A boot-strap estimator for joint flux and parameters online identification for vector controlled induction motor drives

This paper presents a new approach for joint rotor flux and electrical parameters on-line identification in vector controlled high-performance induction motor drives based on a boot-strap estimator that uses a reduced order extended Kalman filter for rotor flux components and rotor parameters estimation and a recursive prediction error method for stator parameters estimation. Within the prediction error method some approaches are used and compared that affect both the adaptation gain and the direction in which the updates of stator parameters are made. The induction motor model structures are described in the rotor reference frame in order to reduce the computational effort by using a higher sampling time interval

Алгоритм идентификации электромагнитных параметров асинхронной машины при работе от трехфазной электрической сети

Synthesized algorithm for electromagnetic rotor time constant, active resistance and equivalent leakage inductance of stator induction motor for free rotating rotor. The problem is solved for induction motor model in the stationary stator frame α-β. The algorithm is based on the use of recursive least squares method, which ensures high accuracy of the parameter estimates for the minimum time. The observer does not assume prior information about the technical data machine and individual parameters of its equivalent circuit. Results of simulation demonstrated how effective of the proposed method of identification. The flexible structure of the algorithm allows it to be used for preliminary identification of an induction motor, and in the process operative work induction motor in the frequency-controlled electric drive with vector control.Синтезирован алгоритм идентификации электромагнитной постоянной времени ротора, активного сопротивления и эквивалентной индуктивности рассеяния статора асинхронного двигателя при полнофазном управлении со свободно вращающимся ротором. Задача решена для модели асинхронного двигателя, представленной в системе координат статора α–β. Алгоритм основан на применении рекуррентного метода наименьших квадратов, который гарантирует высокую точность оценки параметров за минимальное время. Наблюдатель не предполагает предварительной информации о технических данных машины и отдельных параметрах ее схемы замещения. Результаты имитационного моделирования свидетельствуют об эффективности предложенного метода идентификации. Гибкая структура алгоритма позволяет использовать его как для предварительной идентификации АД, так и в процессе оперативной работы АД в составе частотно-регулируемого электропривода с векторным управлением

A new online identification methodology for flux and parameters estimation of vector controlled induction motors

A new online identification methodology for estimation of the rotor flux components and the main electrical parameters of vector controlled induction motors is presented in this paper. The induction motor model is referred to the rotor reference frame for estimation of rotor flux and rotor parameters, and referred to the stator reference frame to estimate stator parameters. The stator parameters estimation is achieved by a prediction error method based on a model structure described by a linear regression that is independent of rotor speed and rotor parameters. The rotor flux components and rotor parameters are estimated by a reduced order extended Kalman filter, using a 4th-order state-space model structure where the state equation is described by matrices that are diagonal and independent of rotor speed as well as stator parameters. Both methods work in a boot-strap manner

Classes of model structures for state and parameter identification of vector controlled induction machines

The purpose of this paper is to present a synthesis of classes of model structures for joint state and parameter identification of vector controlled induction motors for real time and normal operating conditions. Based on its classical model a set of new classes of model structures is discussed and proposed for simultaneous estimation of rotor flux components and electrical parameters

Speed control of induction motor using fuzzy recursive least squares technique

Este artículo presenta el diseño de un controlador adaptativo, el sistema de control emplea lógica difusa adaptativa, modos deslizantes y es entrenado con la técnica de mínimos cuadrados recursivos. El problema de la variación de parámetros es resuelto con el controlador adaptativo; se utiliza un regulador interno PI con el cual se produce que el control de velocidad del motor de inducción sea realizado por medio de las corrientes de estator en vez de los voltajes. Se usa el modelo del motor en el sistema de coordenadas de flujo orientado del rotor para el desarrollo y prueba del sistema de control.A simple adaptive controller design is presented in this paper, the control system uses the adaptive fuzzy logic, sliding modes and is trained with the recursive least squares technique. The problem of parameter variation is solved with the adaptive controller; the use of an internal PI regulator produces that the speed control of the induction motor be achieved by the stator currents instead the input voltage. The rotor-flux oriented coordinated system model is used to develop and test the control system

Speed control of induction motor using fuzzy recursive least squares technique

A simple adaptive controller design is presented in this paper, the control system uses the adaptive fuzzy logic, sliding modes and is trained with the recursive least squares technique. The problem of parameter variation is solved with the adaptive controller; the use of an internal PI regulator produces that the speed control of the induction motor be achieved by the stator currents instead the input voltage. The rotor-flux oriented coordinated system model is used to develop and test the control system.Este artículo presenta el diseño de un controlador adaptativo, el sistema de control emplea lógica difusa adaptativa, modos deslizantes y es entrenado con la técnica de mínimos cuadrados recursivos. El problema de la variación de parámetros es resuelto con el controlador adaptativo; se utiliza un regulador interno PI con el cual se produce que el control de velocidad del motor de inducción sea realizado por medio de las corrientes de estator en vez de los voltajes. Se usa el modelo del motor en el sistema de coordenadas de flujo orientado del rotor para el desarrollo y prueba del sistema de control

Vibration-based adaptive novelty detection method for monitoring faults in a kinematic chain

Postprint (published version

Parameters estimation of BLDC motor based on physical approach and weighted recursive least square algorithm

Brushless DC motors (BLDCM) are widely used when high precision converters are required. Model based torque control schemes rely on a precise representation of their dynamics, which in turn expect reliable system parameters estimation. In this paper, we propose two procedures for BLDCM parameters identification used in an agriculture mobile robot’s wheel. The first one is based on the physical approach or equations using experimentation data to find the electrical and mechanical parameters of the BLDCM. The parameters are then used to elaborate the model of the motor established in Park’s reference frame. The second procedure is an online identification based on recursive least square algorithm. The procedure is implemented in a closed-loop scheme to guarantee the stability of the system, and it provide parameter matrices obtained by transforming electrical equations, established in Parks reference frame, and mechanical equation to discrete-time domain. From these matrices, and using well formulated intermediate variables, all desired parameters are deduced simultaneously. The identification procedures are being verified using simulation under Matlab-Simulink software

Identification of Induction Motors with Smart Circuit Breakers

The problem of estimating the parameters of induction motor models is considered, using the data measured by a circuit breaker equipped with industrial sensors. The measured data pertain to direct-on-line motor startups, during which the breaker acquires three-phase stator voltage and current derivative. This setup is novel with respect to previous contributions in the literature, where voltage and current (and possibly also rotor speed) are considered. The collected data are used to formulate a parameter identification problem, where the cost function penalizes the discrepancy between simulated and measured derivatives of the stator currents. The resulting nonlinear program is solved via numerical optimization, and a number of algorithmic improvements with respect to the literature are proposed. In order to evaluate the goodness of the obtained results, an experimental rig has been built, where the motor's voltages and currents are simultaneously acquired also by accurate sensors, and the corresponding identification results are compared with those obtained with the circuit breaker. The presented experimental results indicate that the considered industrial circuit breaker is able to provide data with high-enough quality to carry out model-based nonlinear identification of induction machines. The identified models can then be used for several further applications within a smart grid scenario