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

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

GPU Implementation of DPSO-RE Algorithm for Parameters Identification of Surface PMSM Considering VSI Nonlinearity

In this paper, an accurate parameter estimation model of surface permanent magnet synchronous machines (SPMSMs) is established by taking into account voltage-source-inverter (VSI) nonlinearity. A fast dynamic particle swarm optimization (DPSO) algorithm combined with a receptor editing (RE) strategy is proposed to explore the optimal values of parameter estimations. This combination provides an accelerated implementation on graphics processing unit (GPU), and the proposed method is, therefore, referred to as G-DPSORE. In G-DPSO-RE, a dynamic labor division strategy is incorporated into the swarms according to the designed evolutionary factor during the evolution process. Two novel modifications of the movement equation are designed to update the velocity of particles. Moreover, a chaotic-logistic-based immune RE operator is developed to facilitate the global best individual (gBest particle) to explore a potentially better region. Furthermore, a GPU parallel acceleration technique is utilized to speed up parameter estimation procedure. It has been demonstrated that the proposed method is effective for simultaneous estimation of the PMSM parameters and the disturbance voltage (Vdead) due to VSI nonlinearity from experimental data for currents and rotor speed measured with inexpensive equipment. The influence of the VSI nonlinearity on the accuracy of parameter estimation is analyzed

Parameter Estimation for PMSM based on a Back Propagation Neural Network Optimized by Chaotic Artificial Fish Swarm Algorithm

Permanent Magnet Synchronous Motor(PMSM) control system with strong nonlinearity makes it difficult to accurately identify motor parameters such as stator winding, dq axis inductance, and rotor flux linkage. Aiming at the premature convergence of traditional Back Propagation Neural Network(BPNN) in PMSM motor parameter identification, a new method of PMSM motor parameter identification is proposed. It uses Chaotic Artificial Fish Swarm Algorithm(CAFSA) to optimize the initial weights and thresholds of BPNN, and then strengthens training by BPNN algorithm. Thus, the global optimal network parameters are obtained by using the global optimization of CAFSA and the local search ability of BPNN. The simulation results and experimental data show that the initial value sensitivity of the network model optimized by CAFS-BPNN Algorithm is weak, the parameter setting is robust, and the system stability is good under complex conditions. Compared with other intelligent algorithms, such as RSL and PSO, CAFS-BPNNA has high identification accuracy and fast convergence speed for PMSM motor parameters

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

Dynamic Jiles–Atherton Model for Determining the Magnetic Power Loss at High Frequency in Permanent Magnet Machines

This paper describes a mathematical model for the dynamic magnetic power losses in the laminated steel stator of high frequency permanent magnet machines, such as Brushless DC (BLDC) Motors. The model presented is based on a utilization of the dynamic Jiles-Atherton model. Accurate dynamic BH curve fitting and magnetic power loss derivations have been achieved, where the calculated magnetic losses have shown around 95% accuracy from 5 Hz to 2000 Hz, over a flux density range of 1.0 T to 1.6 T. This approach has been applied to estimate the magnetic power loss of a small scale, high frequency (>10,000 rpm) BLDC motor, with calculated and measured losses being in close agreement.This is the author accepted manuscript. The final version is available from IEEE via 10.1109/TMAG.2014.2382594

NOVEL MODELING, TESTING AND CONTROL APPROACHES TOWARDS ENERGY EFFICIENCY IMPROVEMENT IN PERMANENT MAGNET SYNCHRONOUS MOTOR AND DRIVE SYSTEMS

This thesis investigates energy efficiency improvement in permanent magnet synchronous motor (PMSM) and drive system to achieve high–performance drive for practical industrial and primarily, traction applications. In achieving improved energy efficiency from a system level, this thesis proposes: (1) Accurate modeling and testing of loss components in PMSM considering inverter harmonics; (2) Easy–to–implement, accurate parameter determination techniques to understand variations in motor parameters due to saturation, cross–saturation and temperature; and (3) Control methodologies to improve system level efficiency considering improved loss models and parameter variations. An improved loss model to incorporate the influence of motor–drive interaction on the motor losses is developed by taking time and space harmonics into account. An improved winding function theory incorporating armature reaction fields due to fundamental and harmonic stator magnetic fields is proposed to calculate the additional harmonic losses in the PMSM. Once all contributing losses in the motor are modelled accurately, an investigation into control variables that affect the losses in the motor and inverter is performed. Three major control variables such as DC link voltage, switching frequency and current angle are chosen and the individual losses in the motor and inverter as well as the system losses are studied under varying control variables and wide operating conditions. Since the proposed loss as well as efficiency modeling involves machine operation dependent parameters, the effects of parameter variation on PMSM due to saturation and temperature variation are investigated. A recursive least square (RLS) based multi–parameter estimation is proposed to identify all the varying parameters of the PMSM to improve the accuracy and validity of the proposed model. The impact of losses on these parameters as well as the correct output torque considering the losses are studied. Based on the proposed loss models, parameter variations and the investigation into control variables, an off–line loss minimization procedure is developed to take into account the effects of parameter variations. The search–based procedure generates optimal current angles at varying operating conditions by considering maximization of system efficiency as the objective. In order to further simplify the consideration of parameter variations in real–time conditions, an on–line loss minimization procedure using DC power measurement and loss models solved on–line using terminal measurements in a PMSM drive is proposed. A gradient descent search–based algorithm is used to calculate the optimal current angle corresponding to maximum system efficiency from the input DC power measurement and output power based on the loss models. During the thesis investigations, the proposed models and control techniques are extensively evaluated on a laboratory PMSM drive system under different speeds, load conditions, and temperatures

Investigation of performance of fuzzy logic controllers optimized with the hybrid genetic-gravitational search algorithm for PMSM speed control

Fuzzy logic controllers (FLCs) are widely used to control complex systems with model uncertainty, such as alternating current motors. The design process of the FLC is generally based on the designer’s adjustments on the controller until the desired performance is achieved. However, doing the controller design in this way makes the design process quite difficult and time-consuming, so it is often impossible to make a suitable and successful design. In this study, the output membership functions of the FLC are optimized with heuristic algorithms to reach the best speed control performance of the permanent magnet synchronous motor (PMSM). This paper proposes a new hybrid algorithm called H-GA-GSA, created by combining the advantages of the Genetic Algorithm (GA) and Gravitational Search Algorithm (GSA) to optimize FLC. The paper presents a convenient adjustment and design method for optimizing FLC with heuristic algorithms considered. To evaluate the effectiveness of H-GA-GSA, the proposed hybrid algorithm has been compared with GA and GSA in terms of convergence rate, PMSM speed control performance and electromagnetic torque variations. Optimization performance and results obtained from simulation studies verify that the proposed hybrid H-GA-GSA outperforms GA and GSA

Design of segmental rotor and non-overlap windings in single-phase fefsm for low torque high speed applications

In this research, a new structure of single-phase field excitation flux switching motor (FEFSM) using segmental rotor structure and non-overlap windings arrangement is proposed in order to overcome the drawbacks of low torque and small power performances due to their longer flux path in the single-phase FEFSM using salient rotor structure and overlap windings arrangement. The objectives of this study are to design, analyse and examine performance of the proposed motor, to optimize the proposed motor for optimal performances, and to develop the proposed motor prototype for experimental performance validation. The design and analyses thru 2Dfinite element analysis (FEA) is conducted using JMAG Designer version 15, while deterministic optimization method is applied in design optimization process. To validate the 2D-FEA results, the motor prototype is developed and tested experimentally. Based on various rotor poles analysis, a combination of 12 pole 6 pole (12S-6P) has been selected as the best design due to their highest torque and power capability of 0.91 Nm and 277.4 W, respectively. Besides, the unbalance armature magnetic flux of the proposed FEFSM using segmental rotor has been resolved by using segmental rotor span refinement. The balanced armature magnetic flux amplitude ratio obtained is 1.002, almost 41.2% reduction from the initial design. In addition, the optimized motor has increased maximum torque and power by 80.25% to 1.65 Nm, and 43.6% to 398.6W, respectively. Moreover, copper loss of the optimized design has decreased by 9.7%%, hence increasing the motor efficiency of 25.3%. Finally, the measured results obtained from the prototype machine has reasonable agreement with FEA results, proving their prospect to be applied for industrial and home appliances