Inter-turn Shorts Research Articles

Application of ANN in Induction-Motor Fault-Detection System Established with MRA and CFFS

This paper proposes a fault-detection system for faulty induction motors (bearing faults, interturn shorts, and broken rotor bars) based on multiresolution analysis (MRA), correlation and fitness values-based feature selection (CFFS), and artificial neural network (ANN). First, this study compares two feature-extraction methods: the MRA and the Hilbert Huang transform (HHT) for induction-motor-current signature analysis. Furthermore, feature-selection methods are compared to reduce the number of features and maintain the best accuracy of the detection system to lower operating costs. Finally, the proposed detection system is tested with additive white Gaussian noise, and the signal-processing method and feature-selection method with good performance are selected to establish the best detection system. According to the results, features extracted from MRA can achieve better performance than HHT using CFFS and ANN. In the proposed detection system, CFFS significantly reduces the operation cost (95% of the number of features) and maintains 93% accuracy using ANN.

Diagnosis of Inter-Turn Shorts of Loaded Transformer Under Various Load Currents and Power Factors; Impulse Voltage-Based Frequency Response Approach

Inter-turn short circuits within the transformer winding are diagnosable through the impulse voltage based frequency response analysis (IFRA). If the diagnosis is made Online, interruption to the power network can be avoided. However, the transformer’s load may influence the frequency response based diagnostic process, and this aspect has not yet been investigated. Motivated by this research gap, experimental investigations were carried out on a three-phase 5 kVA, 440 V/440 V Star/Delta transformer and a three-phase 315 kVA, 11 kV/433 V, Dyn11 distribution transformer, when they were supplying power to loads of different magnitudes and power factors. The influence of the load current and its power factor on the IFRA was observed under the transformer’s healthy condition and with inter-turn short circuits in one of the windings. Investigations under these different loaded conditions revealed that a signature FRA plot developed at a particular load condition cannot be directly used for assessing the condition of the transformer at a different load. Both the magnitude and the power factor of the load are found to influence the frequency response. A careful interpretation of the Frequency response of the transformer, based on its load, was found effective in detecting and locating the inter-turn shorts.

Extended Use for the Frequency Response Analysis: Switching Impulse Voltage Based Preliminary Diagnosis of Potential Sources of Partial Discharges in Transformer

The Frequency Response Analysis approach (FRA) is useful in the fault diagnosis of transformers. However, its usefulness in diagnosing any potential sources of Partial Discharge (PD) in transformers has not been thoroughly investigated so far. In this work, the use of Impulse voltage-based FRA (IFRA) in diagnosing inter-turn shorts and potential sources of PD were investigated on a 315 kVA, 11 kV/433 V transformer. Inter-turn shorts and PD sources were emulated and the usefulness of IFRA in their diagnosis was investigated while using switching impulse voltage at different magnitude levels as the test signals. For emulating the inter-turn shorts and the PDs, special tappings were provided on one of the 11 kV windings through the low capacitance bushings. Low voltage impulse was successful in diagnosing the inter-turn shorts, but unsuccessful in identifying the sources of PDs. During the test condition, the test voltage was adjusted with the presence of artificially created PD sources. The frequency response of the transformer before and after the inception of PD was observed and analyzed in this article. The FRA results demonstrated that the switching impulse voltage based IFRA approach at moderate voltages could be useful in diagnosing the presence of the potential sources of PDs.

Incipient Fault Detection in Stator Windings of an Induction Motor Using Stockwell Transform and SVM

In this article, Stockwell transform (ST) is used to analyze the stator current signals for diagnosis of various motor conditions such as healthy, stator winding interturn shorts, and phase to ground faults. ST decomposes the current signals into complex ST matrix whose magnitude has been utilized for the fault detection. The nature of the fault, that is, ground or interturn is identified using the zero sequence currents followed by postfault detection. Two separate frequency bands are defined to extract the features which are fed to two different support vector machine (SVM) models for faulty phase detection for both types of faults. Under both cases, a heuristic feature selection approach is utilized to find the optimal features for classification purposes. Average classification accuracy of 96% has been achieved for both types of faults.

Extended Application for the Impulse-Based Frequency Response Analysis: Preliminary Diagnosis of Partial Discharges in Transformer

Frequency response analysis (FRA) with low impulse voltage signals (IFRA) is useful in diagnosing faults like the mechanical displacements and inter-turn shorts in transformers. However, they fail to diagnose the presence of any source of partial discharges (PD) across the oil-paper insulation. PD is happening only above its inception voltage level for the applied signal, thereby changing the loading effect of the insulation system and becoming detectable. If FRA tests are extended carefully up to high voltages, they can be even useful for the preliminary diagnosis of the potential sources of PD. Investigations were not carried out so far to get an insight into this aspect. Motivated by this, an investigation was done, first time, on a 315 kVA, 11 kV/433 V transformer. IFRA was carried out carefully at various voltage levels, up to 50% of the Basic impulse insulation level. The IFRA results of the transformer under its healthier condition, under the emulated inter-turn shorts and, the PD sources across the major-insulation were, observed and compared. Low-voltage IFRA is found sufficient, only for diagnosing the inter-turn shorts. When IFRA is extended to moderately high voltage, it is found useful in the preliminary diagnosis of the PD sources.

A Methodology for Remote Sensing Inter-Turn Fault Events in Power System Air-Core Reactors, via Simulation of Magneto Quasi-Static Fields in 2D FDTD

We present a numerical methodology to estimate the transient fault currents and to simulate the remote sensing of transient fault information embedded in the magnetic field emissions caused by inter-turn shorts in 60 Hz air-core reactors, thru a magneto quasi-static (MQS) field approximation in the method of Finite-Difference Time-Domain (FDTD) in 2-dimensional (2D) space. The MQS 2D FDTD fields of reactor in normal operation are scaled by correlation against an equivalent circuit model that is derived from application of basic physics principles to parameters of the 3D air-core reactor. The proposed multi-scale quasi-static modeling methodology, based on the reduced c modification, provides fine-feature access down to the single-wire level and can efficiently estimate the transient fault fields and currents due to turn-to-turn short in a reactor with core height in several meters, core diameter in meters, wire diameter in millimeters, and number of turns in the thousands, at 60 Hz; this is accomplished by using computational resources of a typical laptop computer within seconds or minutes , as opposed to days that would be otherwise required without the reduced c modification.

Analysis and Detection of Inter-Turn Short-Circuit Fault Through Extended Self-Commissioning

This paper presents a comprehensive analysis of permanent magnet synchronous machine stator winding impedance variation in inter-turn short-circuit and eccentricity faults. The phase impedances are monitored through an extended self-commissioning procedure at the standstill mode before or after operation, and hence are agnostic to well-known issues caused by transients, load/speed level, or controller coefficient dependency. Moreover, the effect of stator iron core saturation on the electrical parameters is analyzed in depth. In order to distinguish the inter-turn shorts from the eccentricity fault which exhibits similar behaviors, a classification algorithm based on the saturation effect is introduced and analyzed on the machines with different pole–slot combinations. Both two-dimensional finite element analysis simulation and experimental test results are provided to show the efficacy of this analysis.

FAULT DETECTION IN SWITCHED RELUCTANCE MOTOR DRIVES USING DISCRETE WAVELET TRANSFORM AND K-MEANS CLUSTERING

This study presents a novel method of detection of inter turn shorts based on k means clustering technique. In addition to inter turn short detection, the other faults like open, short, phase to phase faults and DC volt-age faults are detected through wavelet transforms and k means clustering. Open and short faults are classified using artificial neural network. All other faults are classified using Support Vector Machines (SVM). Switched reluctance motors are very popular in these days, because of ease in manufacturing and operation. Though an electronic circuit can detect the faults like open and short, the classification cannot be done effectively with electronic circuitry. More over an intelligent method can easily identify the fault and classify and hence the root cause of the fault may be guessed and rectified using this method of classification. This is highly possible with the time localization property of the wavelet transforms. So instant of fault occurrence can be detected along with the type of fault. The information used to include this intelligence in the system are just current waveforms, flux waveforms and torque waveforms. Inter turn shorts are very critical for a long run operation of the motor. Moreover, the early detection minimizes the faulty operation time and ensures the plant stability and saves the life of motor too. Hence an integrated system to detect the major faults under a simulation model has been proposed in this study.

Detection and location of interturn short circuits in the stator windings of operating motors

One major cause of motor failures is breakdown of the turn insulation leading to puncture of the groundwall. Early detection of interturn shorts during motor operation would eliminate consequential damage to adjacent coils and the stator core reducing repair costs and motor outage time. In addition to the benefits gained from early detection of turn insulation breakdown, significant advantages would accrue by locating the faulted coil within the stator winding. Fault location would not only increase the speed of the repair, but would also permit more optimal scheduling of the repair outage. This work was successful in practically implementing a theory to predict changes in the axial leakage flux resulting from stator winding interturn shorts and in developing an algorithm to locate the position of the faulted coil. An experimental setup consisting of a 200 hp motor loaded by a generator was used to validate this theory. Suitable transducers were developed and installed on this motor. Measurement using this experimental configuration clearly validated the theoretical model. On the basis of this experimental work an instrument to continuously monitor for shorted turns is under development.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>