On-line monitoring and diagnosis of transformers have been investigated and discussed significantly in last decade. This study has concentrated on issues arising while on-line transformer winding deformation diagnosis is going to be applied on transformers with various kinds of techniques. From technical perspective, before replacing off-line methods by on-line methods and eventually by intelligent approaches, practical challenges must be addressed and overcome. Hence, available off-line transformer winding deformation diagnosis methods are discussed precisely. Mathematical calculation in on-line short circuit impedance measurement is investigated. On-line transformer transfer function measurement setup is presented. A profound insight to the problems pertaining on-line transformer winding deformation recognition methods, characterizes existing online methods, explains the concepts behind online measurements and striving to open the discussion doors towards challenges are discussed. In the end a 400 MVA step up transformer has been taken as a case in order to clarify the capability of Frequency Response Analysis (FRA) method in fault detection while short circuit impedance could only demonstrate some rough understanding about transformer condition.

Advanced transformer winding deformation diagnosis: moving from off-line to on-line

This paper presents the current status and future trends in the application of the frequency-response analysis (FRA) technique with the transformer in service (online) through bibliographic review and analysis. As a result, three basic stages of the online FRA test have been identified and defined: injection and excitation signal measurement; recording, filtering and processing of measured signals; and curve analysis and interpretation. This work presents an overview of the online FRA technique, useful for subsequent research in this area.

Current Status and Future Trends in Frequency-Response Analysis With a Transformer in Service

This paper introduces a novel fault diagnosis approach for transformer based on self-powered radio-frequency identification (RFID) sensor and deep learning technique. The exploited RFID sensor tag with functionalities of signal collection, data storage, and wireless transmission employs surrounding electromagnetic field as power source. A customized power management circuit, including ac–dc converter, supercapacitor, and its corresponding charging circuit, is presented to guarantee constant dc power for the sensor tag. The measured vibration signal contains miscellaneous noises and is characterized as nonlinearity and nonstationarity, so it is difficult to extract robust and useful features by using traditional feature extraction approaches. As one of the deep learning techniques, stacked denoising autoencoder (SDA) shows satisfactory performance in learning robust features from complex signal. Hence, in this paper, SDA approach is employed to learn robust and discriminative features from measured signals. The experimental results show that the presented power supply can generate 2.5-V dc voltage, which is the rated operating voltage for the rest of the sensor tag. The developed sensor tag can achieve a reliable communication distance of 17.3 m in the test environment. Furthermore, the SDA approach shows satisfactory performance in learning robust and discriminative features. Experimental results indicate that the presented approach is effective and time-saving in terms of fault diagnosis for transformer winding and core.

Transformer Fault Diagnosis Using Self-Powered RFID Sensor and Deep Learning Approach

Monitoring and diagnosis of transformers have been investigated and discussed for many years While transformer routine tests indicate electrical and also insulation weakness or defects, Frequency Response Analysis (FRA) as a modern test can indicate mechanical defects in transformer active part This paper introduces the concepts behind online measurements in frequency domain and is striving to open the discussion doors towards advanced investigations which are all aligned with smart grid concept This study has also concentrated partly on issues rising while online transfer function measurement techniques are going to be applied on transformers with various kinds of winding connections and accessories In the end, crucial issues from theoretical and also practical perspectives for online transfer function implementation are discussed

Practical challenges in online transformer winding deformation diagnostics

A novel transformer fault diagnosis method using self-powered radio frequency identification (RFID) sensor tag, deep belief network (DBN), and multiple kernel support vector machine (MKSVM) is presented. The self-powered RFID sensor tag is applied to measure transformer vibration signals, which has advantages of convenience, long-term monitoring, low power consumption, and fast location. Then, the DBN is employed to extract features from the measured signals, and the DBN method’s extraction performance is improved by optimizing its learning rates and momentum factors. The extracted features of the same fault are highly centralized, and the features of the different faults are obviously separated. Based on the features, the MKSVM is utilized to construct a transformer fault diagnosis model, and the MKSVM’s penalty factor and kernel weights are optimized through a quantum-behaved particle swarm optimization (QPSO) algorithm. Finally, a mean signal deviation (MSD) metric is presented to locate the fault position. In this experiment, a 10 kV transformer is used to demonstrate the proposed transformer fault diagnosis procedure, and the diagnosis results reflect that the proposed method can effectively measure the transformer vibration signals, extract the essential features, construct an accurate diagnosis model, and locate the fault position.

Transformer Fault Diagnosis Method Based on Self-Powered RFID Sensor Tag, DBN, and MKSVM

The ability of a transformer to withstand the dynamic effects of a short circuit test is conventionally evaluated by a measurement of short circuit reactance before and after the test. We propose new techniques for fault detection based on continual assessment during the test. The methods are based on the comparative null method for accuracy measurement in instrument transformers. They include voltage comparison, current comparison and real/reactive power measurements. High resolution sampling based acquisition systems are used to compute changes on a cycle-to-cycle basis. The proposed methods are validated on a number of models including a voltage transformer and a distribution transformer winding. They would be useful for designers in assessing events leading to failure in a more transparent manner.

Real-Time Techniques to Measure Winding Displacement in Transformers During Short-Circuit Tests

Electric traction is a promising technology that can bring out significant improvement in vehicle performance, fuel efficiency and energy utilization. The energy management in the traction system can be well improved through regeneration. In this paper, a method for regenerative braking has been proposed in which the induction machine is operated with negative slip to obtain braking torque. Regeneration during deceleration of electric vehicle is demonstrated by controlling supply frequency and voltage.

Regenerative braking in induction motor drives in applications to Electric Vehicles

On line assessment of winding deformation in a power transformer can be performed via a bushing tap excitation with optimized high frequency excitation. A benchmark layer winding is used to validate the use of the specific high frequency excitation. Axial displacement, compression and buckling of the winding are simulated and the changes identified. Measurements are performed on a voltage transformer to verify the suitability of the technique. The procedure will result in a considerably shorter time for measurement.

Online assessment of winding deformation based on optimised excitation

Modern active power filters are capable to compensate high order harmonics (typically, the 25th) dynamically. Even though, shunt active power filter maintains source current nearly sinusoidal, considerable distortion is observed in source current due the presence of high order harmonics (greater than 25th). A passive high pass filter is used to filter out these harmonics. The design procedure of these high pass filters involves equations of more than second order; hence the parameters are selected by trial and error. In this paper, a simplified design procedure is proposed for designing a high pass filter used for shunt active power filter applications. Equations are derived from the basics and design procedure is demonstrated by considering a case study of a single phase shunt active power filter.

Design of passive high pass filter for shunt active power filter application

A new coreless non-intrusive current probe is presented in this paper Most of the existing current sensors require 360° access to the wire, while the proposed probe accurately measures the current even if the access to the wire is limited to a small angle Another new feature of the probe is that its output is not sensitive to the variation in the gap between the wire and the probe, which is a major advantage as it helps to provide reliable measurement over time The proposed probe employs two sets of high sensitive and lightweight Hall effect or giant magneto-resistance-based sensors Results from the experimental studies conducted showed that the proposed configuration is insensitive to the variation in the gap between the wire and the probe This feature ensures that the output is independent of the insulation thickness, vibration, thermal expansion, etc Two prototypes of the proposed probes have been developed and tested The results obtained established the feasibility of the proposed low-cost current sensor

S. Gopalakrishna

Papers

Real-Time Techniques to Measure Winding Displacement in Transformers During Short-Circuit Tests

Regenerative braking in induction motor drives in applications to Electric Vehicles

Online assessment of winding deformation based on optimised excitation

Design of passive high pass filter for shunt active power filter application

Development of a New Low-Cost and Reliable Core-Less Current Probe for Conductor With Reduced Access