Inter-turn Insulation Failure Research Articles

Influence of Rotor Eccentricity on the Reliability of the Pole Drop Test for Salient Pole Synchronous Machines

The pole drop test is the most common test for detecting inter-turn insulation failure in the rotor field winding of salient pole synchronous machines. Shorted field turns are detected by comparing the voltage drop across each rotor pole with ac voltage applied to the field winding terminals. It is a simple and effective test that is usually performed with the rotor inserted for convenience and high sensitivity. In this work, the influence of rotor eccentricity on the pole drop test is analyzed. It is shown that anomalies in the airgap can interfere with the pole drop test to cause false positive indications. Alternative test options immune to false indications are also suggested in this letter. The analysis and test options for screening false indications due to eccentricity are verified on 4 and 6 pole, 5, 5.5, and 30 kVA synchronous machines under emulated fault conditions. It is shown that eccentricity interferes with the pole drop test and that testing under dc excitation is immune to eccentricity although the sensitivity is lower, which are not reported elsewhere.

Fault Diagnosis Method of Inter-turn Insulation Failure for Circuit Breaker Control Coil

The failure of insulation between control coils is an important cause of circuit breaker failure, from the perspective of engineering application, it is expected to accurately screen out this kind of indiscernible inter-turn fault, an effective diagnostic method must be explored. First, apply high-voltage dc (HVDC) pulse excitation to the control coil of the high-voltage circuit breaker, and the response voltage signal is denoised by continuous wavelet transform (CWT) to obtain the main frequency signal which can reflect the essential characteristics of the response; the V/t characteristic curves of the electromagnet coils are then compared with those of standard electromagnet coils at the same frequency and at the same voltage; finally, the variation rules of the integral flux and reluctance of the electromagnet coil under test are judged, so as to judge the inter-turn insulation fault.

Comprehensive Monitoring of Field Winding Short Circuits for Salient Pole Synchronous Motors

An interturn insulation failure in the field winding of synchronous motors is a common problem that can lead to degradation in performance and accelerated wear of motor components. Existing offline tests for detecting this fault are known to be unreliable and inconvenient to perform, as they require motor disassembly. Online monitoring based on airgap flux monitoring has been proven to be successful, but could lack sensitivity for cases where the number of turns per pole is large. In addition, field winding turn shorts can be intermittent depending on the centrifugal force and could be unobservable at standstill or under an steady state operation. Therefore, monitoring of the turn faults under standstill and starting conditions with high sensitivity and without motor disassembly is desirable. In this paper, the detection of field winding short circuits for salient pole synchronous motors based on an airgap search coil under the starting transient, and new methods for standstill testing without motor disassembly are proposed. Two-dimensional finite element analysis and experimental testing on a 30-kW motor are given to verify the claims made. It is shown that the proposed set of tests can provide sensitive and reliable detection of field winding short circuits under standstill and starting conditions.

Modeling and detecting the stator winding inter turn fault of permanent magnet synchronous motors using stator current signature analysis

Many researchers have been attracted by the challenges of electrical machines fault detection, diagnosis and monitoring, which provide early warning that could help schedule necessary maintenance to avoid catastrophic consequence. Considerable papers have presented reviews and compared conditions monitoring and fault diagnosis methods for induction machines, but none for permanent magnet machines. In this paper a dynamic fault model for a rotor surface mounted PMSM machine under inter-turn insulation failure is derived. this model allows to study the location and severity of the stator winding fault using electrical circuit magnetically coupled. To achieve this objective, a precise mathematical model that can describe both healthy and fault conditions is developed. To detect stator winding short circuit faults automatically and to estimate the severity of the fault, power spectral density (PSD) was used. It was found that the amplitude of the third harmonic of the current was the most distinctive characteristic for detecting the short circuit fault ratio of the SPMSM.

Performance of a load-immune classifier for robust identification of minor faults in induction motor stator winding

Reliable detection of induction motor stator winding insulation failure at its early stages is a challenging issue in modern industry. Insulation failure between small number of turns, involving less than 5% turns of phase winding are often indiscernible and detection becomes even more complicated when motor operates at varying load levels. In line-fed motors, supply voltage unbalance is another inadvertent issue which may tend to exhibit current signature similar to stator winding inter-turn insulation failure case. The proposed work presents a robust system, to identify severity of stator winding insulation faults when an induction motor with random wound stator winding works under such operating conditions. In the present work, various features obtained from time, frequency, timefrequency, and non-linear analysis of stator currents at various stator winding short circuit faults and supply voltage unbalance conditions for different load levels have been studied. A Support Vector Machine based Recursive Feature Elimination (SVM-RFE) algorithm is used to identify the features which can provide discrimination information related to severity of fault level, independent of supply voltage unbalance and immune to load level variations. Among the extracted features, features obtained through Detrended Fluctuation Analysis (DFA) are found to be most robust for this purpose. Finally a Support Vector Machine in Regression mode (SVR) has been formed to identify winding failures employing the optimum number of features selected through SVM-RFE technique.

Monitoring of inter-turn insulation failure in induction motor using advanced signal and data processing tools

Detection of stator winding inter-turn insulation failure at early stages is crucial for promoting safe and economical use of induction motors in industrial applications. Whereas major insulation failures involving larger percentages of winding are easily discernible from magnitude of supply current, minor inter-turn insulation failures involving less than 5% of turns often go undetected. The present contribution reports experimental results of minor faults due to inter-turn insulation failures in stator windings of induction motor under different loading conditions being analyzed using data and signal processing tools combining Park's Transform and Cross Wavelet Transform. Rough Set Theory (RST) based classifier has been used for fault severity monitoring.

ANN based real time incipient fault detection and protection system for induction motor

Artificial neural network (ANN) has its unique advantage in the area of incipient faults detection. This article presents an ANN based real time fault detection and protection system for two types of incipient faults viz. inter-turn insulation failure and bearing wear in single-phase induction motor. The ANN fault detection (ANNFD) program is developed in C++ and implemented using a PC based DSP controller board. The ANN is trained and tested by collecting the online experimental data for five input parameters viz. motor intake current, rotor speed, winding temperature, bearing temperature and noise. The results are compared with the signature of measurable parameters. The results of evaluation indicate that the system produces satisfactory performance for the fault detection as well as for the protection of motor.

Induction Motor: Fuzzy System for the Detection of Winding Insulation Condition and Bearing Wear

In this article, fuzzy logic method is used to detect two types of incipient faults viz., interturn insulation failure and bearing wear in single-phase induction motor. The fuzzy rules are framed depending upon the experimental data for five input parameters viz., motor intake current, rotor speed, winding temperature, bearing temperature, and noise. The simulation and experimental results are given to verify the proposed method. Initially the performance is tested with two inputs, motor intake current and rotor speed; later the remaining three input parameters were added and the performance is tested with five inputs. It is found that the performance with five inputs gives better results.

Inverter statistics for online detection of stator asymmetries in inverter-fed induction motors

A simulation model of an induction machine that enables the analysis of the influence of stator fault conditions on the machine control of inverter-fed drives is presented. Based on this model, the influence of the breakdown of the stator windings insulation on the behavior of the machine and especially on the current control scheme is shown. A new online method to detect such asymmetries caused, for example, by an interturn insulation failure in the stator windings is proposed and investigated. This new method utilizes the influence of stator asymmetries on the inverter current control scheme. By evaluating the statistical distribution of the different inverter switching states and switching times, asymmetries in the stator can be detected and isolated. The measurements required to implement this method are already available in modern inverter fed drives as they are used to realize the current control loop. Thus, no additional sensors are necessary. The practical realization of the fault detection algorithm is demonstrated in combination with a predictive single step current controller. Measurements performed on a drive test stand verify the applicability of the proposed online method to detect and isolate stator faults.

Modeling of large AC motor coils for steep-fronted surge studies

Interturn insulation failure is now recognized as a common cause of failure in large AC machines. The line-end coil is the most likely site of such failure, since under steep-fronted surge conditions it bears 100% of the surge amplitude for several microseconds. During this period, measurements show that the interturn voltage distribution in the line-end coil is nonlinear, with up to 40% of the surge voltage appearing between the last two turns of the coil. To predict the voltage distribution in the line-end coil under such conditions, a multiconductor transmission line model of the coil has been developed. This model can then be used in a general-purpose electromagnetic transients program (EMPT) or in a special-purpose EMPT. Results are presented for measured and simulated waveforms on a large AC motor. The simulations have been done on a special- purpose EMPT. The latter has also been used to compute the impulse response of the coil which can, then be convolved with the applied surge waveforms to derive the coils response to a variety of surge waveshapes. This is a much cheaper method of solution if responses to a variety of surge waveforms are required since it only requires running the PT program once, followed by a number of runs of the much simpler and faster convolution program.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

The Use of Partial Discharge and Impulse Voltage Testing in the Evaluation of Interturn Insulation Failure of Large Motors

Several 13.2 kV, 21,000 HP synchronous motors failed as a result of interturn insulation breakdown. Investigation of motor failures indicated that the cause was related to high magnitude, steep fronted surge waves (References 1 and 2). Partial discharge (corona) related failures were discounted without any actual measurements. To confirm the validity of the previous investigation, partial discharge testing was conducted on four new 21,000 HP motors and on one failed 21,000 HP motor. Additionally, surge impulse testing was performed on the failed motor to assess interturn insulation endurance. This paper presents the results of above tests. It is concluded that failure of motors could not be caused by corona, Confirmation of previous investigation results indicated that partial discharge testing can be used as a diagnostic tool in the evaluation of interturn insulation of large motor.

The Use of Partial Discharge and Impulse Voltage Testing in the Evaluation of Interturn Insulation Failure of Large Motors

The Use of Partial Discharge and Impulse Voltage Testing in the Evaluation of Interturn Insulation Failure of Large Motors