Yukai Wang

Loss Manipulation Capabilities of Deadbeat Direct Torque and Flux Control Induction Machine Drives

This paper investigates the loss manipulation capabilities of deadbeat direct torque and flux control (DB-DTFC) induction machine drives. For each switching period, a volt-second-based inverse model provides a range of volt-second solutions to achieve the desired torque at the end of each switching interval, whereas the stator flux magnitude provides another degree of freedom to manipulate machine losses. With a flux-based DB-DTFC loss model proposed, losses can be manipulated each switching period without compromising torque dynamics. For a steady-state operation and typical process trajectories, the minimum loss can be achieved with corresponding energy savings. Alternatively, significant losses can be rapidly induced to dissipate kinetic energy in a machine during braking transients. This loss maximization technique provides significant braking torque without requiring additional hardware to transfer/dissipate energy. DB-DTFC induction machine drives provide an effective and elegant solution for continuous loss manipulation without compromising dynamic performance.

A Low-Switching-Frequency Flux Observer and Torque Model of Deadbeat–Direct Torque and Flux Control on Induction Machine Drives

This paper presents a digital implementation of deadbeat-direct torque and flux control (DB-DTFC) for induction machines at low switching frequencies (SFs). For high SFs, existing discrete-time flux observers and Volt-second-based inverse torque models used for DB-DTFC achieve acceptable flux estimation accuracy and fast torque response. However, the flux estimate is less accurate when the SF is reduced and the DB-DTFC performance degrades. This paper develops a more suitable flux observer and Volt-second-based inverse torque model that minimizes flux estimation error and improves torque control for DB-DTFC. Simulation and experimental results are provided to evaluate the performance of the proposed observer and torque model at very low SFs. Consequently, digital implementation of low-SF DB-DTFC on high-power induction machines is feasible.

Real-Time Parameter Identification and Integration on Deadbeat-Direct Torque and Flux Control (DB-DTFC) Without Inducing Additional Torque Ripple

This paper is dedicated to exploring real-time parameter identification approaches and their integration with deadbeat-direct torque and flux control (DB-DTFC) without additional torque ripple. By using the voltage model, at medium and high speed ranges DB-DTFC is insensitive to parameters. Performance degrades at low speeds with the rotor time constant errors because the current model is utilized for flux linkage estimation. Two different real-time parameter estimation approaches are developed, including a flux observer-based model reference adaptive system (MRAS) and a pulsating flux injection-based method. Both are experimentally evaluated for how well they mitigate DB-DTFC performance degradation at low speed. For the MRAS-based method, the fundamental components are used for parameter convergence, which does not induce additional torque ripple. For the injection-based method, traditional d-axis pulsating voltage vector injection used in IFOC yields significant torque ripple, particularly at high speed. The proposed pulsating flux injection scheme used in DB-DTFC excites the same magnitude of current harmonics and induces no additional torque ripple over a wide speed range.

Implementation issues and performance evaluation of deadbeat-direct torque and flux control drives

Despite many advances in deadbeat, direct torque and flux control (DB-DTFC) drives, practical implementation and evaluation challenges remain. Unlike field oriented control drives with current vector inner loops embedded, DB-DTFC drives utilize flux linkage estimates and inverse torque models to directly calculate Volt.-sec vectors, which yield significant difference in implementation and evaluation processes. Moreover, deadbeat controllers can only be formulated in the discrete time domain, which requires estimating states at the next sample instant and a clear events timing sequence between each sample instant. This paper is dedicated to presenting detailed implementation and evaluation approaches for DB-DTFC drives, with particular foci on processor events timing, discrete time current/flux observers tuning and evaluation, closed-loop DB-DTFC control law implementation, and feasible Volt-sec ranges to achieve deadbeat responses. Based on experimental results on induction machines and permanent magnet synchronous machines, this paper contributes to a systematic procedure for implementation of DB-DTFC.

Loss manipulation capabilities of deadbeat-direct torque and flux control induction machine drives

This paper investigates the loss manipulation capabilities of deadbeat-direct torque and flux control (DB-DTFC) induction machine drives. For each switching period, a Volt.-sec.-based inverse model provides a range of Volt.-sec. solutions to achieve the desired torque at the end of each switching interval, while stator flux magnitude provides another degree-of-freedom to manipulate machine losses. With a flux-based DB-DTFC loss model, losses can be manipulated each switching period without compromising torque dynamics. For steady-state operation and typical process trajectories, the minimum loss can be achieved with corresponding energy savings. Alternatively, significant losses can be induced rapidly to dissipate kinetic energy in the machine during braking transients. This provides smooth braking torque and decreases deceleration time without requiring additional energy dissipation or storage hardware. DB-DTFC induction machine drives provide an effective and elegant solution for loss manipulation.

Real-time parameter identification and integration on deadbeat-direct torque and flux control (DB-DTFC) without inducing additional torque ripple

This paper is dedicated to exploring real-time parameter identification approaches and their integration with deadbeat-direct torque and flux control (DB-DTFC) without additional torque ripple. Using the voltage model at medium- and high-speed ranges, DB-DTFC is insensitive to parameters. Performance degrades at low speeds with the rotor time constant errors, because the current model is utilized for flux linkage estimation. Two different real-time parameter estimation approaches are developed, including a flux observer-based model reference adaptive system (MRAS) and a pulsating flux injection-based method. Both are experimentally evaluated for how well they mitigate DB-DTFC performance degradation at low speed. For the MRAS-based method, the fundamental components are used for parameter convergence, which does not induce additional torque ripple. For the injection-based method, traditional d-axis pulsating voltage vector injection used in indirect field-oriented control (IFOC) yields significant torque ripple, particularly at high speed. The proposed pulsating flux injection scheme used in DB-DTFC excites the same magnitude of current harmonics and induces no additional torque ripple over a wide speed range.

Deadbeat-direct torque and flux control on low switching frequency induction machine drives using the enhanced flux observer and torque model

This paper presents a digital implementation of deadbeat-direct torque and flux control (DB-DTFC) for induction machines at low switching frequencies. For high switching frequencies, existing discrete time flux observers and Volt-sec.-based inverse torque models used for DB-DTFC achieve acceptable flux estimation accuracy and fast torque response. However, the flux estimate is less accurate when switching frequency is reduced and DB-DTFC performance degrades. This paper develops a more suitable flux observer and Volt-sec.-based inverse torque model that minimizes flux estimation error and improves torque control for DB-DTFC. Simulation and experimental results are provided to evaluate the performance of the proposed observer and torque model at very low switching frequencies. Consequently, digital implementation of low switching frequency DB-DTFC on high power induction machines is feasible.

Using Volt-Second Sensing to Directly Improve Torque Accuracy and Self-Sensing at Low Speeds

As a result of dead-time, device on-state voltage drop, dc bus voltage measurement error, etc., volt-second errors degrade precise control of torque and flux linkage, particularly at low speeds. This is true for deadbeat-direct torque and flux control, which directly manipulates the volt-second vector sourced by inverters, as well as for indirect field oriented control drives. This paper introduces a real-time sensing scheme to measure the motor terminal volt-second vectors for each switching period with negligible phase lag. Based on the volt-second sensing, a model reference adaptive system-based approach is developed to decouple the volt-second errors from inverter nonlinearity, and dc bus voltage fluctuation and measurement error. By delivering an accurate volt-second vector for each switching period, torque and flux control accuracy, self-sensing performance, and parameter estimation accuracy are significantly enhanced.

Using Volt-sec. sensing to directly improve torque accuracy and self-sensing at low speeds

As a result of dead-time, device on-state voltage drop, DC bus voltage error and fluctuation, etc., Volt-sec. errors degrade precise control of torque and flux linkage, particularly at low speeds. This is true for deadbeat-direct torque and flux control (DB-DTFC) which directly manipulates the Volt-sec. vector sourced by inverters as well as for indirect field oriented control (IFOC) drives. This paper introduces a real-time sensing scheme to measure the terminal Volt-sec. vector for each switching period with negligible phase delay. Based on Volt-sec. sensing, a model reference adaptive system (MRAS)-based approach is developed to decouple Volt-sec. errors from inverter nonlinearity and DC bus voltage. By delivering accurate Volt-sec. for each switching period, torque and flux control accuracy, self-sensing performance and parameter estimation accuracy are significantly enhanced.

Using Volt-sec. sensing to extend the low speed range and the disturbance rejection capability of back-EMF-based self-sensing

This paper introduces usage of Volt-sec. sensing in back-EMF-based self-sensing (sensorless) induction machine drives. In practice, both inverter nonlinearity and dc bus voltage affect back-EMF estimation accuracy, and therefore self-sensing performance. A real-time Volt-sec. sensing scheme to measure the terminal Volt-sec. vector for each switching period is introduced in the paper. The measured Volt-sec. information can be used in the back-EMF state filter such that the effects from inverter nonlinearity and dc bus voltage measurement errors are mitigated. The resulting extended low speed range and enhanced disturbance rejection capability are quantified via experimental evaluation.