Short Circuit Fault at Delta Connection Induction Motor Using FRA Method: Experimental Analysis

The short circuit fault detection practices of power-transformer winding are important to ensure its unfailing and proper operation. In this regard, Frequency response analysis (FRA) is a favourable tool that can be used to detect a such fault. The FRA method has four measurement configurations called: end-to-end; open circuit test, short circuit test, and inductive inter-winding test, capacitive inter-winding test. However, a method of detecting the short circuit fault using FRA end-to-end measurement is investigated. This paper aims on investigating the inductive inter-winding of short circuit faults by showing a bipolar signature. The FRA inductive inter-winding measurement has been obtained from 11/0.415 kV, 500kVA distribution transformer to interpret the characteristics of resonances indicating a bipolar signature. There is a different level of short circuit fault artificially applied to the transformer winding. The inductive inter-winding measurements show that the resonances showing the bipolar signature at the mid-frequency range can be employed to determine the short circuit fault in the transformer winding. Based on this finding, a process of identifying the short circuit fault of a transformer winding is proposed. The data of short circuit fault and normal data of FRA measurement are compared. The proposed method can add to improving the purpose of using the FRA method to detect short-circuit faults in power transformers.

Frequency response analysis (FRA) is considered as an accurate, fast, effective and sensitive method for detection of mechanical defects within power transformers, providing worthy information on active part structure. FRA measurement has been industrialized and employed since the last decade while FRA spectrum interpretation is still under development. The interaction of distributed resistances, series and shunt capacitances as well as self- and mutual-inductances in transformer winding leads to FRA spectrum oscillations. In fact, each and every parameter has its own impact on this spectrum. This study has concentrated on shunt capacitance influences on frequency response spectrum oscillations in Bode diagram. Shunt capacitance impacts on low-, mid- and high-frequency bands of FRA spectrum are discussed. A practical study on a single phase air-core glassy model transformer is carried out to explore the influence of shunt capacitances of HV and LV windings with respect to the transformer tank on FRA spectrum. In addition, a single phase 11/0.25 kV, 25 kVA core type distribution transformer is also tested to evaluate the shunt capacitance impacts on FRA fluctuations.

Frequency response analysis is increasing become a popular technique for assessment of the mechanical integrity of Shunt reactor. This method can diagnose whether a fault exists in core and windings. This paper introduces the test principle and process of frequency response analysis. The vibration device was installed on the shunt reactor. The diagnosis in accordance with the actual fault. The results indicate that frequency response analysis method is feasible.

Brushless DC (BLDC) motors are used extensively in the industry for a wide variety of applications. In the Aerospace industry, they are mainly used in the control actuation system in launch vehicles. Different types of faults can develop in a BLDC motor. It is essential to detect these faults in the early stage itself before they develop into system failures. In this paper, fault detection in BLDC motors using parameter estimation technique is presented. Detailed fault diagnosis is subsequently carried out by a fuzzy inference system. Discrete Square Root Filtering in Covariance form (DSFC) algorithm is used initially for estimating the physical parameters of the BLDC motor. These parameters are then passed on to the fuzzy inference system which carries out the detailed diagnosis. Simulation studies carried out indicates that the developed fault detection and isolation scheme is capable of isolating different types of faults like open circuit, short circuit faults, performance degradation of magnets etc. in BLDC motors.

This paper presents a new investigation to detect various faults within the three-phase star and delta induction motors (IMs) using a frequency response analysis (FRA). In this regard, experimental measurements using FRA are performed on three IMs of ratings 1 HP, 3 HP and 5.5 HP in normal conditions, short-circuit fault (SC) and open-circuit fault (OC) conditions. The SC and OC faults are applied artificially between the turns (Turn-to-Turn), between the coils (Coil-to-Coil) and between the phases (Phase-to-Phase). The obtained measurements show that the star and delta IMs result in dissimilar FRA signatures for the normal and faulty windings. Various statistical indicators are used to quantify the deviations between the normal and faulty FRA signatures. The calculation is performed in three frequency ranges: low, middle and high ones, as the winding parameters including resistive, inductive and capacitive components dominate the frequency characteristics at different frequency ranges. Consequently, it is proposed that the boundaries for the used indicators facilitate fault identification and quantification.

Undetected short circuit faults are a significant problem in power transformers and can eventually develop into catastrophic faults. At present, frequency response analysis (FRA) is one of the well-recognized diagnostic tools for the detection of winding faults, but it has some limitations, such as a low signal-to-noise ratio (SNR) and instability caused by changes in the measuring voltage. In this paper, a novel method called sweep frequency impedance (SFI) is proposed to address the difficulties that arise from FRA. Based on the mechanism of this new method, a nondestructive testing system was established to demonstrate the advantages of SFI measurements. The SFI test system has a better stability, repeatability, and SNR by comparing it with the FRA test system. Moreover, FRA and SFI curves obtained under the same conditions was symmetrical about a specific straight line above 10 kHz, and the SFI value at 50 Hz is equivalent to the short circuit impedance (SCI) value of a transformer. These results indicate that the existing criteria of FRA and SCI methods can be used in the SFI method to detect transformer faults. Finally, the experiments on a special oil-immersed testing transformer demonstrate that the SFI detection system is feasible, sufficiently sensitive to detect short circuit faults and able to quantify the level of the fault.

Online testing system based on frequency response analysis is proposed for the deformation of transformer winding. The structure of testing system, the selection of sweep sequence and computer diagram of data acquisition system are developed in the course of design. In the process of analysis, the concept of correlation coefficient is introduced to judge the relationship between the deformation extent and correlation coefficient. With the proposed method, the location of deformation can be determined. The testing curves demonstrate the effectiveness of the presented system.

Frequency response analysis (FRA) method has been proven to detect the transformer winding damage. However, there is no practical study which investigates the damages on transformer tap changer even though it has a high failure rate. Hence, this paper investigates normal and faulty transformer tap changer using FRA. The FRA measurements conducted using four different test configurations. Also, normal tap changer, pitting and coking tap changer contact were investigated. In addition to FRA measurement, winding resistance measurement has been conducted. The transformer turn ratio measurement also conducted to identify the taps positions. This study results significant to understand the effect of pitting and coking tap changer on the transformer frequency response. The results showed that pitting has a slight influence on the frequency response. However, coking shows a huge variation in low-frequency response. This effect observed during the winding resistance measurement.

The Fourth Industrial Revolution has heightened the demand for intelligent and reliable predictive maintenance systems in industrial environments. This study proposes a hybrid deep learning-based framework for real-time fault detection in Squirrel-Cage Induction Motors (SCIMs). Utilizing eight deep learning architectures—CNN-GRU, CNN-LSTM, LSTM, BiLSTM, Stacked LSTM, GRU, CNN, and ANN—the framework was trained and tested on a comprehensive dataset comprising one million samples, evenly divided between healthy and faulty motor conditions. Hybrid models, particularly CNN-GRU and CNN-LSTM, achieved classification accuracies of 92.57% and 92.27%, respectively, outperforming the other baseline models across precision, recall, and F1-score by effectively capturing both temporal and spatial features. Beyond classification accuracy, the hybrids further demonstrated computational efficiency in terms of inference time, latency, and throughput, validating their suitability for real-time deployment. The system analyzes real-time sensor data, including torque, speed, power, and stator/rotor currents, to identify various fault types such as short circuits, overloads, mechanical failures, and open circuits. Developed in MATLAB Simulink, the framework demonstrates high accuracy and scalability for real-time deployment. While results are promising, the claims are positioned within the scope of the evaluated models, as direct benchmarking with state-of-the-art methods was not within the present scope. The framework demands substantial computing power and annotated datasets, yet it represents a step toward intelligent, self-aware industrial systems. Future work will focus on model optimization, deployment in resource-constrained environments, and validation with real-world noisy industrial data, explicitly considering sensor drift, varying load conditions, and fault severity levels across diverse motor types and operational scenarios. In addition, since bearing faults account for a significant share of induction motor failures in practice, it will be a key priority to ensure comprehensive industrial applicability.

Deep learning techniques for transmission line fault classification – A comparative study

The calculation of high-frequency model parameters of a winding is crucial for the integrity assessment of transformer winding damages and its severity. Bat algorithm (BA) is one of the modern and efficient methods used in computational intelligence due to its efficiency to obtain better results for complicated non-linear problems. In this study, an iterative method to ensure the mutually coupled equivalent model synthesis of an actual isolated transformer winding is proposed via bat search algorithm based on frequency response analysis data measurement (resonance frequencies, equivalent inductance and effective capacitance). The equivalent ladder network synthesis of transformer winding using the BA is based on the construction of the search space of the model parameters, thus each model element to be identified must be limited between two bounds (the upper and lower bounds) based on constraints and inequalities defining the model parameters. The effectiveness of the BA was tested by simulation and experimentally on two case studies of two isolated transformer windings with an air core. Furthermore, a comparative study of BA, particle swarm optimisation and the magnitude vector fitting was made in terms of accuracy and convergence speed.

Power transformer failures can lead to power outages and significant financial loss, particularly at high voltage and medium voltage levels. Frequency response analysis (FRA) is used to diagnose transformer faults and particularly so for detecting mechanical displacements in windings and core. However, such displacements or faults affect significantly the electrical impedance of the transformer windings and challenges remain in the correlation of FRA signature and fault type. Furthermore, other factors such as the adopted measurement method and practical test setup influence the FRA signature. This paper investigates FRA responses of small laboratory test transformers measured using an IEEE standard recommended method and proposes a new generic test setup having additional current measurement points and using precision lock-in amplifiers. The results show the effect of change in the magnitude of applied voltage levels on FRA signatures.

Frequency response analysis (FRA) is regarded as the most effective technique to detect mechanical faults of transformers. Over the years, FRA measurement data have been collected by utilities into transformer asset databases. The characteristic of FRA data is fundamentally determined by the transformer's equivalent electrical circuit, which consists of inductance and capacitance parameters that are windings' design and structure dependent. Different winding types tend to have different FRA characteristics, and a transformer's design information such as winding type, dimension etc. is often not known to the utility but critically important for asset management. This study reviews the state‐of‐the‐art transformer FRA databases and application of machine learning techniques in this field, and proposes to apply a support vector machine (SVM) model onto the FRA data to identify the winding type. The SVM model is first trained by FRA traces of transformers with known winding types, and after testing, the SVM model is then applied to FRA traces with unknown winding information. A set of data from the UK's National Grid FRA database, was used to demonstrate and verify the SVM model. All transformers used in this study are 400/275/13 kV transmission transformers, which were designed using four different winding types, namely multiple layer, plain disc, interleaved disc and single helical windings. The proposed method can successfully identify the correct winding type.

This study presents a machine learning technique for fault diagnostics in induction motor drives. A normal model and an extensive range of faulted models for the inverter-motor combination were developed and implemented using a generic commercial simulation tool to generate voltages and current signals at a broad range of operating points selected by a machine learning algorithm. A structured neural network system has been designed, developed and trained to detect and isolate the most common types of faults: single switch open circuit faults, post short-circuits, short circuits and the unknown faults. Extensive simulation experiments were conducted to test the system with added noise, and the results show that the structured neural network system which was trained by using the proposed machine learning approach gives high accuracy in detecting whether a faulty condition has occurred, thus isolating and pin-pointing to the type of faulty conditions occurring in power electronics inverter-based electrical drives. Finally, the authors show that the proposed structured neural network system has the capability of real-time detection of any of the faulty conditions mentioned above within 20 ms or less.

Fault detection in induction motors operating in non-stationary regimes has become a need in today’s industry. Most of the works published deal with line-fed motors. Nevertheless, the number of inverter-fed induction motors has significantly increased in recent years. Therefore, several fault detection techniques have been proposed lately for this type of motors, based mainly on an adequate input signal processing to obtain fault signatures in the time-frequency domain. In this paper, a comparison of time-frequency techniques applied to fault detection in inverter-fed induction motors in a transient state is presented. For that purpose, the techniques are applied to two current signals acquired from two induction motors with two types of faults: bar breakage and mixed eccentricity. The paper shows the particularities and special difficulties of diagnosing under this type of feeding, reviewing the works related to each technique. The strengths and weaknesses of these techniques are discussed with the goal of providing a criterion for its application in an industrial environment and guidance for future developments in this field.