Christian Kral

Detection of mechanical imbalances of induction machines without spectral analysis of time-domain signals

Mechanical rotor imbalances and rotor eccentricities are reflected in electric, electromagnetic, and mechanical quantities. Therefore, many surveillance schemes determine the Fourier spectrum of a single line current in order to monitor the motor condition. Mechanical imbalances give rise to two first-order current harmonics. Due to the interaction of the currents and voltages, both these current harmonics are also reflected by a single harmonic component in the frequency spectrum of the electric power. This single component is easier to assess than both the current harmonics. The technique proposed in this contribution evaluates this imbalance-specific modulation of the electric power. The proposed approach does not determine the Fourier spectrum of a time-domain signal, though. First, the imbalance specific oscillation of the electric power is extracted by a bandpass filter. Then, the averaged pattern of this component is determined by means of an angular data clustering technique. In that way, the oscillation of the electric power in the time domain becomes mapped into a discrete waveform in an angular domain. The amplitude of the fundamental harmonic of these discrete data serves as the imbalance indicator of the proposed scheme. This technique, therefore, overcomes small load and slip fluctuations. Measured results of a mechanically unbalanced machine and a case of combined static and dynamic eccentricity are presented.

Rotor temperature estimation of squirrel-cage induction motors by means of a combined scheme of parameter estimation and a thermal equivalent model

This paper deals with a rotor temperature estimation scheme for fan-cooled mains-fed squirrel-cage induction motors. The proposed technique combines a rotor resistance estimation method with a thermal equivalent circuit. Usually, rotor resistance estimation works quite well under rated load conditions. By contrast, if the motor is slightly loaded, rotor resistance estimation becomes inaccurate due to the small slip. Therefore, rotor temperature estimation under low-load conditions may be estimated by a thermal equivalent model. In order to determine the rotor resistance and, thus, rotor temperature accurately, several machine parameters have to be obtained in advance. Load tests provide the leakage reactance and the iron losses of the induct machine. The stator resistance has to be measured separately. The parameters of the thermal equivalent model are a thermal resistance and a thermal capacitance. These parameters are derived from a heating test, where the reference temperature is provided from the parameter model in the time domain. This lumped thermal parameter model is based on the assumption that the total rotor temperature increase is caused by the total sum of the losses in the induction machine. Measuring results of a 1.5-kW and an 18.5-kW four-pole low-voltage motor and a 210-kW four-pole high-voltage motor are presented and compared.

A comparison of rotor fault detection techniques with respect to the assessment of fault severity

This contribution compares rotor fault detection techniques of squirrel cage induction machines such as motor current signature analysis, Parks vector, motor power, torque, search coils, vibrations and acoustic emissions. Innumerable papers have been published about those techniques so far. Yet, the assessment of the severity of the actual fault has not received much attention. The term severity refers to the grade of electrical asymmetry of the rotor. This paper examines fault assessment and the respective specification. The objective is to compare those rotor fault assessment techniques which reflect the state of the art. A wide range of literature is cited in order to provide appropriate references. The authors also present and compare some measuring results in order to illustrate the investigated techniques.

Rotor temperature estimation of squirrel cage induction motors by means of a combined scheme of parameter estimation and a thermal equivalent model

This paper deals with a rotor temperature estimation scheme for fan-cooled, mains-fed squirrel cage induction motors. The proposed technique combines a rotor resistance estimation method with a thermal equivalent circuit. Usually, rotor resistance estimation works quite well under rated load conditions. By contrast, if the motor is slightly loaded, rotor resistance estimation becomes inaccurate due to the small slip. Therefore, rotor temperature estimation under low load conditions may be estimate by a thermal equivalent model. In order to determine the rotor resistance and thus rotor temperature accurately, several machine parameters have to be obtained in advance. Load tests provide the leakage reactance and the iron losses of the induction machine. The stator resistance has to be measured separately. The parameters of the thermal equivalent model are a thermal resistance and a thermal capacitance. These parameters are derived from a heating test, where the reference temperature is provided from the parameter model in the time domain. This lumped thermal parameter model is based on the assumption that the total rotor temperature increase is caused by the total sum of the losses in the induction machine. Measuring results of a 1.5 kW and a 18.5 kW four pole, low voltage motor and a 210 kW, four pole high voltage motor are presented and compared.

Detection of rotor faults under transient operating conditions by means of the Vienna Monitoring Method

This contribution investigates the Vienna monitoring method during transient operation. The Vienna monitoring method is a rotor fault detection technique based on two space phasor machine models. These models derive quantities such as flux and torque. An induction machine with rotor asymmetries gives rise to double slip frequency oscillations of shaft torque. These oscillations are also reflected in the modelled torques. Accordingly, the fault indicator of the Vienna Monitoring Method is derived from the difference of the computed torques to compensate load effects. This paper presents measurement results regarding the Vienna Monitoring Method during stationary and transient operating conditions. The transient conditions include varying voltage and frequency as well as varying torque. The two transient cases are a challenge for any rotor fault detection technique, since the magnitudes and frequencies of fault-specific signatures are highly load and speed dependent.

Detection of mechanical imbalances during transient torque operating conditions

Mechanical rotor imbalances and rotor eccentricities give rise to specific mechanical vibration, and certain electric, electromagnetic and mechanical harmonics. The extent and location of these harmonics depends on the actual severity of the imbalance, machine supply and load torque. Many fault detection techniques evaluate the spectral components of any of the mentioned electric or mechanical quantities. Such techniques are therefore usually only applicable for steady state operating conditions. To realize an imbalance detection technique working under transient conditions, load dependencies have to be taken into account. In this contribution a technique is used that employs a phase locked loop to track the imbalance-specific harmonics of the electrical power. This technique, therefore, overcomes the slip and load dependency of the imbalance-specific harmonics. The proposed imbalance detection technique is applied to measured data. Results for steady state and transient operating conditions are then compared.

Detection of mechanical imbalances without frequency analysis

Mechanical rotor imbalances and rotor eccentricities are reflected in electric, electromagnetic and mechanical quantities. Therefore, many surveillance schemes determine the Fourier spectrum of a single line current in order to monitor the motor condition. Mechanical imbalances give rise to two first-order current harmonics. Due to the interaction of the currents and voltages, both these current harmonics are also reflected by a single harmonic component in the frequency spectrum of the electric power. This single component is easier to assess than both the current harmonics. The technique proposed in this contribution evaluates this imbalance-specific modulation of the electric power. The proposed approach is not a frequency domain method, though. The imbalance specific oscillation of the electric power is extracted by a band-pass filter. Then, the averaged pattern of this component is determined by means of an angular data clustering technique. In that way the oscillation of the electric power in the time domain becomes mapped into a discrete waveform in an angular domain. The fundamental of these discrete data serves as an imbalance indicator of the proposed scheme. This technique, therefore, overcomes small load and slip fluctuations. Measured results of a mechanically unbalanced machine and a case of combined static and dynamic eccentricity are presented.

Stator winding turn fault detection for induction machines

Stator winding failures are a serious problem of induction machines. In the case of bearing failures or rotor faults, fault-specific signatures increase slowly. An induction machine with such failures still can be operated for a certain time without taking strict precautions. Stator faults may occur much faster due to insulation breakdown or overload and are therefore more critical with respect to time. This paper presents measurement results of an accidentally arising stator winding fault, which was recorded during the investigation of stator asymmetries. Furthermore, an on-line fault detection technique as well as some measurement data applied to this technique, are presented. The used technique is based on extracting a so called injected negative sequence current.

Condition Monitoring and Fault Detection of Electric Drives

An electric drive consists of an electric machine, which converts electrical power to mechanical power, power electronics to operate the machine and a unit to control the motion of the drive. These are the components of the drive. Parts of each of these components could fail and give rise to specific failure scenarios. The drive types investigated in this chapter are limited to asynchronous induction machine and permanent magnet synchronous machines, since these are the most common machine types in modern electric drive applications. Faults of power electronics are not discussed since most failures lead to the outage of the drive as the power electronics usually show no symptoms before failure. The task of identifying and classifying drive failures from certain measured quantities is called fault detection. Under some conditions, fault detection may require certain safety protection actions. Example: A turn to turn short circuit in the stator winding of the machine is one example for a safety critical issue. If the short remains for a certain time, parts of the winding will be destroyed. This in turn could cause winding failures that lead to a larger short circuit current which may result in the failure and outage of the entire drive. In this sense, a safety critical issue is a time critical issue. If the failure cannot be detected within a certain time, the drive will be damaged and fails. It is thus highly demanded to accurately detect safety critical faults and to protect the drive (and the application) in this case.

Experiences with online partial discharge diagnoses on turbogenerators

This paper describes the experiences with partial discharge diagnoses of high voltage turbogenerators. After a short introduction of the structure of a high voltage winding with Roebel-bars the sources of partial discharge in the winding are described. The phase resolved partial discharge pattern analysis as well as the applied method for online and offline partial discharge measurement are described and practical measurement results for offline and online diagnoses are presented. The important link to maintenance actions is outlined for a typical case. In an example the fingerprints of offline diagnoses and online diagnoses of the same generator are compared. Also the change of the fingerprint over a period of three years is presented. The degradation of the insulation in the first period due to increased operation voltage and the fingerprint after maintenance actions during the second operation period are shown. The interpretation of the partial discharge fingerprint is discussed for presented examples. Many typical partial discharge patterns have been collected and classified for many years. Therefore, it is possible to identify fingerprints at the very first online measurement. An example is presented.