Parameter Identification Of Induction Motor Research Articles

Research on Parameter Identification of Induction Motor Based on Model Reference Adaptive System

Abstract This paper proposes a novel online parameter identification method based on model-reference adaptive systems to achieve more precise control of induction motors. This method efficiently and accurately obtains the parameters of induction motors during operation, facilitating effective control. Experimental validation is conducted on a hardware-in-the-loop test platform, demonstrating that the proposed online parameter identification method for induction motors achieves accurate parameter estimation with the advantages of high identification speed and precision. Control experiments on induction motors using the identified parameters further validate the feasibility of the proposed method for online parameter identification.

Reduced Order Modeling and Parameter Identification of Induction Motors Based on FEA Solutions

Saturation and skin effects are two key factors which affect the simulation accuracy of three-phase induction motors. This paper introduces a methodology to decouple the rotor parameters to consider the saturation effect and skin effect independently. The skin-effect parameters are further modeled by a two-branch circuit with optimized sectioned-bar parameters. Then, a reduced order model (ROM) is developed based on the two-rotor-branch circuit to consider the skin effect in the time domain. All model parameters are identified by a two-stage algorithm based on FEA solutions in the frequency domain at various stator phase currents and rotor slips, using a locked rotor scheme with equivalent rotor conductivities. The first stage identifies both the decoupled saturation-effect parameters and skin-effect parameters, and the second stage identifies the two-rotor-branch parameters based on the optimal value of the rotor bar section ratio. The simulation results using the proposed model are compared with the transient FEA results and measured data.

Parameter Identification of Induction Motor by Using Cooperative-Coevolution and a Nonlinear Estimator

Induction motors are one of the critical industrial drivers due to its simplicity, inexpensiveness, and high resistance. Such motors have a nonlinear model divided into two electrical and mechanical equations in terms of modeling. Knowing the values of electric parameters and mechanical moment of inertia is critically important for speed controlling and induction motors’ position. In many algorithms, electric parameters can be obtained by the locked rotor and unloaded tests, conducting these methods in laboratory would probably cost a lot of time and money. In this paper electrical parameters and moment of inertia are used approximately, without doing the above test by currents, voltages, and motor speed sampling in motor normal operation. This paper applies cooperative co-evolution method to remove certain costly tests that are required for induction motors. Two identification algorithms are suggested for all electrical parameters and moment of inertia. All inductances and resistances which are the two input parameters measured in electric equations using Cooperative-Coevolution algorithm. Mechanical model estimated the moment of inertia and load torque by using a nonlinear method based on Lyapunov. Computerized numerical simulations show that electric parameters, moment of inertia, and load torque were properly estimated by integrating the two smart and classic methods. The results show that the stator inductance error is about 1% and rotor inductance error is around 20%. Rotor and stator resistance error and self-Inductance is also less than one percent.

Parametric Identification of an Induction Motor Based on a Phase-Locked-Loop Frequency Control Algorithm

A method of identification of induction motor parameters based on a phase-locked loop frequency control algorithm was considered. The existing identification methods of the equivalent circuit parameters were analyzed and classified. The methods operating in real time and in the adaptation mode of electric drive during the first operative startup of electric drive were distinguished. In addition, using the methods of active identification, in which an additional test information signal is injected into the power circuit of an electric drive, is unacceptable during electric drive operation and they are used at the time of prior adjustment of electric drive until its operative startup or for a short time to adaptation of changing parameters. An evaluation algorithm of motor parameters was proposed, and functional and structural diagrams of parametric identification system were developed. The developed identification method based on a phase-locked loop frequency control algorithm is constructed upon leveling of the phase difference between the real (measured) stator current vector and the stator current vector calculated by the model. The simulation results of the implemented parametric identification system are presented. According to the results of model experiments, the error of estimation of motor parameters did not exceed 1%. The proposed parametric identification system can be used as part of a vector control system to determine the coefficients of internal regulators of electric drives.

Application of Simulated Annealing Particle Swarm Optimization Based on Correlation in Parameter Identification of Induction Motor

The strengths and weaknesses of correlation algorithm, simulated annealing algorithm, and particle swarm optimization algorithm are studied in this paper. A hybrid optimization algorithm is proposed by drawing upon the three algorithms, and the specific application processes are given. To extract the current fundamental signal, the correlation algorithm is used. To identify the motor dynamic parameter, the filtered stator current signal is simulated using simulated annealing particle swarm algorithm. The simulated annealing particle swarm optimization algorithm effectively incorporates the global optimization ability of simulated annealing algorithm with the fast convergence of particle swarm optimization by comparing the identification results of asynchronous motor with constant torque load and step load.

The Squirrel-Cage Induction Motor Model and Its Parameter Identification Via Steady and Dynamic Tests

One of the most important bases for designing robust closed-loop controllers applied to induction motor with high performance is establishing its mathematical model and state observers, as well as the parameter identification with high accuracy. In this paper, a step-by-step mathematical model of the squirrel-cage induction motor is described at αβ coordinate frame where the parameters are defined in detailed form; the rotor flux linkages and load torque are estimated via an asymptotic observer; the induction motor parameter identification is performed via a data acquisition board, applying dynamic and steady-state tests. Inductances of the induction motor model are calculated using the proposed relationships between the magnetically coupled circuit and equivalent circuit model. The mathematical model, state observers, and parameter identification procedure of squirrel-cage induction motor are validated via comparison of simulation signals with their corresponding real-time signals. This validation is made experimentally by a steady-state test, where load conditions are changed via a dynamometer which is belt coupled with the squirrel-cage induction motor.

Identification of Induction Motor Parameters at Standstill Based on Integral Calculation

This paper proposes an identification method for induction motor parameters at standstill using integral calculations. The rotor time constant and magnetizing inductance are identified. During the identification process, the induction motor to be tested is consistently excited according to a predetermined current reference. Using the predetermined current, the induction motor emulates the rated rotor flux and the rated slip condition, even at standstill. This method is based on an integral calculation so that the results are robust to measurement noise. Based on these features, the identification result of the proposed method is quite accurate, and the method shows excellent repeatability. Due to the simplicity of the proposed technique, this method can be easily applied to commercial inverter products. Simulation and experimental results confirm the effectiveness of the proposed method.

Induction Motor Parameter Identification Using a Gravitational Search Algorithm

The efficient use of electrical energy is a topic that has attracted attention for its environmental consequences. On the other hand, induction motors represent the main component in most industries. They consume the highest energy percentages in industrial facilities. This energy consumption depends on the operation conditions of the induction motor imposed by its internal parameters. Since the internal parameters of an induction motor are not directly measurable, an identification process must be conducted to obtain them. In the identification process, the parameter estimation is transformed into a multidimensional optimization problem where the internal parameters of the induction motor are considered as decision variables. Under this approach, the complexity of the optimization problem tends to produce multimodal error surfaces for which their cost functions are significantly difficult to minimize. Several algorithms based on evolutionary computation principles have been successfully applied to identify the optimal parameters of induction motors. However, most of them maintain an important limitation: They frequently obtain sub-optimal solutions as a result of an improper equilibrium between exploitation and exploration in their search strategies. This paper presents an algorithm for the optimal parameter identification of induction motors. To determine the parameters, the proposed method uses a recent evolutionary method called the gravitational search algorithm (GSA). Different from most of the existent evolutionary algorithms, the GSA presents a better performance in multimodal problems, avoiding critical flaws such as the premature convergence to sub-optimal solutions. Numerical simulations have been conducted on several models to show the effectiveness of the proposed scheme.

A Prediction Error Method-Based Self-Commissioning Scheme for Parameter Identification of Induction Motors in Sensorless Drives

To avoid inconvenience caused by shaft rotation and improve immunity to measurement noises, an elaborately designed scheme is proposed for parameter identification of induction motors. After analyzing the responses of a simple step-voltage test, a sequence of pseudorandom signals, customized to excite abundant dynamics in the featured frequency band of the motor, are injected into the stator in a single-phase mode at standstill. The crucial feature of the proposed scheme is that a nonlinear procedure is introduced to minimize “predicted errors” of the estimation model, which lowers influences of measurement noises notably, and thus the design of low-pass filters is simplified greatly. Experimental comparisons are carried out, including not only tests on a squirrel-cage motor, but also extended tests on a wound-rotor motor to testify accuracy of rotor-side parameters, both using a real inverter. The results indicate that the proposed scheme is able to estimate parameters required by controllers accurately in the noisy environment, and improve performances of sensorless motor drivers.

Research on Identification of Induction Motor Parameters Based on Nameplate Data

A new method for identification of motor equivalent circuit parameters is proposed based on motor nameplate and particle swarm optimization (PSO). That is effective solution to the present problems of motor equivalent circuit parameters identification method. An accurate motor equivalent circuit is built by introducing an equivalent stray-loss resistance, mechanical loss, and slip loss of ring brushes contact for wound motor. The identification accuracy of motor equivalent circuit parameters is improved. An identification optimization model of equivalent circuit is established based on motor nameplate parameters and motor equivalent circuit’s calculating parameters. With PSO for solving optimization model is achieved a higher probability optimization goals. Finally, a group of equivalent circuit parameters to achieve the objective function value is randomly selected. The motor electrical parameters is calculated at different speeds with the equivalent circuit identified parameters and compared with the measured values. Experimental results demonstrate the proposed parameter identification method based on motor nameplate parameters and combining PSO optimization method is effective.

Improved Artificial Fish Swarm Algorithm Applied on the Static Model of the Induction Motor Parameter Identification

According to the induction motor operating characteristics, application of improved artificial fish swarm algorithm based on the simplex method to solve the equivalent circuit model parameters identification problem. Gives a detailed calculation steps and the curve of function expressions. Finally, the identification results and identification using genetic algorithm result is compared to prove that the accuracy of the identification results of the improved artificial fish swarm algorithm having higher precision.

Parameters Identification of Induction Motor for Electric Vehicle Based on Least Squares Method

This paper deals with on-line parameter identification of induction motors (IM) by means of least square techniques .Using stator voltages, stator currents and velocity as input-output data. For analytical identification by recursive least square (RLS) algorithms, filtering of experimental data is performed by means of anticausal filters. The simulation results show that error of the parameter estimation of the rotor resistance, self inductance of the rotor winding, as well as the stator leakage inductance are less than 5% .It demonstrate the practical use of the identification method.

Application of evolutionary algorithms with adaptive mutation to the identification of induction motor parameters at standstill

PurposeThe purpose of this paper is to obtain a preliminary off‐line identification of induction motor (IM) parameters at standstill in a reasonable calculation time, which will be useful for the initial adjustment of controllers and state observer parameters in the sensorless drive system.Design/methodology/approachThe identification procedure of electrical parameters of IM equivalent circuit is performed at standstill and is based on the reconstruction of the stator current response to the forced stator voltage using evolutionary algorithms (EAs) with hard selection and different mutation schemes.FindingsIt is shown that an application of the EA with adaptive mutation mechanism based on simulated annealing method gives very good accuracy of parameters identification and the shortest execution time of the identification procedure as well in simulation as in the experimental tests.Research limitations/implicationsThe investigation looks mainly at the minimization of the execution time of the identification algorithm and on the identification accuracy performance, taking into account the good approximation of the measured stator current response.Practical implicationsThe proposed EA with the improved adaptive mutation scheme can be easily realised using modern digital signal processor (DSP), which is usually applied for control purposes of the sensorless IM drive system with vector control. The implementation is tested in experimental setup with floating point DSP used as the system controller.Originality/valueThe application of adaptive mutation with simulated annealing in the EA with hard selection for the fast, off‐line preliminary identification of the IM parameter at standstill.

Identification of Induction Motor Parameters in Industrial Drives with Artificial Neural Networks

This paper presents a new method of online estimation of the stator and rotor resistance of the induction motor in the indirect vector-controlled drive, with artificial neural networks. The back propagation algorithm is used for training of the neural networks. The error between the rotor flux linkages based on a neural network model and a voltage model is back propagated to adjust the weights of the neural network model for the rotor resistance estimation. For the stator resistance estimation, the error between the measured stator current and the estimated stator current using neural network is back propagated to adjust the weights of the neural network. The performance of the stator and rotor resistance estimators and torque and flux responses of the drive, together with these estimators, is investigated with the help of simulations for variations in the stator and rotor resistance from their nominal values. Both types of resistance are estimated experimentally, using the proposed neural network in a vector-controlled induction motor drive. Data on tracking performances of these estimators are presented. With this approach, the rotor resistance estimation was found to be insensitive to the stator resistance variations both in simulation and experiment.

Identification of the induction motor parameters at standstill using soft computing methods

PurposeThe paper sets out to deal with the off‐line identification of induction motor (IM) parameters at standstill. Determination of values of the IM parameters is essential in sensorless drives with regard to accuracy and quality of the control system.Design/methodology/approachThe presented identification method is based on the reconstruction of stator current response to the forced stator voltage step change; thus the cost function is defined in the classical form of the mean squared error between the computed and experimental data. The identification via evolutionary algorithms (EAs) is presented. The considered problem is continuous and thus a continuous version of EA is suggested as more suitable.FindingsThis approach has been shown to have several advantages over classical optimisation methods like the ability to cope with ill‐behaved problem domains exhibiting attributes such as: discontinuity, time‐variance, randomness, and, what is particularly important in this application, the ability to cope with the signals disturbed by noises. Owing to this ability the EAs could be implemented directly for the identification of IM parameters not only in simulations but also in the industrial applications for the motor control, though the electrical signals acquired from real motor and used as input data in the identification procedures are to a large extent disturbed by the electrical noises.Originality/valueTwo versions of the suggested approach are compared: the EA with hard selection and with soft selection. Both algorithms were tested in a simulation and experimental set‐up using digital signal processor for control and signal processing of the voltage inverter‐fed IM drive. Satisfactory results were obtained for the identification procedure based on the selected EA.

Pre-Estimation for Better Initial Guesses

Fast, assured convergence of iterative identification methods is important in many on-line applications, including systems that exploit indirect measurements, diagnostics, and model-based control. The transient response of a system can often be mapped directly to an estimate of the system model parameters. This initial estimate, or pre-estimate, can be evaluated in fixed time and may greatly accelerate subsequent iterative system identification procedures. A pre-estimation structure is developed and demonstrated with examples including single-tone frequency estimation and induction motor parameter identification.

Parameter identification of induction motors using stochastic optimization algorithms

In the area of optimization, applied research typically focuses on finding the best possible solution to the practical problem at hand. In contrast, a large part of basic research aims at developing novel algorithms with improved performance. In practical application, most studies employ rather simple algorithms. On the other hand, most novel algorithms are tested on a suite of artificial benchmark problems. Hence, there is a research gap between applied and basic research since the former focuses on the problems, whereas the latter concentrates on the algorithms. In this paper, we address the research gap for parameter identification of induction motors. In our experiments, we compare the performance of eight stochastic optimization algorithms on identification of two induction motors. The eight algorithms represent four main groups of algorithms currently used for numerical optimization. The four groups are: local search (LS), evolution strategies (ESs), generational evolutionary algorithms (EAs), and particle swarm optimizers (PSOs). The comparison includes a simple and an advanced algorithm from each group. From our experiments, we draw two conclusions. First, the advanced algorithms had significantly better performance compared with the simple algorithms. This underlines the importance of using advanced algorithms when approaching real-world problems. Furthermore, the improved performance justifies and motivates the development of more advanced techniques. Second, population-based stochastic optimization techniques (ESs, EAs, and PSOs) significantly outperformed the local search algorithms on both problems.

Effective identification of induction motor parameters based on fewer measurements

This paper applies genetic algorithms (GAs) to the problem of parameter identification for field orientation control (FOC) induction motors. Kron's two-axis dynamic model in per-unit system is given, and the model's parameters are estimated by a GA using the motor's dynamic response to a direct on line start. Results with different levels of measurement noise are presented for the model both in the per-unit system and in actual values. For comparison, the results of a simple random search (SRS) method under the same condition are also given. The results show that the parameter identification accuracy, the convergence speed and the practicality of the algorithm have been improved significantly by use of the model in the per-unit system. The results also show that fewer measurements are required to identify the induction motor parameters accurately.

Real-Time Rotor and Stator Resistances Estimation of an Induction Motor

In this paper, four schemes for the real-time identification of induction motor parameters are presented. The first and second schemes are for separate identification of the rotor and stator resistances while the third scheme performs simultaneous identification of these two parameters. The signals used in the identification procedure are the measured stator current, voltage command and rotor speed. The fourth scheme for the rotor resistance identification require neither measuring nor estimating of the rotor speed. All schemes are based on the MRAS identification approach and require persistent excitation conditions to be satisfied.

Least squares and genetic algorithms for parameter identification of induction motors

This paper deals with off-line parameter identification of induction motors by means of least square (LS) techniques and genetic algorithms (GA), using stator voltages, stator currents and velocity as input–output data. For analytical identification by LS algorithms, filtering of experimental data is performed by means of anticausal filters. Two models useful for identification are derived in which the products of acceleration and rotor fluxes, usually neglected, are taken into account. The GA-based identification method consists of the determination of the best parameters which match input–output behaviour of the motor. Both methods are investigated and compared by means of experiments carried out on a 1-kW induction motor.