Low-voltage Induction Motors Research Articles

Localized Bearing Fault Analysis for Different Induction Machine Start-Up Modes via Vibration Time–Frequency Envelope Spectrum

Bearings are the most vulnerable component in low-voltage induction motors from a maintenance standpoint. Vibration monitoring is the benchmark technique for identifying mechanical faults in rotating machinery, including the diagnosis of bearing defects. The study of different bearing fault phenomena under induction motor transient conditions offers interesting capabilities to enhance classic fault detection techniques. This study analyzes the low-frequency localized bearing fault signatures in both the inner and outer races during the start-up and steady-state operation of inverter-fed and line-started induction motors. For this aim, the classic vibration envelope spectrum technique is explored in the time–frequency domain by using a simple, resampling-free, Short Time Fourier Transform (STFT) and a band-pass filtering stage. The vibration data are acquired in the motor housing in the radial direction for different load points. In addition, two different localized defect sizes are considered to explore the influence of the defect width. The analysis of extracted low-frequency characteristic frequencies conducted in this study demonstrates the feasibility of detecting early-stage localized bearing defects in induction motors across various operating conditions and actuation modes.

저압 유도전동기 부하 변동에 따른 역률 보상에 관한 연구

저압 유도전동기 부하 변동에 따른 역률 보상에 관한 연구

On the frequency dependence of the PDIV in twisted pair magnet wire analogy in humid air

The partial discharge inception voltage (PDIV) of low-voltage induction motor turn-turn insulation analogy is investigated for variable relative humidity (RH) of the surrounding air (temperature and pressure are kept constant close to their ambient values). Three materials, perfluoralkoxy alkane, sealed and unsealed alumina (UA), are employed as the dielectric coating (insulation) of test samples. Each material’s dielectric properties change differently with RH—of these, relative permittivity is regarded as the most crucial, as it was previously proven to be, along with the coating’s thickness, the key factor in determining the PDIV in dry air conditions. To further vary the value of permittivity and thus provide larger data sets for the verification study, the frequency of the excitation voltage is also varied between 1 Hz and 50 kHz. The obtained experimental data are compared against the PDIV predictions obtained by a breakdown model based on the reduced thickness of the coating (ratio of the coating thickness to its relative permittivity). Satisfying agreement between the measured and predicted PDIV are obtained for the first two materials, whereas significant discrepancies are seen for the UA material. For this case, surface conductivity is introduced into the prediction model and much better fits to the experimental data are obtained for a suitable value of surface resistance. It is hence demonstrated that the model is able to satisfactorily predict the PDIV for three substantially different materials when the RH of the surrounding air is varied between 10% – 80% .

Effect of Ripple Control on Induction Motors

One method for the remote management of electrical equipment is ripple control (RC), based on the injection of voltage interharmonics into the power network to transmit information. The disadvantage of this method is its negative impact on energy consumers, such as light sources, speakers, and devices counting zero crossings. This study investigates the effect of RC on low-voltage induction motors through the use of experimental and finite element methods. The results show that the provisions concerning RC included in the European Standard EN 50160 Voltage Characteristics of Electricity Supplied by Public Distribution Network are imprecise, failing to protect induction motors against excessive vibration.

ANALYTICAL CALCULATION OF SHAFT CURRENTS IN THE MOTORS WITH HOMOGENEOUS YOKE

Literature dealing with the matter of shaft currents mostly elaborates on the methods of motor diagnostics which only observe and analyse harmonics frequencies characteristic for shaft currents. The analytic calculation of the amplitudes of those currents has not been analysed so far for homogenous yoke motors and, in order for correct protection measures to be undertaken, it is necessary to determine exactly the parameters which influence those currents. Shaft currents are an important component of the currents which electrically damage the bearings and usually represent the dominant contribution. This is especially important for low-voltage induction motors which, according to IEC’s data, are the motors with the widest application range in the industry. In this work, the analytic calculation of shaft currents is explained for those motors.

Effect of different core materials in very low voltage induction motors for electric vehicle

The use of squirrel cage induction motor for electric vehicle (EV) has been increasingly popular than permanent magnet and brushless motors due to their independence on rare materials. However, its performance is significantly affected by the core materials. In this research, induction motors performance with various core materials (M19_24G, Arnon7, and nickel steel carpenter) are studied in very low voltage. Three phases, 50 Hz, 5 HP, 48 V induction motor were used as the propulsion force testbed applied for a golf cart EV. The aims are to identify loss distribution according to core materials and compare power density and cost. The design process firstly determines the motor specifications, then calculates the dimensions, windings, stator, and rotor slots using MATLAB. The parameters obtained are used as inputs to ANSYS Maxwell to calculate induction motor performance. Finally, the design simulations are carried out on RMxprt and 2D transient software to determine the loss characteristics of core materials. It is found that the stator winding dominates the loss distribution. Winding losses have accounted for 52-55 % of the total loss, followed by rotor winding losses around 25-27 % and losses in the core around 1-7 %. Based on the three materials tested, nickel steel carpenter and M19_24G attain the highest efficiency with 83.27 % and 83.10 %, respectively, while M19_24G and Arnon7 possess the highest power density with 0.37 kW/kg and 0.38 kW/kg whereas, in term of production cost, the Arnon7 is the lowest.

Low Voltage Induction Motor Traction Drive Self-Commissioning Technique with the Advanced Measured Signal Processing Procedure

In this paper, the enhanced auto-tuning technique based on the injection of two sinusoidal test signals of different frequencies applicable on the low voltage induction motor self-commissioning process is presented. The main feature of the proposed technique resides in the advanced signal processing of measured IM voltage and current signals based on the cascaded delay signal cancelation structure. This processing algorithm enables the filtering of the symmetry-related fundamental harmonic from the non-symmetrical test signal excitation typical for the self-commissioning process. Based upon the steady-state response from the proposed filtering block, the simple yet effective calculation method derives the complete parameter set of the IM equivalent circuit. The technique is validated through the variety of computer simulations and experimental tests on the digitally controlled low voltage IM traction drive.

Risk assessment of low voltage motors based on PD measurements and insulation diagnostics

Partial discharge (PD) diagnostics is a reliable technique for the health assessment of electrical motors. This paper presents a unique methodology for the failure risk assessment of the low voltage motors containing various insulation defects based on PD measurements. The severity of PD in the motors increases with the time during their operation due to electrical and environmental stresses. In this work, several artificial PD defects have been created in low voltage induction motors under laboratory conditions. Accordingly, the characteristic parameters of PD measured in the laboratory for the artificial defects are evaluated to identify their impact on the resulting degradation of the insulation. Furthermore, the classification of these defects has been carried out based on cumulative energy function using K-mean clustering algorithm followed by the estimation of insulation lifetime. In this regard, Weibull distribution has been employed to quantify the probability of failure and risk evaluation corresponding to the severity of PD defects. The proposed risk assessment framework may be utilized to support asset managers in scheduling the regular maintenance activities and assist them in decision making about the type of actions required to eliminate the latent threat.

Online Slight Inter-Turn Short-Circuit Fault Diagnosis Using the Distortion Ratio of Load Current in a Low-Voltage Induction Motor

Online Slight Inter-Turn Short-Circuit Fault Diagnosis Using the Distortion Ratio of Load Current in a Low-Voltage Induction Motor

Model Predictive Control Based Field-Weakening Strategy for Traction EV Used Induction Motor

The field-weakening scheme is generally adopted for traction motors to achieve a wider speed range where the common proportional and integration compensator is needed to regulate the flux-producing current. However, the regulator performance deteriorates due to dc-link voltage disturbances and motor parameter nonlinearities in the different speed regions. To solve this issue, a model predictive control (MPC) based field-weakening algorithm is proposed for a traction electric vehicle using a low-voltage induction motor. The augmented prediction state relationship between stator voltage and flux-producing current is established for motor current control. The steady-state error can be eliminated with an integrator embedded within the augmented equation. The overall closed-loop control is presented where the system eigenvalues are adjusted in real time for various speed regions, and accordingly, the controller performance can be evaluated with the amplitude of the eigenvalues. Moreover, the weight coefficient in the cost function can be adjusted corresponding to speed variation for guaranteed motor control performance. The simulation and experimental results are provided to verify the proposed MPC-based field-weakening algorithm.

Influence of Die-Cast Rotor Fill Factor on the Starting Performance of Induction Machines

One of the most important requirements for low-voltage induction motor designers is to meet the customer-specified starting characteristics and international regulations on efficiency. Although porosity in die-cast rotors degrade motor performance, the fill factor (FF) of the rotor bar and end ring are usually assumed to be 100 in the design stage. If the influence of FF is not properly taken into account in the design, it results in an inaccurate prediction of the starting performance. In this paper, the influence of die-cast rotor FF on the starting torque is investigated based on the FF and porosity distributions measured from actual aluminum die-cast rotors. A simplified 3-D finite-element analysis model that accounts for the porosity distribution in bars and end rings for different FFs is presented for predicting the starting characteristics. The effectiveness of the proposed method is verified on a 440 V, 15 kW induction motor prototype with rotors with FF of 93% and 67%. The results show that porosity has a significant influence on the starting torque, and must be taken into account for reliable prediction of motor performance for design optimization.

Quantifying Effects of Cutting and Welding on Magnetic Properties of Electrical Steels

The magnetic properties, namely the iron losses and the relative permeability, of SiFe electrical steel laminations after guillotine shearing and cutting by means of fiber and CO 2 lasers are studied. The magnetic measurements are conducted on the Epstein frame for lamination strips with 1, 2, and 3 additional cutting edges along their length, in order to increase the cutting effect and the characterization data. The quantified effects of manufacturing (cutting and welding) are presented for three different material grades: M270-50A, M400-50A, and a nonoriented electrical steel of gauge 0.2 mm called NO20. Usage of the Epstein frame method allows any electrical steel company to reproduce the measurements for any specific grade. Data presented in normalized values facilitate utilization of the presented results and comparison between materials. An original model that incorporates the cutting effect considering homogenously damaged areas is developed and implemented in a finite-element method-based motor design software. Its originality is that it includes dependence on the geometry, included in the material magnetic properties. Simulations made for an industrial low voltage induction motor indicate a more than 15% increase in the iron losses compared with a model that does not consider the mechanical cutting effect. In the case of laser cutting, this increase reaches 30% to 50%, depending on laser settings. These relatively large increases of iron losses justify the implementation of the effect of cutting in industrial finite-element design tools, using a method that does not increase the simulation time.

Frequency spectrum investigation and analytical diagnosis method for turn-to-turn short-circuit insulation failure in stator winding of low voltage induction motor

Low-voltage induction motors are widely used in the factories and are becoming very essential components for the industrial process. Improper diagnosis and condition monitoring of induction motor not only leads to high repair expenses but also cause extraordinary financial losses due to their unexpected downtime. In general, turn-to-turn short-circuit failure of stator winding due to their deterioration or damage of electrical insulation is one of the most feasible failures in motor drive systems. This paper proposes a diagnostic method based on amplitudes of characteristic frequency components of load current as features. These features are extracted by close analysis of frequency spectrum and its dependence on load current variation. This method has the advantage of low cost and short data processing time. Also to enhance the accuracy of diagnosis, a novel diagnostic method is proposed with the aid of Support Vector Machine. It is found that the proposed diagnosis method using the features and Support Vector Machine enables the detection of a slight insulation failure like one turn-to-turn short-circuit with practically accepted accuracy.

Induction Motor Relaying Scheme for External Faults Detection and Classification Using Subtractive Clustering Based Sugeno Fuzzy Inference System

Conventional thermal relays generally suffer from inaccurate induction motor external faults identification. Recent developments in diagnosis systems have led to the scope of radically different diagnosis strategies based on artificial intelligence techniques. This study use fuzzy logic as a software tool and relay logic for external faults identification of an induction motor. In this study, a subtractive clustering based Sugeno-type fuzzy inference system is utilized to classify five external faults and the normal condition of a low-voltage three-phase induction motor using simulation and practical data sets. Five external faults- overload, over-voltage, under-voltage, single-phasing, and voltage unbalance-along with normal conditions are simulated on an induction motor over a wide range of operating voltage and load torque; sampled three-phase RMS voltages and currents are used as input feature vectors to train the fuzzy inference system. Average total classification accuracy and average root means square error obtained with ten-times random sub-sampling cross-validation of test data sets are used to find the best accurate and generalized fuzzy inference system configuration among different cluster radius fuzzy inference systems for both simulation and real-time data sets. This method accurately detects external faults with 94.37% average total classification accuracy for practical test data sets.

Turn-to-turn insulation failure diagnosis of stator winding of low voltage induction motor with the aid of support vector machine

Turn-to-turn short-circuit of stator winding due to deterioration of electrical insulation is one of the most probable faults in motor drive systems. This paper proposes two diagnostic methods to detect the fault based on Support Vector Machine. In the first method, magnitude and phase of load currents at fixed rotating speeds are considered as features reflecting winding condition. It is found that the method is effective particularly in slight insulation failure like one turn-to-turn short-circuit. Classification of winding condition, i.e. specification of number of short-circuit turns, is also achieved by this method. In the second method, only magnitude of load currents is treated as feature aiming at diagnosis available when load current varies. The method also has the advantage of low cost and short data processing time. It is found that two turn-to-turn short-circuit can be diagnosed with practically acceptable accuracy. An appropriate choice of the two methods should be recommended depending on the goal as well as circumstance of diagnosis.

Fuzzy Logic-based Electrothermal Life Model for Inverter-fed Induction Motor Insulation

Pulse width modulated adjustable speed drives used in industries lead to significant advantages in terms of performance, size, and efficiency. But, the output voltage and the current waveforms become non-sinusoidal leading to an increase in voltage and thermal stresses. This results into accelerated insulation aging and premature failure of the motors. With multiple stresses, the life models for insulating materials become complex and ambiguous. This article presents the fuzzy logic application to derive an electrothermal life model to investigate the synergic effects of voltage and thermal stresses on intrinsic aging of inverter-fed induction motor insulation. Three parameters, viz. voltage stress factor, waveform slope stress factor, and spike frequency factor, are proposed to describe the insulation stresses for pulse width modulated voltages. These parameters are computed from the experimental results at different switching frequencies and are used in fuzzy logic-based life estimation algorithms. The results of the fuzzy logic life model are verified by performing the accelerated aging test with the same pulse width modulated voltages on paper insulation. An electrothermal life model is derived from the fuzzy logic results, which can be directly used for the life estimation of any general purpose low-voltage inverter-fed induction motor insulation.

Aging Tests on Nanostructured Enamels for Winding Wire Insulation

Early failures due to stator insulation breakdown are still frequently experienced in industrial applications where low-voltage induction motors are fed by adjustable speed drives. Such events are caused by the quick erosion of organic insulating materials used in Type I random wire wound windings when the inception of partial discharges (PDs) is triggered by the severe electrical stress produced by fast switching electronic converters. The present experimental work provides some interesting results about the medium-long-term behavior of nanostructured organic-inorganic enamel-based compounds in the presence of PDs. The outcome evidences that polymeric-nanocomposite enamels may provide a strong enhancement of the duration; thus, they could be proficiently adopted as the stator winding primary insulation.

Low-Intrusive Approach for Parameters Estimation of Induction Motor Using Adaptive Optimal Design Algorithm Based on On-Site Measurements

This paper presents a novel low-intrusive method for estimating the equivalent circuit parameters of induction motor which focuses on a simple means without expensive testing. This paper describes the use of on-site measurements data incorporated with the adaptive optimal design algorithm based on optimization process in order to estimate the best possible set of motor parameters for determining the mechanical output at each measured load. This algorithm includes the feasible boundary of design variables process for evaluating the suitable boundaries of motor parameters depending on its power rating. This proposed method has been verified by the experimental results from four low-voltage TEFC induction motors rated at 2.2, 4.0, 5.5 and 7.5 kW. The results indicate that the agreement between the estimated parameters and the calculated parameters obtained from the standard equivalent circuit method validates the proposed method over a wide range of load conditions. For comparison, the results of estimated mechanical output of actual motors are illustrated. Therefore, this simple procedure can provide the useful information for selecting the motor most suitable for its application

Diagnosis of Short-Circuit Faults in Stator Winding inside Low-Voltage Induction Motor Using Impulse Voltage Test

Short-circuit faults in windings due to the deterioration of insulation is one of the most common faults in motor drive systems. An easy and effective fault diagnosis method is urgently required to ensure a highly reliable operation. This paper proposes a novel method for the diagnosis of short-circuit faults in stator winding inside a low-voltage induction motor without removing the rotor, by performing an impulse voltage test. As the rotor does not need to be removed from the motor in the novel diagnosis method, the method can be applied for practical use. In this study, first, several impulse voltage tests are carried out on the stator windings of motors. Second, the values of two features that represent the characteristics corresponding to the condition of the motor are calculated, and it is found that the shape of the feature distribution does not depend on the rotor position but on the condition of the winding. Third, the distance between the feature distribution for the healthy motor and features obtained from a target motor is calculated. On the basis of this distance, the quality of stator winding inside the induction motor is determined. The effectiveness of the proposed diagnosis method is verified by performing experiments that involve several motors with healthy and faulty windings.

A Method for Indentifying Broken Rotor Bar and Stator Winding Fault in a Low-voltage Squirrel-cage Induction Motor Using Radial Flux Sensor

In this paper, a method for detecting broken rotor bar and stator winding fault in a low voltage squirrel-case induction motor using an air-gap flux variation analysis is proposed to develop a simple and low cost diagnosis technique. To measure the leakage flux in radial direction, a radial flux sensor is designed as a search coil and installed between stator slots. The proposed method is able to identify two kinds of motor faults by calculating load condition of motors and monitoring abnormal signals those are related with motor faults. Experimental results obtained on 7.5kW three-phase squirrel-cage induction motors are discussed to verify the performance of the proposed method. Induction motors are widely used electrical machines, for their simplicity of construction and reliability. However, they are subject to failures those may be due to production processes or operating conditions. These unexpected failures cause severe damages in industrial processes. Motor reliability working group have announced that percentage failure in induction motors is typically: stator related (38%), rotor related (10%), bearing related (40%), and others (12%) (1). Recently, many researchers have studied diagnosis techniques to predict motor failures at their incipient stage and decide proper replacement time of induction motors. Most of them are focused on the failure prediction method using abnormal signals of failure patterns of motors from current and vibration signals (2)- (4). Although vibration and current analysis are the most powerful methods for diagnosing motor faults, their sensors are occasionally difficult to install where the environment of industrial field is in poor condition. In addition to this installation problem, there are many low priced low voltage induction motors in the field. Therefore, the diagnosis technique should be easy to install their sensors and low cost comparing to the price of motors. This paper proposes a method for detecting broken rotor bar and stator winding fault in a low voltage squirrel-cage induction motor using a radial flux sensor to develop a simple and low cost diagnosis technique. The sensor is designed as a search coil and installed between stator slots during motor production to measure the leakage flux in radial direction at the air-gap. The proposed method consists of a calculating load condition, finding abnormal signals and monitoring those signals related with motor faults to indentify two kinds of motor failures. To demonstrate the performance of the method, experimental results obtained on 7.5kW three-phase squirrel-cage induction motors are discussed.