Feyzullah Erturk

Due to the inherently low saliency of SMPMSMs, achieving stable high-load operation with HFI-based sensorless control at startup or low speeds is challenging. Moreover, even small cross-saturation inductances can cause significant errors in the estimated rotor angle. This paper proposes a physics-informed strategy that adjusts the $d$-axis reference current to ensure a minimum saliency while running FOC with the estimated angle, without explicit angle compensation. Positive $I_{d}$ injection intentionally saturates the motor, reducing inductances and creating sufficient saliency for stable HFI operation. The method defines the minimum $I_{d}$ for maximum torque operation, ensures accurate torque tracking across the full torque range, and provides an initial position detection (IPD) scheme that eliminates the need for a polarity check. Resonant controllers are incorporated to mitigate inverter nonlinearities and spatial harmonic effects, reducing speed and angle ripple. Experiments on a 400 W SMPMSM demonstrate reliable low-speed operation, startup under load, smooth transitions to a B-emf observer, and robust performance under varying torque conditions, validating the effectiveness and practical applicability of the proposed approach, which is also mathematically formulated and rigorously analyzed.

This article presents a seamless dq-axis inductance measurement method for permanent magnet (PM) motor drives at start-up in milliseconds taking magnetic and cross-saturation effects into account. The proposed method deploys a machine model that incorporates these saturation effects and high-frequency injection (HFI) to generate a 3-D/2-D mapping of inductances across all operating regions. Additionally, a unique current sequence injection is introduced for electric vehicles (EVs) and industrial applications where locking the rotor is not feasible. This injection method ensures that the resultant torques keep the rotor stationary throughout the estimation process. Further, the proposed method effectively mitigates the impact of inverter nonlinearity and digital delay on inductance estimation, a topic that is further elaborated upon. Additionally, the proposed offline method is extended to online type with or without the use of position sensor in the application. Experimental validation is conducted on different motors, demonstrating the accuracy of the proposed strategy in estimating the d-q inductances without the need to physically lock the rotor.

This paper presents an online short circuit current amplitude estimation solution for permanent magnet synchronous motors (PMSM) with Inter-Turn Short Circuit (ITSC) fault in their stator winding. Short circuit current estimation amplitude is one of the most critical parameters while making critical decisions for the post-fault operations. Once the fault is detected, it is essential to control it to keep amplitude below rated current to prevent further winding degradation due to overheating. In this paper, a simplified ITSC fault model is developed for permanent magnet motors and integrated into the drive control for all permanent magnet motors. Based on the estimated amplitude of short circuit current, the speed reference is adjusted by an iterative scheme to bring short circuit current amplitude to a safe value. The adjusted speed reference defines the motor's Safe Operating Area (SOA) under ITSC fault. To test the efficacy and validity of the proposed method, experiments are conducted on lab prototype motors that have taps introduced in their stator windings to mimic ITSC faults.

Fault diagnosis and condition monitoring of electric machines using readily available drive sensors are highly critical in safety-critical applications. This article presents an accurate method to detect stator interturn short-circuit (ITSC) fault using the spatial inductance profile, which can be implemented both at start-up (standstill) or in real time. For this purpose, ac motor drive spatially scans the motor at each start-up in milliseconds by injecting high-frequency signals to obtain spatial inductance profile, analyze the fault status, and locate faulty phase. Using the start-up inductance profiling as reference, online detection version is implanted successfully, which is essential for real-time condition monitoring. This approach is highly sensitive ITSC faults, agnostic to temperature variations, and takes inverter nonlinearities into account caused by dead time and voltage drop across the power devices. To demonstrate the efficacy and practicability of the proposed algorithm, it is experimentally verified on two PMSMs, interior permanent magnet synchronous motor (IPMSM) and an induction motor, all rewounded to mimic ITSC faults. It is shown that the algorithm can detect ITSC faults very accurately in the order of milliseconds even if the number of shorted turns is very low.

This paper puts forward a novel method to estimate the fault severity index and consequently the number of shorted turns in permanent magnet motors (PMSM) with inter turn short circuit fault (ITSC). In this method, the machine is excited with DC current at stand still conditions to obtain the winding resistance seen by the d-axis of the machine. The estimated d-axis resistance contains useful information pertaining to the fault severity index, and is used to extract the fault severity index and the number of shorted turns in the faulty motor. The proposed method enables the estimation of fault severity index without complex machine modeling and extensive experiments with different machine prototypes, or Finite Element Analysis (FEA) models to analyze the relationship between machine currents and short circuit current. To enhance the accuracy of the estimation algorithm, this paper addresses practical issues associated with inverter non-linearity effects such as distortion voltage due to dead time effects and voltage drops across the switching devices, and proposes method to estimate the fault severity index that is immune to the aforementioned issues. Determining the fault index is critical in order to design appropriate mitigation techniques, as well as defining a safe operating area for the machine to avoid further damage to the winding. To verify the efficacy of this algorithm, experiments are conducted on a three phase PMSMs with artificial taps that imitate the behavior of inter turn short circuit fault, and results which demonstrates the practicability of this algorithm are presented.

This paper presents a comprehensive study on current loop auto-tuning for self-commissioning of sensorless interior permanent magnet synchronous motor (IPMSM) drives. Well-tuned current controllers are essential both for high performance operation and self-commissioning procedure. However, current controller design parameters i.e. Ld and Lq are not known at the beginning of self-commissioning procedure where parameter estimation has unique challenges. For example, initial rotor position to determine the actual d- and q-axes is not known. Also, feedback controllers are not usable due to unknown motor parameters. As a solution, this paper proposes a method to estimate the spatial inductance map by spatially scanning the motor via sinusoidal voltage injection in a controlled manner. Then it uses the estimated inductance values to tune the current controllers. The proposed method identifies Ld and Lq values without actual d- and q-axis positions and does not use any closed loop controller. The whole procedure takes around a second. Practical considerations such as automatic selection of the injection voltage, and digital control and dead-time effects are carefully addressed. The findings are experimentally verified on a 3-phase IPMSM drive. Furthermore, its applicability to different motor types is also demonstrated.

This paper presents a practical procedure regarding inertia estimation and speed loop auto-tuning for sensorless interior permanent-magnet synchronous motor (IPMSM) drives. In many applications, well-tuned speed controller is essential to enable dynamic and stable drive operations. However, high performance speed controller design requires system parameters such as inertia which are unknown at the beginning of drive commissioning. Manual trial-and-error tuning is unattractive due to its blind, lengthy, non-optimal, and potentially unsafe nature. On the other hand, model-based tuning through inertia estimation has unique challenges at this step of selfcommissioning. Determining the test torque for a safe and reasonably short test in inertia estimation is a real practical problem because the total inertia can be in wide range in different mechanical systems. Furthermore, feedback controllers are not usable to automatically determine the test torque due to unknown motor parameters. As a solution, this paper proposes an automated test torque selection procedure. Then it identifies the inertia and lastly tunes the speed controller using the identified parameter. Overall, a fully automated speed-loop autotuning method is proposed which aims minimal operator involvement. The findings are experimentally verified on an IPMSM drive. In addition, the proposed method can easily be extended to different motor types and sensored or sensorless vector control strategies.

This paper presents a comprehensive study on degradation monitoring of SiC MOSFETs and proposes an early warning method to detect ageing. The proposed plug-in tool can be integrated to smart gate drivers or directly to power converters. During the accelerated ageing tests (power cycling within safe operating area) several electrical parameters are monitored to find out critical signatures and precursors of failure. Among those, gate leakage current is identified as the most practical precursor which exhibits consistent changes in all aged devices and is relatively easy to monitor. Due to its simple scheme and low cost, it can potentially be embedded into commercial gate drivers featuring improved reliability options.