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

Improved high-frequency carrier voltage measurement for position estimation of switched-flux permanent magnet machines

The conventional 12/10 stator/rotor poles switched-flux permanent magnet (SFPM) machine is usually based on all poles wound topology, each phase comprising four winding coils in series connection. However, alternate coils of the same phase have different machine saliency characteristics. Moreover, in order to measure the high-frequency (HF) carrier voltage, the mid-tapered winding wires can be utilized. Consequently, the machine saliencies can be measured separately from two parts of winding coil connections. This paper investigates the influences of machine saliencies on the sensorless rotor position estimations based on different sequence of winding coil connections, in which the primary saliency may contain some additional harmonics referring to the secondary saliency that will degrade the overall sensorless control operations. Furthermore, a simple compensation method is proposed to reduce the influence of multiple saliencies to achieve more accurate sensorless rotor position estimation. By comparing with rotor positon estimations without the proposed compensation and HF carrier current based method, the effectiveness of improved sensorless rotor position estimation has been demonstrated experimentally, as well as the application to dual 3-phase SFPM machines

Influence of nonideal voltage measurement on parameter estimation in permanent-magnet synchronous machines

This paper investigates the influence of nonideal voltage measurements on the parameter estimation of permanentmagnet synchronous machines (PMSMs). The influence of nonideal voltage measurements, such as the dc bus voltage drop, zero shift in the amplifier, and voltage source inverter nonlinearities, on the estimation of different machine parameters is investigated by theoretical and experimental analysis. For analysis, a model-reference-adaptive-system-based estimator is first described for the parameter estimation of the q-axis inductance, stator winding resistance, and rotor flux linkage. The estimator is then applied to a prototype surface-mounted PMSM to investigate the influence of nonideal voltage measurement on the estimation of various machine parameter values. It shows that, at low speed, the inverter nonlinearity compensation has significant influence on both the rotor flux linkage and winding resistance estimations while, at high speed, it has significant influence only on the winding resistance estimation and has negligible influence on the rotor flux linkage estimation. In addition, the inverter nonlinearity compensation will not influence the q-axis inductance estimation when it is under id = 0 control. However, the dc bus voltage drop due to the load variation and zero shift in the amplifier will significantly influence the q-axis inductance estimation. © 2011 IEEE

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

Coevolutionary particle swarm optimization using AIS and its application in multiparameter estimation of PMSM

In this paper, a coevolutionary particle-swarm-optimization (PSO) algorithm associating with the artificial immune principle is proposed. In the proposed algorithm, the whole population is divided into two kinds of subpopulations consisting of one elite subpopulation and several normal subpopulations. The best individual of each normal subpopulation will be memorized into the elite subpopulation during the evolution process. A hybrid method, which creates new individuals by using three different operators, is presented to ensure the diversity of all the subpopulations. Furthermore, a simple adaptive wavelet learning operator is utilized for accelerating the convergence speed of the pbest particles. The improved immune-clonal-selection operator is employed for optimizing the elite subpopulation, while the migration scheme is employed for the information exchange between elite subpopulation and normal subpopulations. The performance of the proposed algorithm is verified by testing on a suite of standard benchmark functions, which shows faster convergence and global search ability. Its performance is further evaluated by its application to multiparameter estimation of permanent-magnet synchronous machines, which shows that its performance significantly outperforms existing PSOs. The proposed algorithm can estimate the machine dq-axis inductances, stator winding resistance, and rotor flux linkage simultaneously. © 2013 IEEE

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

Quantum genetic algorithm-based parameter estimation of PMSM under variable speed control accounting for system identifiability and VSI nonlinearity

This paper proposes a multiparameter estimation scheme for permanent-magnet (PM) synchronous machines (PMSMs) under variable-speed control, of which the estimation model is full rank and has taken into account the estimation and compensation of voltage-source-inverter nonlinearity. During the proposed estimation, the PMSM will work under variable speed control, and two sets of machine data corresponding to two sets of different rotor speeds will be recorded and used for the calculation of the proposed estimation model. It shows that the proposed estimation model can be solved by using a conventional quantum genetic algorithm and can ensure the identifiability of all needed parameters owing to its inherent full rank feature. The performance test of the proposed estimation is then conducted on an interior PMSM, and it shows that parameters such as rotor PM flux linkage and winding resistance can be accurately estimated without the aid from nominal parameter values of PMSM. Therefore, the proposed method can be used for the condition monitoring of stator winding and rotor PMs

Position offset-based parameter estimation for permanent magnet synchronous machines under variable speed control

Aposition offset-based multiparameter estimation for permanent magnet synchronous machines (PMSMs) under variable speed control is proposed in this paper, which does not need the aid from nominal parameter values of the PMSM and could estimate the rotor permanent magnet (PM) flux linkage and winding resistance separately. For the estimation of rotor PM flux linkage, two sets of PMSM data corresponding to two position offsets are measured while the dq-axis reference currents are set to constants so as to ensure that the estimated machine parameters will not vary during the data measurement. Afterwards, the winding resistance will be estimated from measured PMSM data without addition of position offsets, in which the estimation and compensation of distorted voltage due to inverter nonlinearity are also taken into account. The conventional quantum genetic algorithm is used for aiding the calculation of proposed estimation,which is finally tested on an interior PMSM and shows very good performance in the estimation of rotor PM flux linkage and winding resistance. Thus, it could be used for the condition monitoring of stator winding and rotor PMs of PMSMs

Mechanical parameter estimation of permanent-magnet synchronous machines with aiding from estimation of rotor PM flux linkage

This paper proposes a combined parameter estimation for permanent-magnet synchronous machines (PMSMs), by which the rotor permanent-magnet (PM) flux linkage and mechanical parameters, such as the combined moment of inertia and viscous friction coefficient, are estimated without the aid from nominal machine parameter values. The proposed estimation of rotor PM flux linkage is based on the addition of position offsets and does not need the information of other machine parameters or injection of currents. With the aid from estimated rotor PM flux linkage, the electromagnetic torque can be consequently calculated for the estimation of mechanical parameters at no-load condition, and a two-step method is employed to achieve a higher ratio of signal to noise. This combined parameter estimation is finally validated on a surface-mounted PMSM (SPMSM) and an interior PMSM (IPMSM), respectively. It is found that the estimation error of combined moment of inertia with reference to its nominal value can be less than 10% even if it (the combined moment of inertia) is lower than 1 × 10−3 kg · m2, while the estimation error will be further decreased to less than 3% if it is higher than 7 × 10−3 kg · m2

Position-offset-based parameter estimation using the adaline NN for condition monitoring of permanent-magnet synchronous machines

This paper proposes how to use the addition of rotor position offsets as perturbation signals for the parameter estimation of permanent-magnet synchronous machines (PMSMs), which can be used for the condition monitoring of rotor permanent magnet and stator winding. During the proposed estimation, two small position offsets are intentionally added into the drive system, and the resulting voltage variation will be recorded for the estimation of rotor flux linkage. With the aid from estimated rotor flux linkage, the stator winding resistance can be subsequently estimated at steady state. This method is experimentally verified on two prototype PMSMs (150 W and 3 kW, respectively) and shows good performance in monitoring the variation of rotor flux linkage and winding resistance