Parameters Of Induction Motor Research Articles

Estimating the parameters of induction motors in different operating regimes from a set of data containing the rotor cage temperature

The purpose of this paper is to propose a method for accurate estimation of the equivalent circuit parameters of induction motors at different operating regimes from a set of measurement data containing the rotor cage temperature and rotor resistance at 25 $$^{\circ }$$ C. Besides these rotor quantities, the method requires the measurement of supply voltage, stator current, frequency, speed, load torque, stator winding temperature and stator winding resistance at 25 $$^{\circ }$$ C. It is assumed that stator leakage reactance, core-loss resistance, friction and windage losses and stray load losses corresponding to the nominal operation are known. The proposed method iteratively determines air gap voltage, power factor and some other parameters. The effects of temperature change, frequency change, magnetic saturation and stray load losses are taken into consideration. The application of the method is discussed within the existing examples of five different motors. Excellent accuracy is achieved using the proposed method.

A METHOD OF AUTOMATIC DETERMINATION OF THE NUMBER OF THE ELECTRICAL MOTORS SIMULTANEOUSLY WORKING IN GROUP

Purpose. Propose a method of automatic determination of the number of operating high voltage electric motors in the group of the same type based on the determination and analysis of the account data of power consumption, obtained from of electric power meters installed at the connection of motors. Results. The algorithm of the automatic determination program for the number of working in the same group of electric motors, which is based on the determination of the motor power minimum value at which it is considered on, was developed. Originality. For the first time a method of automatic determination of the number of working of the same type high-voltage motors group was proposed. Practical value. Obtained results may be used for the introduction of an automated accounting run of each motor, calculating the parameters of the equivalent induction motor or a synchronous motor.

Power Factor Improvement for Pumping Stations using Capacitor Banks

Abstract One of the fundamental problems affects the performance of pumping stations is its relative high operational cost. As three-phase induction motors are the main prime mover of pumping stations and considered the most widely used electrical motors due to their reliability, ease of maintenance. However, its major problem is the low power factor which results in high electric energy consumption. Energy will be saved when power factor is improved. The main objective of this paper is studying the power factor improvement in El sadaa Pumping Station because of its low operating efficiency which goes from 20 % to 25 %and calculating penalty, ponus and savings in each cases. The correction is achieved by the addition of capacitor banks in parallel with the connected motor circuits and can be applied to the starter, applied at the switchboard or the distribution panel. A model of this station is created using MatlabTM Simulink. Then the determination of induction motor parameters is performed. The station model is discussed. From the simulation results, the power factor enhancement of the pumping station is highlighted.

Online Adaptation of Rotor Resistance based on Sliding Mode Observer with Backstepping Control of A Five-Phase Induction Motor Drives

Multiphase electric drives have been developed due to numerous advantages offered by those machines when it compared with the conventional three-phase machines. Multiphase motor drives are considered for applications, where the reduction of power per phase for both motor and inverter and high reliability are required. High performance control techniques are developed for multi-phase drives. The performances of the high performance controller and flux observers may be degraded during the operation. Since the parameters of Induction Motor (IM) varies continuously due to temperature variation and heating. Thus it is significantly important that the value of rotor resistance is continuously observed online and adapted by the control algorithm in order to avoid detuning effects. The efficiency and performance of an induction motor drive system can be improved by online observation of the critical parameters, such as the rotor resistance and stator resistance. Among the parameters of IM, rotor resistance is a decisive one for flux estimation, and also the stator resistance becomes critical in the low-speed operation condition. This paper presents a new online estimation method for the rotor resistance of the IM for sliding mode observer. This method generally based on theories of variable structure and is useful in order to adjust online unknown parameters (load torque and rotor resistance). The presented non-linear compensator afford a voltage inputs on the articulation of stator current and rotor speed measurements, and engender an estimates for the unknown parameters simultaneously, the non-measurable state variables (rotor flux and derivatives of the stator current and voltage) that converge to the corresponding true values. Under the persistent excitation condition, the proposed method estimates the actual value of rotor resistance, which guarantees the exact estimation of the rotor flux. Non-linear Backstepping control and adaptive sliding mode observer of a five-phase induction motor drive is presented. The accuracy and validity of the method is verified by MATLAB simulation model.

Induction Motor Parameter Estimation Using Sparse Grid Optimization Algorithm

Inaccurate motor parameters can lead to an inefficient motor control. Although several motor estimation methods have been utilized to estimate motor parameters, it is still challenging to ensure a good level of confidence in the estimation. In this paper, we propose a novel offline induction motor parameter estimation method based on sparse grid optimization algorithm. The estimation is achieved by matching the response of machines mathematical model with recorded stator current and voltage signals. This approach is noninvasive as it uses external measurements, resulting in reduced system complexity and cost. A globally optimal point was found by sampling on the sparse grid, which was created using the hyperbolic cross points and additional heuristics. This has resulted in reducing the total number of search points, and provided the best match between the mathematical model and measurement data. The estimated motor parameters can be further refined by using any local search method. The experimental results indicate a very good agreement between estimated values and reference values.

A Novel Control Strategy for Online Harmonic Compensation in Parametrically Unbalanced Induction Motor

Inverter-fed motor drive operating an unbalanced induction motor (IM) has high harmonic content which inflicts large torque ripple on the load. Dead short circuit and incipient or partial short circuit in motor stator windings lead to asymmetry in the machine parameters. Consequently, an imbalance in the voltage supply worsens the condition deteriorating the optimal performance of the drive-based motor due to the injection of both increased time and spatial harmonics. It is of primary importance that these discrepancies are taken care of while modeling a more fault tolerant, reduced harmonics drive system. This paper proposes a novel control strategy to minimize torque ripple by considering the time harmonics produced due to imbalance in inverter voltage and parameters of the faulty IM, and the estimated space harmonics from the measured magnetic flux density in a transient magnetic phenomenon. The proposed control strategy has been implemented on an unbalanced aluminum-rotor IM with online monitoring of unhealthy conditions and feeding it to the harmonic compensation block of the drive system.

Induction Motor Stator Fault Detection by a Condition Monitoring Scheme Based on Parameter Estimation Algorithms

This article presents a simple, low-cost, and effective method for the early diagnosis of stator short-circuit faults. The approach relies on the combination of an induction motor mathematical model and parameter estimation algorithm. The kernel of the method is the efficient search for the characteristic parameters that indicate stator short-circuit faults. However, the non-linearity of a machine model may imply multiple local minima of an objective function implemented in the estimation algorithm. Taking this into consideration, the suitability of two industry-proven optimization algorithms (pattern search algorithm and genetic algorithm) as applied in the proposed condition monitoring method was investigated. Experimental results show that the proposed diagnosis method is capable of detecting stator short-circuit faults and estimating level and location of faults. The study also indicates that the proposed method is robust to motor parameters offset and unbalanced voltage supply. Application of the pattern search algorithm is suitable for a continuous monitoring system, where the previous result can be used as starting point of the new search. The genetic algorithm requires longer computation time and is suitable for the offline diagnostic system. It is not sensitive to the starting point, and achieving global solution is guaranteed.

Analysis and Test of the Sensorless Capability of Induction Motors With Created Saliency

This paper presents various experimental tests on the sensorless capability of induction motors (IM). An intentionally created saliency is introduced in the rotor so as to allow the rotor position to be estimated by means of a high-frequency (HF) injected signal in the stator winding. Experimental measurements are carried out on three prototypes, two with saliency and the third without saliency, as reference. The critical aspects in the prototype realization are discussed in this paper. Suggestion on how to solve them from the machine design point of view is included. The rotor saliency has been measured by means of an HF injection. Then, the steady-state performance has been measured, together with the mechanical characteristic of the three prototypes. FE analysis is used to predict the variation of the IM parameters with rotor position. The results are compared with those achieved from measurements.

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.

Analysis of influence of rotor design on high speed small power induction motors parameters

This paper presents the analysis of the influence of rotor design: massive or cage on high speed small power induction motors parameters. For these analyzes were used both field-circuit method and circuit analytical method. As could be predicted massive rotor motor is characterized by higher losses and thus less efficiency than the squirrel cage motor, especially with copper cage. Despite the massive motors are easier to manufacture and the specific construction may be used.

Parameters estimation of squirrel-cage induction motors using ANN and ANFIS

In the transient behavior analysis of a squirrel-cage induction motor, the parameters of the single-cage and double-cage models are studied. These parameters are usually hard to obtain. This paper presents two new methods to predict the induction motor parameters in the single-cage and double-cage models based on artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). For this purpose, the experimental data (manufacturer data) of 20 induction motors with the different power are used. The experimental data are including of the starting torque and current, maximum torque, full load sleep, efficiency, rated active power and reactive power. The obtained results from the proposed ANN and ANFIS models are compared with each other and with the experimental data, which show a good agreement between the predicted values and the experimental data. But the proposed ANFIS model is more accurate than the proposed ANN model.

Review on model reference adaptive system for sensorless vector control of induction motor drives

The most frequently used electrical machine in various modern high-performance drive applications is the induction motor (IM), especially its squirrel cage type rotor counterpart. The high-precision, efficient control of sensorless IM drives throughout its operating range demands an exact knowledge of a few of the IM parameters. The stator resistance, rotor resistances are such important IM parameters whose variations may lead to improper estimation of speed over the whole operating range in sensorless drives. Numerous methods for the estimation of speed of high-performance sensorless IM drives taking into account the effect of parameter variations have been developed. This paper aims at providing a review of the major model reference adaptive system (MRAS) based techniques applied for the estimation of speed of such sensorless drives. This paper is illustrated throughout with relevant mathematical equations, experimental and simulation results, associated to different MRAS-based sensorless vector control of the IM drive.

Squirrel-Cage Induction Motor Parameter Estimation Using a Variable Frequency Test

This paper presents a method for squirrel-cage induction motor parameter estimation using a phase-to-phase standstill variable frequency test. The measured resistance and reactance at different frequencies are the data of the minimization error function to be minimized for single- and double-cage model parameters estimation. It is observed that the single-cage model is unable to fit the measured data for frequencies above several tenths of Hertz, whereas the double-cage model fits the measured data accurately in all the frequency ranges (from 0 to 150 Hz). The single- and double-cage estimated parameters are validated by comparison with data from two additional tests: 1) steady-state torque and current measurement test at different speeds; and 2) dynamic free-acceleration test. Again, the agreement between measured and predicted torque (in the first test) and current (in both tests) is satisfactory only for the double-cage model.

Performance Analysis of a DTC and SVM Based Field-Orientation Control Induction Motor Drive

<p>This study presents a performance analysis of two most popular control strategies for Induction Motor (IM) drives: direct torque control (DTC) and space vector modulation (SVM) strategies. The performance analysis is done by applying field-orientation control (FOC) technique because of its good dynamic response. The theoretical principle, simulation results are discussed to study the dynamic performances of the drive system for individual control strategies using actual parameters of induction motor. A closed loop PI controller scheme has been used. The main purpose of this study is to minimize ripple in torque response curve and to achieve quick speed response as well as to investigate the condition for optimum performance of induction motor drive. Depending on the simulation results this study also presents a detailed comparison between direct torque control and space vector modulation based field-orientation control method for the induction motor drive.</p>

A Method to Calculate Performance and Circuit Parameters of Linear Induction Motors Using Simple Two-Phase Model

A Method to Calculate Performance and Circuit Parameters of Linear Induction Motors Using Simple Two-Phase Model

Estimation of induction motor parameters: an overview

The parameters of induction motor depends on various factors such as: machine internal state, machine ageing, magnetic saturation, operating conditions, the coupling effect between the internal system and external system. The paper deals with an overview of parameter estimation of three phase induction motor using different soft computing techniques. The soft computing techniques which are considered in the paper are fuzzy system, artificial neural network (ANN), Neuro-Fuzzy, genetic algorithms (GA) and particle swarm optimization (PSO). It is observed that the estimated parameter using soft computing techniques were much closer to actual value.

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.

Induction Motor Drive Parameters Identification Applying Difference Schemes

The authors have developed and tested by mathematical modeling the method of the induction motor parameters identification based on the difference schemes. When obtaining a difference equation system, the total delay time was taken to be equal to a quarter of a stator voltage period. The nonlinear predictive filtration was used to determine the expected value. The authors determined the estimators of rotor active resistance reduced to the stator; the rotor winding equivalent inductance; the stator winding equivalent inductance, the resulting inductance conditioned by the magnetic flux in the machine air gap; equivalent time constant for rotor circuit in the squirrel cage induction motor. The root mean square values of relative errors of parameters estimations total less than 4 %.

Direct Torque Control of Induction Motors Based on Discrete Space Vector Modulation Using Adaptive Sliding Mode Control

—Direct torque control (DTC) with space vector modulation (SVM) ensures constant switching frequency and high-performance operation, both in the steady state and under transient conditions. This article is devoted to the presentation of a comparison study between two DTC-SVM approaches for electric propulsion systems applications: (i) DTC-SVM with controllers, and (ii) DTC-SVM with sliding mode controllers. Following the formulation of both DTC-SVM approaches, their implementation in the Matlab-Simulink environment has been treated. The transient behavior as well as the steady state features of the drive system under both approaches are compared and commented. The robustness with respect to parameter variations of the induction motor drive is also tested. Simulation results show the effectiveness and the robustness of the proposed DTC-SVM adaptive sliding mode control against variations of induction motor parameters.

Soft Computing Techniques for Identification of an Asynchronous Machine Parameters

This manuscript discusses about the Parameter estimation of Induction motor by utilizing the soft computing methodologies that is by using evolutionary algorithms such as Genetic algorithm, Particle swarm optimization, Artificial immune algorithm to overcome the difficulties in the conventional method where we calculating the per phase equivalent circuit parameters from the No load test and Blocked rotor test which compromises in result in terms of accuracy of the result and also evaluated the accuracy of the different algorithm in estimating the parameters of the induction motor.