Permanent Magnet Synchronous Motors (PMSMs) are now extensively used in many critical applications. There is an increasing need for the motor and control system to have fault tolerant capabilities. This paper presents a fault tolerant control strategy to operate the PMSM during inter-turn fault conditions. The proposed technique combines the Model Predictive Control (MPC) for the speed and current control loops, and an almost error-free Unscented Kalman Filter (UKF) to estimate the PMSM inter-turn fault ratio. The PMSM statespace model for healthy and faulty conditions will be presented. Also, the equations and the remedial action of the MPC and UKF are provided in detail. The proposed algorithm is applied to PMSM model as a case study with a range of simulation analysis and discussion of results.
This paper investigates the detection of inter-turn phase faults in Permanent Magnet Synchronous Generators (PMSGs) using the Unscented Kalman Filter (UKF) compared to the Extended Kalman Filter (EKF). PMSGs are subject to several faults such as bearing, eccentricity, demagnetization, short circuit, and inter-turn faults. Accurate and early detection of the fault type is crucial for robust operation. Several techniques can detect these faults. UKF and EKF are presented here as one of the model-based fault diagnosis techniques. In the presented simulation, a comparison between the UKF and EKF estimation response of the fault has been shown. Both techniques have provided the ability to detect the inter-turn fault with the proposed PMSG fault model. However, the difference between the estimation response accuracy and speed plays an important role to decide the most effective technique.
Fault detection is critical for industrial applications to maintain a stable operation and to reduce maintenance costs. Many fault detection techniques have been introduced recently to cope with the increasing demand for more safe operations. One of the most promising fault detection algorithms is the Unscented Kalman Filter (UKF). UKF is a model-based algorithm that could be used to detect different fault types for a given system. On the other hand, the three-tank system is a well-known benchmark that simulates many industrial applications. The fault detection of the three-tank system is quite challenging as it is a Multi-Input Multi-Output (MIMO) nonlinear system. Therefore, UKF will be employed as a fault detection strategy for this system to detect sensor and actuator faults. The performance of the UKF will be investigated under different operating and fault conditions to show its merits for the given case study.
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