Realization of the Sensorless Permanent Magnet Synchronous Motor Drive Control System with an Intelligent Controller

The significant advantages of permanent magnet synchronous motors, such as very good dynamic properties, high efficiency and power density, have led to their frequent use in many drive systems today. However, like other types of electric motors, they are exposed to various types of faults, including stator winding faults. Stator winding faults are mainly inter-turn short circuits and are among the most common faults in electric motors. In this paper, the possibility of using the spectral analysis of symmetrical current components to extract fault symptoms and the machine-learning-based K-Nearest Neighbors (KNN) algorithm for the detection and classification of the PMSM stator winding fault is presented. The impact of the key parameters of this classifier on the effectiveness of stator winding fault detection and classification is presented and discussed in detail, which has not been researched in the literature so far. The proposed solution was verified experimentally using a 2.5 kW PMSM, the construction of which was specially prepared for carrying out controlled inter-turn short circuits.

Section: Introductionmentioning

confidence: 99%

On-line Detection and Classification of PMSM Stator Winding Faults Based on Stator Current Symmetrical Components Analysis and the KNN Algorithm

Pietrzak

Wolkiewicz

2021

“…The proposed method was validated on the two different drives (induction motor, PMSM) and demonstrated good performance with respect to parametric uncertainties, unknown load disturbance and measurement noise in the position and current loops. In [15], an estimator and an intelligent controller were proposed for sensorless control of the PMSM drive. The estimator, for position and rotor speed estimation, was constructed using a sliding mode observer and a phase-locked loop.…”

Section: The Special Issuementioning

confidence: 99%

Advanced Control for Electric Drives: Current Challenges and Future Perspectives

Merabet

2020

In the Special Issue “Advanced Control for Electric Drives”, the objective is to address a variety of issues related to advances in control techniques for electric drives, implementation challenges, and applications in emerging fields such as electric vehicles, unmanned aerial vehicles, maglev trains and motion applications. This issue includes 15 selected and peer-reviewed articles discussing a wide range of topics, where intelligent control, estimation and observation schemes were applied to electric drives for various applications. Different drives were studied such as induction motors, permanent magnet synchronous motors and brushless direct current motors.

“…Another characteristic regarding the difficulty of harmonizing these models is that the mathematical models of electromechanical systems are continuous (which can be discretized with the usual methods) while the mathematical models of power electronic systems are hybrid (continuous-discrete). The rapid progress in computing technology and hardware in recent years has enabled the development of several directions for creating PMSM modelling and control strategies, based on predictive controllers [8,9], adaptive controllers [10,11], fuzzy controllers [12,13], or controllers based on artificial neural networks [14,15], but also controllers based on the fractional calculus [16,17] in order to improve the functionality of industrial installations. Moreover, to eliminate some of the transducers and mechanical parts which can decrease the reliability of the system, a series of observers has been developed for the estimation of the PMSM rotor speed and position [17][18][19][20][21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Sensorless Control of PMSM Based on Backstepping-PSO-Type Controller and ESO-Type Observer Using Real-Time Hardware

Nicola

Nicola²,

Selişteanu

2021

In the case of using a Permanent Magnet Synchronous Motor (PMSM) linear model of limited-range parametric variations and of relatively low dynamic of the load torque, the Field Oriented Control (FOC) type strategy ensures good performance of the PMSM control. Therefore, when using a non-linear model of wide-range parametric variations and of high dynamic of the load torque, a backstepping-type controller is proposed, whose tuning parameters are optimized by using a Particle Swarm Optimization (PSO) method. By designing an Extended State Observer (ESO), which provides a good estimate of the PMSM rotor position and speed under uncertainty conditions and with a response time shorter than that of the backstepping-type controller, this observer can be incorporated into the PMSM sensorless control system. The superior performance of the proposed sensorless control system based on the backstepping-PSO-type controller and an ESO-type observer is demonstrated through numerical simulations. Given that the real-time implementation of the control algorithms and observers in an embedded system is a difficult task, consisting of several steps, it is presented after the numerical simulations, which can be assimilated into the Software-in-the-Loop (SIL) step, the Processor-in-the-Loop (PIL) intermediate step, and the Hardware-in-the-Loop (HIL) final step. A comparison between the backstepping-PSO-type controller and the PI-PSO-type controller is presented by means of the real-time implementation of these controllers and demonstrates the superiority of the backstepping-PSO-type controller.