Transformer Condition Monitoring Research Articles

Характеристика систем непрерывного контроля технического состояния печных трансформаторов

The paper describes briefly transformers of the ladle furnaces (LF) of the electric steel making shop (ESMS) at OJSC Magnitogorsk Iron and Steel Works. It notes a lack of regulations and experience of development and implementation of the system for control of furnace transformer condition at domestic iron and steel works. The paper provides analysis of the known systems enabling condition monitoring for transformers of this class. Their disadvantages are mentioned, too. Main attention is paid to estimation of the TDMS system developed by OJSC Dimrus together with Magnitogorsk State Technical University and OJSC Magnitogorsk Iron and Steel Works. The modular configuration is under consideration. System units and sensors installed at ESMS transformers are listed. The study provides a list of main functions and technological parameters controlled by the system. It describes the MINITRANS device designed for gas and oil moisture control and installed at transformer of LF No. 3 and the unit of built-in sensors used within the transformer monitoring system of LF No. 1. Interface of the TDMS system is considered; main views of the application are also provided. The paper suggests the threshold values of main diagnostic parameters reflecting transformer technical condition. Technical and economical benefits resulted from the system introduction should be noted.

음향신호의 유중 전파특성 분석

This paper dealt with the propagation characteristics of acoustic signal in insulation oil for the purpose of improving the reliability of AE (acoustic emission) method used for condition monitoring of oil-immersed transformers. A discharge source was placed in insulation oil and AE sensors ( :140 kHz) were attached on the oil tank to study the changes of velocity and propagation path with the depth and distance. The average velocity was 1,436 m/s and the velocity decreased with the increase of depth from the oil surface to 430 mm. The propagation paths were classified into three sections by the shortest reflection path of the detected signal. The minimum distinguishable distance in each section was 70 mm. It was also verified that PD (partial discharge) with a magnitude over than 500 pC can be detected by the AE sensors.

Condition Monitoring of Power Transformer Using Different Techniques

Transformer is one of the key equipment of the power system. Power rating of the transformers varies from KVA to several hundreds of MVA. The transformer is an expensive equipment of the power system. Failures of transformer usually lead to substantial profit loss to the utility, potential environmental damage, explosion and fire hazards and expensive repairing or replacement costs; thus, it is desirable that the maximum service life of transformer is required. Condition monitoring of transformer can help to increase the life of the transformer and reduce the maintenance cost. On-line monitoring is the record of significant data of a transformer and analysis of data including the history of the transformer. In this paper, we present the different techniques adopted for condition monitoring of transformer. Keywords: Power transformer, insulation conditioning, winding condition, condition monitoring techniques Cite this Article Pravin Rathod, Ankit Shah Patel, Sameer Patel, Chirag Parekh . Condition Monitoring of Power Transformer Using Different Techniques . Trends in Electrical Enginerring . 2016; 6(2): 1–8p.

Analysis of Partial Discharge Signal Characteristics of Nano-Mineral Oil for Transformer Condition Monitoring Applications

Transformer is an expensive and key component in an electric power transmission network. Most of the failure of transformer occurs due to degradation of insulation oil, which further leads to major economical losses and power shutdown. Advancements in nanotechnology has given horizons for the development of nanofluids for transformer insulation applications. Understanding the enhancements in dielectric properties of nanofiller added mineral oil is a hot research area. In recent times, partial discharge (PD) detection and analysis is a well recognized insulation condition monitoring technique for transformers. However, reports on PD signal characteristics of nano filler added mineral oil is scanty. This paper attempts to investigate the PD characteristics of SiO2 nanofiller added mineral oil at different %wt concentrations of nanofiller material. Major PD source normally experienced in transformers i.e. corona discharge due to non-uniform electric field was simulated in the laboratory using needle-plane electrode configuration and the corresponding PD signals were measured using wide band PD detection system. Important PD characteristics such as inception voltage, stable PD formation voltage, PD amplitude with respect to increase in voltage stress and PD signal frequency spectrum were evaluated. Results clearly show that addition of SiO2 nanofiller material in the base mineral oil enhances PD performance of mineral oil and these results will be useful for electrical utilities for developing effective condition monitoring system for transformers.

Condition monitoring of high voltage transformers using Fiber Bragg Grating Sensor

High voltage (HV) power apparatus determines the stability of any electrical power system. Failure of such apparatus is mainly due to damage of insulation normally caused by the Partial Discharge (PD). Therefore PD measurements are globally considered as most essential and favored testing procedure for most of the HV power apparatus. Online condition monitoring may help to take proper corrective action at proper time, which reduces unscheduled downtime and can save money for electrical supply utilities.HV power transformers are being considered as heart of any electrical system and they have received much attention in recent years. In HV transformers, PD is one of the main reasons for degradation of insulation quality. Therefore its detection can provide early indication of the insulation failures of transformers. PD can be traced through direct measurement of current, electromagnetic radiation, chemical changes or acoustic emission. Electrical, acoustic, chemical and optical methods of PD measurement are the popularly accepted methods of PD detection in the high voltage (HV) power transformers. Over the past decades various PD sensors have been developed, most of these require electrical connections closed to measurement site and electrical wirings and amplifiers for transmission of measured signals. Placing electrical components and wirings closer to HV terminals is difficult for installation of such sensors; moreover, due to proximity of HV terminals these sensors suffer from electromagnetic interference (EMI).In this context, development of optical fiber based PD sensor can be promising as the optical fibers are made of dielectric material so can be easily placed inside the equipment without any additional insulation arrangements. On the other hand, it has very low transmission loss; therefore, the measured signal can be transmitted to the sub-station control room without use of any electrical amplification system and it is free from EMI. In this work, a Fiber Bragg Grating (FBG) based optical PD sensor has been developed for online detection of PD within HV power transformer. The shift in wavelength of FBG due to impulsive acoustic pressure generated during PD event has been used for the detection. The performance of the developed FBG based sensor has been validated experimentally with standard electrical (IEC60270) PD measurement system. Experimental results show that the developed sensor is a suitable substitute for conventional PD measuring system and it can be placed inside the apparatus.

Smart Transformer for Smart Grid—Intelligent Framework and Techniques for Power Transformer Asset Management

Condition monitoring and diagnosis have become an essential part of power transformer asset management. A variety of online and offline measurements have been performed in utilities for evaluating different aspects of transformers' conditions. However, properly processing measurement data and explicitly correlating these data to transformer condition is not a trivial task. This paper proposes an intelligent framework for condition monitoring and assessment of power transformer. Within this framework, various signal processing and pattern recognition techniques are applied for automatically denoising sensor acquired signals, extracting representative characteristics from raw data, and identifying types of faults in transformers. This paper provides case studies to demonstrate the effectiveness of the proposed framework and techniques for power transformer asset management. The hardware and software platform for implementing the proposed intelligent framework will also be presented in this paper.

A new fuzzy logic approach to identify power transformer criticality using dissolved gas-in-oil analysis

Abstract Dissolved gas analysis (DGA) of transformer oil is one of the most effective power transformer condition monitoring tools. There are many interpretation techniques for DGA results however all current techniques rely on personnel experience more than analytical formulation. As a result, the current techniques do not necessarily lead to the same conclusion for the same oil sample. A significant number of DGA results fall outside the proposed codes of the ratio-based interpretation techniques and cannot be diagnosed using these methods. Moreover, ratio methods fail to diagnose multiple fault conditions due to the mixing up of produced gases. To overcome these limitations, this paper introduces a new fuzzy logic approach that aids in standardizing DGA interpretation and identifies transformer critical ranking based on DGA data. The approach relies on incorporating all traditional DGA interpretation techniques (Roger, Doerenburg, IEC, key gas and Duval triangle methods) into one expert model. In this context, DGA results of 338 oil samples of pre-known fault conditions that were collected from different transformers of different rating and different life span are used to establish the model. Traditional DGA interpretation techniques are used first to analyze the DGA results to evaluate the consistency and accuracy of each method in identifying various faults. Results of this analysis were then used to develop the proposed fuzzy logic model. The model is validated using another set of DGA data that were collected form previously published papers.

Selection of Most Relevant Input Parameters Using Waikato Environment for Knowledge Analysis for Gene Expression Programming Based Power Transformer Fault Diagnosis

The diagnosis of incipient fault is important for power transformer condition monitoring. Incipient faults are monitored by conventional and artificial intelligence based models. Key gases, percentage value of gases, and ratio of the Doernenburg, Roger, IEC methods are input variables to artificial intelligence models, which affects the accuracy of incipient fault diagnosis, so selection of the most influencing relevant input variable is an important research area. With this main objective, Waikato Environment for Knowledge Analysis software is applied to 360 simulated samples having different operating lives to find the most influencing input parameters for incipient fault diagnosis in the gene expression programming model. The Waikato Environment for Knowledge Analysis identifies%C2H2,%C2H4,%CH4, C2H6/C2H2, C2H2/C2H4, CH4/H2, C2H4/C2H6, and C2H2/CH4 as the most relevant input variables in incipient fault diagnosis, and it is used for fault diagnosis using different artificial intelligence methods, i.e., artificial neural networks, fuzzy logic, support vector machines, and gene expression programming. The compared results shows that gene expression programming gives better results than the artificial neural network, fuzzy logic, and support vector machine with accuracy variation from 98.15 to 100%, proving the gene expression programming method can be used in transformer fault diagnosis research.

An experimental study of winding vibration of a single-phase power transformer using a laser Doppler vibrometer

Transformer tank vibration has been used for the condition monitoring and fault diagnosis of power transformers, as well as for the evaluation of radiated sound power from such power transformers. In both applications, seeking a correlation between transformer tank vibration and the vibration of the transformer windings and core becomes a challenge for fault identification and noise control at the sources of vibration and noise. The purpose of this paper is to experimentally investigate the winding vibration of an electrically live power transformer and characterize the changes in the spatial and frequency features of the vibration as various mechanical faults are introduced to the transformer winding. To avoid the effects of transducer loading and electromagnetic fields on the measurement results, a laser Doppler vibrometer was used to make non-contact measurements of the winding vibration.

Chromatic acoustic condition monitoring of transformers

Purpose – The purpose of this paper is to present an approach for continuous acoustic condition monitoring of transformers based on chromatic principles for abstracting information on individual acoustic events as well as secondary trends in the behaviour of the events. Design/methodology/approach – The potential benefits of condition monitoring of high-value transformer equipment are explored, and an approach based on chromatic information abstraction is illustrated and discussed. Findings – Tracking of large numbers of complex and variable individual acoustic events over time using a chromatic approach appears to offer a means for remote operators to evaluate mechanical transformer tap changer condition in a traceable manner. Originality/value – The condition monitoring is retrofittable and non-intrusive, and the approach may be applied generically for combining condition indicators for overall health-checking. A complex system behaviour may be operationally simplified without discarding the complexity.

<b>Performance evaluation of transformer oil using uv-visible spectrophotometer<b>

To ensure reliable operation of Power transformer, condition monitoring of transformer becomes obligatory. In this paper, condition assessment of transformer oil under various working conditions is performed using UV-visible spectrophotometer. Test samples include both laboratorial prepared ones and those obtained from in service transformers. An index table of spectral responses for various faults which predominantly occur in the transformer is developed. The index table will acts as a reference for spectral response of faulted transformer oil. In this paper, an attempt has been made to determine the quality of transformer oil using their spectral response characteristics.

An Improvement of IED Configuration Tool and Modeling for Transformer Condition Monitoring IED

This paper analyzes the object-oriented modeling technology of IEC 61850 standard and the modeling steps of condition monitoring intelligent electronic devices (IED). Aiming at the problem of the existing configuration tools poor scalability for the IEC 61850 standard, a new design method of the IED configuration tool is brought up. Information model of transformer condition monitoring IED is established including logical device (LD), logical node (LN), data object (DO), data attribute (DA), dataset and report control block with the method mentioned above, and described with substation configuration description language.

An Improvement of IED Configuration Tool and Modeling for Transformer Condition Monitoring IED

This paper analyzes the object-oriented modeling technology of IEC 61850 standard and the modeling steps of condition monitoring intelligent electronic devices (IED). Aiming at the problem of the existing configuration tools poor scalability for the IEC 61850 standard, a new design method of the IED configuration tool is brought up. Information model of transformer condition monitoring IED is established including logical device (LD), logical node (LN), data object (DO), data attribute (DA), dataset and report control block with the method mentioned above, and described with substation configuration description language.

SFRA, Detect Of Winding Deformation in Power Transformer

The sweep frequency response analysis is extensively used technique for detect hidden fault and condition monitoring of power transformer. The operation is carried out by supply a low voltage signal of varying frequencies to the transformer windings and measuring both the input and output signals. These two signals give the required response of the ratio is called the transfer function of the transformer from which both the magnitude and phase can be obtained. Frequency response is change as measured by SFRA techniques may indicate a physical change inside the transformer, and then causes of fault identified and investigation is required for root cause analysis.

An efficient diagnosis method for data mining on single PD pulses of transformer insulation defect models

Reviewing the various Partial Discharges (PD data mining researches which have been reported so far, this study compares the performance of different feature spaces and different classifiers employed for PD classification in insulation condition monitoring of power transformers. In this process, first a knowledge basis is developed through construction of 4 different types of PD models in the high voltage laboratory. Background noise is considered as one class in this knowledge basis. The high frequency time domain current signals of high voltage equipment are captured over one power frequency cycle. The single PD activities within this captured signal are extracted by application of a threshold-based method. Four popular feature extraction methods i.e. Statistical, texture, FFT and Cepstral features are applied on these recorded extracted PD signals. To distinguish the different PD types, three conceptually different classifier types, Neural Network, Decision Tree, and k-nearest neighbours, are applied on the recorded feature spaces. Using Bayesian theory, a performance analysis is carried out to find whether the classifiers are over-fitted or not. Although, the most reliable data mining tool found to be a combination of a Cepstral feature space, and neural network classifier however, since the statistical features can be computed very fast it is employed in this work. Next, it is proposed to use a cascade PD identifier to find whether the detected signal is noise or not. And if it is PD, employing Cepstral feature space knowledge-basis, its type is identified.

A new fuzzy logic approach for consistent interpretation of dissolved gas-in-oil analysis

Dissolved gas analysis (DGA) of transformer oil is one of the most effective power transformer condition monitoring tools. There are many interpretation techniques for DGA results however all these techniques rely on personnel experience more than analytical formulation. As a result, various interpretation techniques do not necessarily lead to the same conclusion for the same oil sample. Furthermore, significant number of DGA results fall outside the proposed codes of the current based-ratio interpretation techniques and cannot be diagnosed by these methods. Moreover, ratio methods fail to diagnose multiple fault conditions due to the mixing up of produced gases. To overcome these limitations, this paper introduces a new fuzzy logic approach to reduce dependency on expert personnel and to aid in standardizing DGA interpretation techniques. The approach relies on incorporating all existing DGA interpretation techniques into one expert model. DGA results of 2000 oil samples that were collected from different transformers of different rating and different life span are used to establish the model. Traditional DGA interpretation techniques are used to analyze the collected DGA results to evaluate the consistency and accuracy of each interpretation technique. Results of this analysis were then used to develop the proposed fuzzy logic model.

Research of Intelligent Substation Condition Monitoring Based on the Neural Network

The substation electric power equipment condition monitoring is the basis of intelligent substation. This paper analyzes the composition of the substation electric power equipment condition monitoring system and monitoring parameters, and with the transformer condition monitoring as an example, this paper proposes fault diagnosis methods of electric power equipment using artificial neural network(ANN).

Pattern Recognition for Partial Discharge in Power Transformer Based on Probabilistic Neural Networks

Partial discharge diagnosis plays an important role in condition monitoring of power transformer. Different discharge types cause varying degrees of effects on insulation degradation. Therefore pattern recognition for partial discharge is necessary for power transformer fault diagnosis. Probabilistic Neural Networks (PNN) is based on the theory of Bayesian minimum risk which perfectly fits for classification. In this paper PNN is employed for pattern recognition of partial discharge. 18 characteristic parameters are extracted from UHF partial discharge signals as the inputs of the PNN classifier. Results show that the PNN method gives fast and accurate classification performance.

Research on Electrical Power System with Information Modeling and SCL Description of IED for Transformer Condition Monitoring Based on IEC61850

A transformer is a critical device in an electrical power system substation. Monitoring its condition, including insulation material statuses, is imperative. This article discusses the information modeling techniques of IEC61850 and modeling steps of condition-monitoring IED, analyzes the reporting service, focuses on the characteristics of transformer condition monitoring, rationally develops and makes use of logical nodes and data in the IEC61850 standard, conducts modeling for the actual intelligent electronic device (IED) of transformer condition monitoring and describes the model with substation configuration description language (SCL). The configuration file of this model has passed the test done with standard syntax (Schema mode).

Condition Monitoring of Distribution Transformer's Mechanical Parts Using Sweep Frequency Response Analysis (SFRA)

Distribution transformer is the key element in the electricity transmission throughout the nation. Hence, serious attention for the transformer condition monitoring is a crucial. With an occurrence of short-circuit in the power system, the transformer active parts such as core and winding will experience a mechanical movement created by the electromagnetic forces. There are various types of transformer condition monitoring measurement in market, but the monitoring of the mechanical movement in transformer is still lacking. Therefore, Sweep Frequency Response Analysis (SFRA) has been introduced to assess any mechanical movement especially of the transformer's core and winding. The frequency response of the transformer provides mechanical information of core and winding conditions. The SFRA measurement results were validated using the actual transformer untanking process.