Parameter estimation for condition monitoring of PMSM stator winding and rotor permanent magnets

Winding resistance and rotor flux linkage are important to controller design and condition monitoring of a surface-mounted permanent-magnet synchronous machine (PMSM) system. In this paper, an online method for simultaneously estimating the winding resistance and rotor flux linkage of a PMSM is proposed, which is suitable for application under constant load torque. It is based on a proposed full-rank reference/variable model. Under constant load torque, a short pulse of id 0 is transiently injected into the d-axis current, and two sets of machine rotor speeds, currents, and voltages corresponding to id = 0 and id 0 are then measured for estimation. Since the torque is kept almost constant during the transient injection, owing to the moment of system inertia and negligible reluctance torque, the variation of rotor flux linkage due to injected id 0 can be taken into account by using the equation of constant torque without measuring the load torque and is then associated with the two sets of machine equations for simultaneously estimating the winding resistance and rotor flux linkage. Furthermore, the proposed method does not need the values of the $dq$-axis inductances, while the influence from the nonideal voltage measurement, which will cause an ill-conditioned problem in the estimation, has been taken into account and solved by error analysis. This method is finally verified on two prototype PMSMs and shows good performance. © 1982-2012 IEEE

NOVEL METHODS FOR PERMANENT MAGNET DEMAGNETIZATION DETECTION IN PERMANENT MAGNET SYNCHRONOUS MACHINES

Monitoring and detecting PM flux linkage is important to maintain a stable permanent magnet synchronous motor (PMSM) operation. The key problems that need to be solved at this stage are to: 1) establish a demagnetization magnetic flux model that takes into account the influence of various nonlinear and complex factors to reveal the demagnetization mechanism; 2) explore the relationship between different factors and demagnetizing magnetic field, to detect the demagnetization in the early stage; and 3) propose post-demagnetization measures. This thesis investigates permanent magnet (PM) demagnetization detection for PMSM machines to achieve high-performance and reliable machine drive for practical industrial and consumer applications. In this thesis, theoretical analysis, numerical calculation as well as experimental investigations are carried out to systematically study the demagnetization detection mechanism and post-demagnetization measures for permanent magnet synchronous motors. At first a flux based acoustic noise model is proposed to analyze online PM demagnetization detection by using a back propagation neural network (BPNN) with acoustic noise data. In this method, the PM demagnetization is detected by means of comparing the measured acoustic signal of PMSM with an acoustic signal library of seven acoustical indicators. Then torque ripple is chosen for online PM demagnetization diagnosis by using continuous wavelet transforms (CWT) and Grey System Theory (GST). This model is able to reveal the relationship between torque variation and PM electromagnetic interferences. After demagnetization being detected, a current regulation strategy is proposed to minimize the torque ripples induced by PM demagnetization. Next, in order to compare the demagnetization detection accuracy, different data mining techniques, Vold-Kalman filtering order tracking (VKF-OT) and dynamic Bayesian network (DBN) based detection approach is applied to real-time PM flux monitoring through torque ripple again. VKF-OT is introduced to track the order of torque ripple of PMSM running in transient state. Lastly, the combination of acoustic noise and torque is investigated for demagnetization detection by using multi-sensor information fusion to improve the system redundancy and accuracy. Bayesian network based multi-sensor information fusion is then proposed to detect the demagnetization ratio from the extracted features. During the analysis of demagnetization detection methods, the proposed PM detection approaches both form torque ripple and acoustic noise are extensively evaluated on a laboratory PM machine drive system under different speeds, load conditions, and temperatures

Methods of resistance estimation in permanent magnet synchronous motors for real-time thermal management

Real-time thermal management of electrical ma- chines relies on sufficiently accurate indicators of internal tem- perature. One indicator of temperature in a permanent-magnet synchronous motor (PMSM) is the stator winding resistance. Detection of PMSM winding resistance in the literature has been made on machines with relatively high resistances, where the resistive voltage vector is significant under load. This paper describes two techniques which can be applied to detect the winding resistance, through ‘Fixed Angle’ and ‘Fixed Mag- nitude’ current injection. Two further methods are described which discriminate injected current and voltages from motoring currents and voltages: ‘Unipolar’ and ‘Bipolar’ separation. These enable the resistance to be determined, and hence the winding temperature in permanent-magnet machines. These methods can be applied under load, and in a manner that does not disturb motor torque or speed. The method distinguishes between changes in the electro-motive force (EMF) constant and the resistive voltage. This paper introduces the techniques, whilst a companion paper covers the application of one of the methods to a PMSM drive system

Global identification of electrical and mechanical parameters in PMSM drive based on dynamic self-learning PSO

A global parameter estimation method for a PMSM drive system is proposed, where the electrical parameters, mechanical parameters and voltage-source-inverter (VSI) nonlinearity are regarded as a whole and parameter estimation is formulated as a single parameter optimization model. A dynamic learning estimator is proposed for tracking the electrical parameters, mechanical parameters and VSI of PMSM drive by using dynamic self learning particle swarm optimization (DSLPSO). In DSLPSO, a novel movement modification equation with dynamic exemplar learning strategy is designed to ensure its diversity and achieve a reasonable tradeoff between the exploitation and exploration during the search process. Moreover, a nonlinear multi-scale based interactive learning operator is introduced for accelerating the convergence speed of the Pbest particles; meanwhile a dynamic opposition-based learning (OBL) strategy is designed to facilitate the gBest particle to explore a potentially better region. The proposed algorithm is applied to parameter estimation for a PMSM drive system. The results show that the proposed method has better performance in tracking the variation of electrical parameters, and estimating the immeasurable mechanical parameters and the VSI disturbance voltage simultaneously

Thermal Modeling of Permanent Magnet Synchronous Motors for Electric Vehicle Application

Permanent magnet synchronous motor (PMSM) is a better choice as a traction motor since it has high power density and high torque capability within compact structure. However, accommodating such high power within compact space is a great challenge, as it is responsible for significant rise of heat in PMSM. As a result, there is considerable increase in operating temperature which in turn negatively affects the electromagnetic performance of the motor. Further, if the temperature rise exceeds the permissible limit, it can cause demagnetization of magnets, damage of insulation, bearing faults, etc. which in turn affect the overall lifecycle of the motor. Therefore, thermal issues need to be dealt with carefully during the design phase of PMSM. Hence, the main focus of this thesis is to develop efficient ways for thermal modeling to address thermal issues properly. Firstly, a universal lumped parameter thermal network (LPTN) is proposed which can be used for all types of PMSMs regardless of any winding configuration and any position of magnets in the rotor. Further, a computationally efficient finite element analysis (FEA) thermal model is proposed with a novel hybrid technique utilizing LPTN strategy for addressing the air gap convection in an efficient way. Both proposed LPTN and FEA thermal models are simplified ways to predict motor temperature with a comparatively less calculation process. Finally, the proposed thermal models have been experimentally validated for the newly designed interior and surface mounted PMSM prototypes. Again, a procedure for effective cooling design process of PMSM has been suggested by developing an algorithm for cooling design optimization of the motor. Further, a computational fluid dynamics (CFD) model with a proposed two-way electro-thermal co-analysis strategy has been developed to predict both thermal and electromagnetic performance of PMSM more accurately considering the active cooling system. The developed step algorithm and CFD modeling approach will pave the way for future work on cooling design optimization of the newly designed interior and surface mounted PMSM prototypes

Modelling and Detecting Faults of Permanent Magnet Synchronous Motors in Dynamic Operations

Paper VI is excluded from the dissertation until the article will be published.Permanent magnet synchronous motors (PMSMs) have played a key role in commercial and industrial applications, i.e. electric vehicles and wind turbines. They are popular due to their high efficiency, control simplification and large torque-to-size ratio although they are expensive. A fault will eventually occur in an operating PMSM, either by improper maintenance or wear from thermal and mechanical stresses. The most frequent PMSM faults are bearing faults, short-circuit and eccentricity. PMSM may also suffer from demagnetisation, which is unique in permanent magnet machines. Condition monitoring or fault diagnosis schemes are necessary for detecting and identifying these faults early in their incipient state, e.g. partial demagnetisation and inter-turn short circuit. Successful fault classification will ensure safe operations, speed up the maintenance process and decrease unexpected downtime and cost. The research in recent years is drawn towards fault analysis under dynamic operating conditions, i.e. variable load and speed. Most of these techniques have focused on the use of voltage, current and torque, while magnetic flux density in the air-gap or the proximity of the motor has not yet been fully capitalised. This dissertation focuses on two main research topics in modelling and diagnosis of faulty PMSM in dynamic operations. The first problem is to decrease the computational burden of modelling and analysis techniques. The first contributions are new and faster methods for computing the permeance network model and quadratic time-frequency distributions. Reducing their computational burden makes them more attractive in analysis or fault diagnosis. The second contribution is to expand the model description of a simpler model. This can be achieved through a field reconstruction model with a magnet library and a description of both magnet defects and inter-turn short circuits. The second research topic is to simplify the installation and complexity of fault diagnosis schemes in PMSM. The aim is to reduce required sensors of fault diagnosis schemes, regardless of operation profiles. Conventional methods often rely on either steady-state or predefined operation profiles, e.g. start-up. A fault diagnosis scheme robust to any speed changes is desirable since a fault can be detected regardless of operations. The final contribution is the implementation of reinforcement learning in an active learning scheme to address the imbalance dataset problem. Samples from a faulty PMSM are often initially unavailable and expensive to acquire. Reinforcement learning with a weighted reward function might balance the dataset to enhance the trained fault classifier’s performance.publishedVersio

Improved position offset based parameter determination of permanent magnet synchronous machines under different load conditions

© The Institution of Engineering and Technology 2017.This study proposes a novel method for the parameter determination of permanent magnet (PM) synchronous machines under different load conditions. It can identify the total dq-axis flux linkages and also the PM flux linkage separately by the addition of a pair of negative and positive position offsets. It is also noteworthy that the influence of uncertain inverter non-linearity and winding resistance is cancelled during the modelling process, and the experimental results on two different PM synchronous machines show a good agreement with the finite-element prediction results. More importantly, it shows good performance in online tracking the variation of PM flux linkage, which is an important feature for aiding the condition monitoring of PMs, for example, monitoring the temperature of PMs

Parameter estimation for VSI-Fed PMSM based on a dynamic PSO with learning strategies

© 1986-2012 IEEE.A dynamic particle swarm optimization with learning strategy (DPSO-LS) is proposed for key parameter estimation for permanent magnet synchronous machines (PMSMs), where the voltage-source inverter (VSI) nonlinearities are taken into account in the parameter estimation model and can be estimated simultaneously with other machine parameters. In the DPSO-LS algorithm, a novel movement modification equation with variable exploration vector is designed to effectively update particles, enabling swarms to cover large areas of search space with large probability and thus the global search ability is enhanced. Moreover, a Gaussian-distribution-based dynamic opposition-based learning strategy is developed to help the pBest jump out local optima. The proposed DPSO-LS can significantly enhance the estimator model accuracy and dynamic performance. Finally, the proposed algorithm is applied to multiple parameter estimation including the VSI nonlinearities of a PMSM. The performance of DPSO-LS is compared with several existing PSO algorithms, and the comparison results show that the proposed parameters estimation method has better performance in tracking the variation of machine parameters effectively and estimating the VSI nonlinearities under different operation conditions

Online multiparameter estimation of nonsalient-pole PM synchronous machines with temperature variation tracking

The ill-convergence of multiparameter estimation due to the rank-deficient state equations of permanent-magnet synchronous machines (PMSMs) is investigated. It is verified that the PMSM model for multiparameter estimation under id = 0 control is rank deficient for simultaneously estimating winding resistance, rotor flux linkage, and winding inductance and cannot ensure them to converge to the correct parameter values. A new method is proposed based on injecting a short pulse of negative id current and simultaneously solving two sets of simplified PMSM state equations corresponding to id = 0 and id ≠ 0 by using an Adaline neural network. The convergence of solutions is ensured, while the minimum |i d| is determined from the error analysis for nonsalient-pole PMSMs. The proposed method does not need the nominal value of any parameter and only needs to sample the winding terminal currents and voltages, and the rotor speed for simultaneously estimating the dq-axis inductances, the winding resistance, and the rotor flux linkage in nonsalient-pole PMSMs. Compared with existing methods, the proposed method can eliminate the estimation error caused by the variation of rotor flux linkage and inductance as a result of state change due to the injected d-axis current in the surface-mounted PMSM. The method is verified by experiments, and the results show that the proposed method has negligible influence on output torque and rotor speed and has good performance in tracking the variation of PMSM parameters due to temperature variation. © 2010 IEEE

Estimation of Stator Resistance and Rotor Flux Linkage in SPMSM Using CLPSO with Opposition-Based-Learning Strategy

Electromagnetic parameters are important for controller design and condition monitoring of permanent magnet synchronous machine (PMSM) system. In this paper, an improved comprehensive learning particle swarm optimization (CLPSO) with opposition-based-learning (OBL) strategy is proposed for estimating stator resistance and rotor flux linkage in surface-mounted PMSM; the proposed method is referred to as CLPSO-OBL. In the CLPSO-OBL framework, an opposition-learning strategy is used for best particles reinforcement learning to improve the dynamic performance and global convergence ability of the CLPSO. The proposed parameter optimization not only retains the advantages of diversity in the CLPSO but also has inherited global exploration capability of the OBL. Then, the proposed method is applied to estimate the stator resistance and rotor flux linkage of surface-mounted PMSM. The experimental results show that the CLPSO-OBL has better performance in estimating winding resistance and PM flux compared to the existing peer PSOs. Furthermore, the proposed parameter estimation model and optimization method are simple and with good accuracy, fast convergence, and easy digital implementation