electrical parameters are different from the real parameters of the motor drive system. In this paper, the effects of switching devices, connecting wires, and the magnetic saturation are considered by simultaneously using the short pulse and closed loop equations of resistance and synchronous inductances. Therefore, the proposed algorithm can be easily and safely implemented with a reduced measuring time. In addition, it does not need any external or additional measurement equipment, information on the motor’s dimensions, and material characteristics as in the case of FEM. Several experimental results verify the effectiveness of the proposed algorithm.

Loss Minimization for Form-Wound Stator Winding of a High-Speed Induction Motor

Resistive losses in the form-wound stator winding of a high-speed induction motor (HSIM) are minimized in the design stage by using Differential Evolution (DE). Time-discretized finite-element analysis (FEA) serves as a tool to offer electromagnetic losses in the machine for the optimization process. Temperature rise of the machines is fast checked during the optimization by using a thermal network model. Insulation thicknesses and power factor are other constraints in the algorithm. Time consumption of using time-discretized FEA is still a problem when a large number of variables or motor dimensions is required for the optimization algorithm. To reduce the number of required variables, the influence of stator dimensions on the electromagnetic losses is analyzed by using the Taguchi approach. From this evaluation, the important stator parameters to be varied during optimization algorithm are determined. Finally, form-wound stator winding with a minimum loss for a HSIM is obtained within certain limitations.

Eddy-Current Loss Modeling for a Form-Wound Induction Motor Using Circuit Model

Eddy-current losses in a form-wound stator winding of a 1250-kW cage induction motor was modeled using time-discretized finite- element analysis (FEA). A simpler and faster model, typically a circuit model, is needed to include these losses in the loss control algorithms of a frequency converter. The conventional T-equivalent circuit was augmented by an additional branch both in the stator and rotor to model the eddy-current effects. The parameters of the refined circuit were estimated using the voltages and currents from the FEA simulations. The circuit model was verified by comparing the calculated losses and torque with those obtained from the FEA.

Early detection and classification of bearing faults using support vector machine algorithm

Bearings are one of the most critical elements in rotating machinery systems. Bearing faults are the main reason for failures in electrical motors and generators. Therefore, early bearing fault detection is very important to prevent critical system failures in the industry. In this paper, the support vector machine algorithm is used for early detection and classification of bearing faults. Both time and frequency domain features are used for training the support vector machine learning algorithm. The trained classier can be employed for real-time bearing fault detection and classification. By using the proposed method, the bearing faults can be detected at early stages, and the machine operators have time to take preventive action before a large-scale failure. The usefulness of the algorithm is validated by using a run-to-failure experimental test data.

Temperature Rise Estimation of Induction Motor Drives Based on Loadability Curves to Facilitate Design of Electric Powertrains

Thermal protection limits are equally important as mechanical specifications when designing electric drivetrains. However, properties of motor drives like mass/length of copper winding or heat dissipation factor are not available in producers’ catalogs. The lack of this essential data prevents the effective selection of drivetrain components and makes it necessary to consult critical design decisions with equipments suppliers. Therefore, in this paper, the popular loadability curves that are available in catalogs become a basis to formulate a method that allows to estimate temperature rise of motor drives. The current technique allows for evaluating a temperature rise of a motor drive for any overload magnitude, duty cycle, and ambient temperature, contrary to using a discrete set of permissible overload conditions that are provided by manufacturers. The proposed approach is based on industrially adopted practices, greatly improves flexibility of a design process, and facilitates communication in a supplier–customer dialog.

Bearing fault detection based on time-frequency representations of vibration signals

To prevent failures of a rolling bearing in the gearbox drive system, acceleration sensors are used to detect fault-related signals of the bearing. It is a big challenge to observe and identify signals caused by bearing defects in the time domain or the frequency spectrum by a conventional Fourier analysis. The time-frequency representation of the fault-related signals implemented by the windowed Fourier transform is studied in this work. It is shown that the fault characteristic frequencies can be clearly identified in the time-frequency spectrum if a fault occurs in the bearing of the gearbox at different speeds. Otherwise, the shaft frequency and its multiples are the main harmonics in the spectrum.

Form-wound stator winding for high-speed induction motors

Random-wound stator windings of high-speed induction motors (HSIM) have serious problems with eddy-current losses because their circular copper wires are randomly distributed. As a result, the differences in the total electromotive forces induced in the parallel conductors cause high additional losses in random-wound windings. Moreover, the eddy-current losses are underestimated during design stage due to difficulties of modeling eddy-current effect in the random-wound windings. A form-wound stator winding is an alternative option to reduce those losses. A roughly designed model of form-wound winding is presented based on the original random-wound stator windings of HSIM. Time-discretized finite-element analysis (FEA) is used to model losses in a 300 kW HSIM with random and form-wound stator windings. Those losses are used as the input data for the temperature rise analysis to check temperature rise constraints of the designed machine. Possibilities using form-wound windings for HSIM are evaluated from the losses and temperature rise analysis.

Resistive loss identification of an inverter-fed deep-bar induction motor

This paper deals with the resistive losses in the deep-bar induction motor fed by a PWM inverter. The effect of harmonics from the inverter on the resistive losses of the induction motor is investigated by varying the amplitude or phase of the harmonics. The investigation is performed on 45 kW and 1250 kW induction motors. The equivalent circuit of the deep-bar induction motor is presented and used to calculate the resistive losses of motor. The data for estimating the equivalent circuit of the deep-bar induction motor 45 kW is produced by two-dimensional time-stepping finite element analysis (FEA) via an impulse test. The accurate equivalent circuit is suitable for calculating the resistive losses at different frequencies.

Identification of parameters and harmonic losses of a deep-bar induction motor

High frequency harmonics from a frequency converter causes additional losses in a deep-bar induction motor. The harmonics have their own amplitude and phase with respect to the fundamental signal, but the harmonic loss is only dependent on the amplitude of harmonics. A deep-bar induction motor can be modelled by a triple-cage circuit to take skin effect into account. The triple cage circuit having many parameters could be estimated from a small-signal model of the machine by using Differential Evolution. The correctly estimated parameters make the triple-cage circuit valid in a wide range of frequencies. However, the triple-cage circuit is very complicated which makes it difficult to model harmonic losses for motor control. A simplified circuit based on the triple cage circuit is proposed for certain frequency ranges. The identified losses implemented on the simplified circuit are verified via finite element analysis and triple-cage circuit.