Medium Size Induction Motors Research Articles

Modified Efficiency Estimation Tool for Three-Phase Induction Motors

This article presents a modified version of novel algorithms the authors previously proposed to help North America electric motor service centers to evaluate their repaired, rewound, or any existing motors for efficiency. Due to the dependency of the algorithms on a large 60 Hz induction motor database to produce reliable and accurate results, a software was needed and developed to incorporate both the algorithms and the data to create a useful single affordable tool for the North America electric motor repair industry. To eliminate the need for the software, and allow an open-source implementation, the direct dependency of the efficiency algorithms on the motor test data are eliminated in this paper. This allows the two techniques presented here to be used directly by engineers in the motor repair industry in North America. To achieve this goal, modifications to the two original algorithms are required and proposed in this paper. The modifications include the implementation of a new stray-load loss formula and the IEEE Std 112 hot temperature formula. The modified algorithms are validated by testing 28 new and aged, small- and medium-sized induction motors in the range of 1-500 hp. The modified techniques demonstrate a high level of accuracy.

A Novel In Situ Efficiency Estimation Algorithm for Three-Phase Induction Motors Operating With Distorted Unbalanced Voltages

This paper presents a novel algorithm for in situ efficiency estimation for induction motors operating with unbalanced distorted voltages. The proposed technique utilizes the genetic algorithm, IEEE form F2-method F1 calculations, large motor test database, and a novel stray-load loss formula. The technique is evaluated by testing three small- and medium-sized induction motors with different combinations of voltage unbalance and total harmonic distortion. The results showed acceptable accuracy. The repeatability and usability of the technique with balanced sinusoidal voltages are also validated. The technique may be used as a potential industrial tool that can help derate induction motors in the presence of voltage unbalance and harmonics.

A New Stray-Load Loss Formula for Small and Medium-Sized Induction Motors

This paper proposes a new stray-load loss (SLL) formula for small and medium induction motors (IMs) based on tests data of a 182, 60 Hz IMs in the range of 1–500 hp (0.75–375 kW). They are all tested in accordance with IEEE Std 112-Method B. The proposed formula is validated by recalculating the efficiency of the same number of motors by using the proposed formula, as well as the IEEE Std 112 and the IEC 60034-2-1 standards. Another validation was done on testing 17 additional IMs that are independent of the 182-motor data. In both validations, the new formula demonstrates better accuracy. This formula shows the potential to replace the existing SLL estimation formula for this horsepower range.

A Novel Technique for <italic>In Situ</italic> Efficiency Estimation of Three-Phase IM Operating With Unbalanced Voltages

Three-phase voltages of any power supply cannot ever be identical due to many technical reasons. Unbalanced voltages severely impact the performance of induction motors. They also increase the difficulty of the process of efficiency estimation. This paper presents a novel algorithm for in situ full-load efficiency estimation of induction motors operating under unbalanced voltages. The proposed technique utilizes the genetic algorithm (GA), IEEE Form F2-Method F1 calculations, and pretested motors’ data. The method requires a dc test, full-load rms voltages, currents, input power, and speed measurement. The proposed algorithm uses a sensorless speed measurement. The technique is evaluated by testing four small- and medium-sized induction motors with different voltage unbalance conditions. The results show acceptable accuracy.

Speed control of induction motors using neuro-fuzzy dynamic sliding mode control

This paper presents a novel approach for robust speed control of medium size induction motors using neuro-fuzzy dynamic sliding mode control. First, a simple dynamic model of induction motors is introduced. Then, a conventional sliding mode control is presented. To reduce the chattering phenomenon, dynamic sliding mode control is designed using a secondary PID-type sliding surface. Moreover, in order to eliminate the tedious trial and error procedure in choosing a proper uncertainty upper bound, uncertainties have been estimated using a neuro-fuzzy system. The online training of the neuro-fuzzy dynamic sliding mode control is based on the adaptation law derived from the stability analysis. In addition, the reconstruction error of the neuro-fuzzy system is compensated to guarantee the asymptotic convergence of the speed tracking error. Simulation results verify that the neuro-fuzzy dynamic sliding mode control is robust against various uncertainties including parametric variations, external load disturbance, unmodeled dynamics and input voltage disturbances.

Design of optimal MLP and RBF neural network classifier for fault diagnosis of three phase induction motor

Induction motors are subject to incipient faults which, if undetected, may lead to serious machine failures. Now, it is well understood that incipient fault detection methods applied in large induction motors (e.g., greater than 100 hp) are often too costly or may not be feasible for use on small and medium size induction motors. From the scrupulous review of the related work, it is observed that neuro-fuzzy and neural network (NN) based fault detection schemes are performed only for large machines and they are not only expensive but also complex. Our NN based incipient fault detection method avoid the problems associated with traditional incipient fault detection schemes by employing more readily available information such as stator current. This paper develops inexpensive, reliable and non-invasive NN based incipient fault detection scheme for small and medium sized induction motors. Detailed design procedure for achieving the optimal NN model and sensitivity analysis for dimensionality reduction is proposed. Overall, 13 statistical parameters are used as feature space to achieve the desired classification. MLP and RBF NN models are designed and verified for optimal performance in fault identification on experimental data set of custom designed 2 hp, three phase 50 Hz induction motor.

Induction Machine Fault Detection Using Generalized Feed Forward Neural Network

Industrial motors are subject to incipient faults which, if undetected, can lead to motor failure. The necessity of incipient fault detection can be justified by safety and economical reasons. The technology of artificial neural networks has been successfully used to solve the motor incipient fault detection problem. This paper develops inexpensive, reliable, and noninvasive NN based incipient fault detection scheme for small and medium sized induction motors. Detailed design procedure for achieving the optimal NN model and Principal Component Analysis for dimensionality reduction is proposed. Overall thirteen statistical parameters are used as feature space to achieve the desired classification. GFFD NN model is designed and verified for optimal performance in fault identification on experimental data set of custom designed 2 HP, three phase 50 Hz induction motor.

Induction machine fault detection using support vector machine based classifier

Industrial motors are subject to various faults which, if unnoticed, can lead to motor failure. The necessity of incipient fault detection can be justified by safety and economical reasons. The technology of artificial neural networks has been successfully used to solve the motor fault detection problem. This paper develops inexpensive, reliable, and noninvasive NN based fault detection scheme for small and medium sized induction motors. Detailed design procedure for achieving the optimal NN model and Principal Component Analysis for dimensionality reduction is proposed. Overall thirteen statistical parameters are used as feature space to achieve the desired classification. Generalized Feed Forward (GFFDNN) and Support Vector Machine (SVM) NN models are designed and verified for optimal performance in fault identification on experimental data set of custom designed 2 HP, three phase 50 Hz induction motor.

Induction Motor Bearing Failure Detection and Diagnosis: Park and Concordia Transform Approaches Comparative Study

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> This paper deals with the problem of bearing failure detection and diagnosis in induction motors. Indeed, bearing deterioration is now the main cause of induction motor rotor failures. In this context, two fault detection and diagnosis techniques, namely the Park transform approach and the Concordia transform, are briefly presented and compared. Experimental tests, on a 0.75 kW two-pole induction motor with artificial bearing damage, outline the main features of the aforementioned approaches for small- and medium-size induction motors bearing failure detection and/or diagnosis. </para>

Aspects of voltage responses of induction motor loads

In this paper, the voltage dynamics of induction motor loads up to some ten seconds are discussed in terms of a linearized model. It is generally recognized that the dynamics of a power system load mainly originate from the induction motor dynamics constituting the load. Aspects of the voltage dynamics of induction motors are analyzed with respect to machine constants. The results show that small and medium-sized induction motors have dynamics that can be well approximated at the first order, thus partly accounting for the fact that most feeders indicate first-order dynamics.

PRELIMINARY CONSIDERATIONS ABOUT THE ADOPTION OF UNCONVENTIONAL MAGNETIC MATERIALS AND STRUCTURES FOR INDUCTION MOTORS

ABSTRACT The activity carried out and presented in this paper deals with the opportunity of utilizing high quality magnetic materials, such as amorphous, microcristalline or cubic texture materials for the magnetic structure of medium size induction motors. In order to evaluate the benefits introduced by these materials for reducing the iron losses in a.c. motors, digital preliminary computations have been carried out considering the characteristic of such materials (Induction versus specific losses) and the obtained results for each solution have been compared. At the present, however, the; above mentioned materials are costly, not easy to handle and to assemble and therefore a practical solution with grain oriented steel in a crossed structure obtained alternating sheets with orthogonal grain orientation, in order to simulate the cubic texture material (i-oriented material) has been proposed. To verify the obtained theoretical results with the digital computations, a prototype of 20 Hp, 380 V, three phases induction motor with this non conventional magnetic structure has been designed and built. The tests on the prototype to evaluate the iron losses are in good agreement with the digital simulation results.

A neural network approach to real-time condition monitoring of induction motors

A neural network-based incipient fault detector for small and medium-size induction motors is developed. The detector avoids the problems associated with traditional incipient fault detection schemes by employing more readily available information such as rotor speed and stator current. The neural network design is evaluated in real time in the laboratory on a 3/4 hp permanent magnet induction motor. The results of this evaluation indicate that the neural-network-based incipient fault detector provides a satisfactory level of accuracy, greater than 95%, which is suitable for real-world applications.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>