Condition Assessment Of Power Transformers Research Articles

SnO<sub>2</sub> Nanofibers Gas Sensor Detecting H<sub>2</sub> Dissolved in Transformer Oil

Hydrogen is an effective fault gas dissolved in transformer oil, and online monitoring its concentration has important meaning on condition assessment and fault diagnosis of power transformer. A facile and simple synthesis method of ultra-sensitive SnO2 nanofibers through a hydrothermal approach was reported. The crystalline phases and microstructures were performed by X-ray powder diffraction and field-emission scanning electron microscopy. The gas sensor based on prepared SnO2 nanofibers was fabricated by a side-heated preparation, and its gas sensing performances to H2 were measured. The fabricated sensor exhibits excellent sensing properties to H2, such as low optimum operating temperature, high gas response, rapid response and recovery time, good stability and repeatability.

Recent Trends in Power Transformer Fault Diagnosis and Condition Assessment

Recent Trends in Power Transformer Fault Diagnosis and Condition Assessment

Recent Trends in Power Transformer Fault Diagnosis and Condition Assessment

Recent Trends in Power Transformer Fault Diagnosis and Condition Assessment

Recent Trends in Power Transformer Fault Diagnosis and Condition Assessment

Power transformers are interface between different voltage levels of essential importance. Because of the long manufacturing processes, transformers are one of the most critical and expensive equipment and so this is one of the reasons why condition monitoring becomes more popular. Monitoring systems as basis for diagnostics open the possibility for expanding the operating time, reducing the risk of expensive failures and allows several maintenance strategies. With different monitoring techniques, detailed informations about the transformer condition can be received and helps to minimize the probability of an unexpected outage. In this paper a methodology has been developed to use information derived from condition monitoring and diagnostics for rehabilitation purposes of transformers. The interpretation and understanding of the test data are obtained from the International Standards.

Application of neuro-fuzzy scheme to investigate the winding insulation paper deterioration in oil-immersed power transformer

In this paper, an attempt has been made to examine the effectiveness of Neuro-Fuzzy Scheme (NFS), to identify the deterioration of the winding insulation paper (WIP) in a oil-immerged power transformer, and to compare its performance over conventional methods (IEEE/IEC). The comparison of convergence characteristics of IEEE and IEC approach reveal that the NFS approach is quite faster in investigations leading to reduction in computational burden and give rise to minimal computer resource utilization. Simultaneous identification of deterioration of the WIP and operating conditions in oil-immersed power transformer has never been attempted in the past using NFS. The technique proposed in this paper provides not only best dynamic response for the deterioration of the WIP diagnosis and condition assessment of power transformer but also present its appropriate maintenance scenario as well. This approach will address a proactive assertion to the power utilities for effective realization of electrical health of oil-immersed power transformer under consideration. In this paper, testing analysis of 25 transformer samples has been carried out to demonstrates the robustness of the investigated four status conditions (Normal Operation – NO; Modest Concern – MCI; Major Concern – MCMI and Imminent Risk Failure – IRF) for wide changes in operating condition and loading condition perturbation.

A New Approach of Determination of Comprehensive Weight in Condition Assessment of Power Transformer

The weight of evaluation index plays a key role in condition assessment and maintenance control of power transformer. A new approach of determination of comprehensive weight is proposed in this paper. The approach combines the entropy objective weight with the expert subjective weight together in a specific form, which not only avoids the situation that single weight determines the overall weight, but considers the primary and secondary problem between the two weights. The comprehensive weight calculated by this approach can reflect the place of every index in condition assessment of power transformer well. Empirical results show that the approach is reasonable and available.

Condition Assessment of Power Transformers based upon Weighted Criteria OWA Operators and Fuzzy Model

Condition Assessment of Power Transformers based upon Weighted Criteria OWA Operators and Fuzzy Model

Statistical learning techniques and their applications for condition assessment of power transformer

The condition of power transformers has a significant impact on the reliable operation of the electric power grid. A number of techniques have been in use for condition assessment of transformers. However, interpreting measurement data obtained from these techniques is still a non-trivial task; correlating measurement data to transformer condition is even more difficult. This paper investigates statistical learning techniques, which is able to learn statistical properties of a system from known samples and to predict the system output for unknown samples. Within the statistical learning framework, this paper develops a support vector machine (SVM) algorithm, which can be utilised for automatically analyzing measurement data and assessing condition of transformers. Case studies are presented to demonstrate the applicability of the developed algorithm for condition assessment of power transformer.

On-site PD Diagnostics, FRA and Moisture Measurement for Power Transformers

The paper presents three methods for condition assessment of power transformers which can be applied on-site. A combination of UHF and acoustic partial discharge (PD) measurement methods enables to detect PD in power transformers and to improve interpretation and localisation of their sources. Frequency response measurements are a widely applied technique for power transformer winding deformation detection. Dielectric response methods deliver results about the moisture condition of a transformer.

Significance of cellulose power transformer condition assessment

Power transformers are designed to withstand system abnormalities such as over voltages and external short-circuits. Transformer monitoring and diagnostics are the effective techniques in preventing the eventual failures and contribute to ensure the plant's reliability. Transformer life mainly depends on the integrity of its solid insulation (cellulose). The solid insulation in transformers degrades with time at rates which depend on the temperature and the amount of moisture, oxygen and acids in the insulation system. Moisture and oxygen cause the paper insulation to decay much faster than normal. Moisture in a transformer accelerates the aging process and causes severe deterioration in the mechanical and electrical properties of insulation system. The aging of paper insulation is irreversible. The degree of polymerization value is a main relation between insulation deterioration and formation of aging products. To predict a failure or an unusual behavior, an accurate interpretation of monitoring, diagnostics and maintenance data is required. The monitoring of insulation system and verification of core and winding mechanical integrity allow optimization of the lifecycle management of an asset. This paper presents an investigation and condition assessment of 152 MVA, 512 kV, 1-phase, 2-winding generator step-up transformer having a history of combustible gases.

An Integrated Decision-Making Model for Condition Assessment of Power Transformers Using Fuzzy Approach and Evidential Reasoning

This paper presents an integrated model based upon the fuzzy approach and evidential reasoning decision-making approach to condition assessment of power transformers. An assessing index system, including the DGA data, electrical testing, and oil testing, is established to facilitate the assessing model. The model is composed of two levels, that is, the fuzzy model and evidential reasoning model. The fuzzy model is proposed for generating the original basic probability assignments for the second-level model. Afterwards, an evidential reasoning decision-making model is utilized to combine all of the evidence and give an overall evaluation conclusion. Based upon this integrated model, a decision-making procedure is put forward to serve as an effective tool for transformer condition assessments. The results show that the assessing model is capable of offering an overall evaluation of the observed transformer condition.

Expert system for the assessment of power transformer insulation condition based on type-2 fuzzy logic systems

An efficient expert system for the power transformer condition assessment is presented in this paper. Through the application of Duval’s triangle and the method of the gas ratios a first assessment of the transformer condition is obtained in the form of a dissolved gas analysis (DGA) diagnosis according IEC 60599. As a second step, a knowledge mining procedure is performed, by conducting surveys whose results are fed into a first Type-2 Fuzzy Logic System (T2-FLS), in order to initially evaluate the condition of the equipment taking only the results of dissolved gas analysis into account. The output of this first T2-FLS is used as the input of a second T2-FLS, which additionally weighs up the condition of the paper-oil system. The output of this last T2-FLS is given in terms of words easily understandable by the maintenance personnel. The proposed assessing methodology has been validated for several cases of transformers in service.

Condition Assessment of Power Transformers Using Loss Tangent Measurements

Mineral oil, cellulose paper and pressboard are the main insulation materials in a power Transformer. During service, increase of moisture, as the by -products ageing and increases the risk of insulation failure. This paper presents the possibility to use loss tangent (or power factor) and capacitance (tanδ and C) measurements as an estimate of the 'wetness' of aged transformer insulation. Firstly, oil, paper and pressboard samples were aged under laboratory conditions and their tan S and dielectric constant (er) values were measured. Applying the tanδ and er obtained onto a transformer insulation model, the tanδ and C of transformer were calculated and the effects of insulation geometry, moisture content (MC) of pressboard, oil conductivity (σ oil) and insulation temperature under test were established. It was found that tanδ and C values of a transformer strongly dep ended on its insulation geometry and the temperature. If the temperature is fixed, the change of tanδ value of a transformer can be a direct measure of moisture in pressboard for 'wet' and 'extremely wet' conditions. For a 'dry ' transformer, the σoil oil value is needed as an additional known parameter in order to estimate accurately the moisture in pressboard.

Insulation Condition Assessment of Power Transformers Using Accelerated Ageing Tests

Thermal stress due to losses and environment temperature causes degradation to paper/oil insulation systems in transformers, even at operating temperature. Experience indicates that thermal ageing of oil and paper in power transformers leads to the change of some insulation characteristics. In this paper, insulating papers immersed in oil have been acceleratory aged at 140, 150, and 160 °C under laboratory conditions. Some of the oil properties, such as water content, breakdown voltage, acidity, together with the aged insulating paper properties such as electric strength, dielectric dissipation factor and tensile strength were measured and analyzed. Also, insulation system conditions under thermal stress have been evaluated by electrical/distinctive techniques like recovery voltage, polarization and depolarization currents. Correlations between these parameters have been investigated. Finally, paper tensile strength has been used as a criterion to estimation of insulating paper life time.

EFFECT OF GEOMETRICAL ARRANGEMENT OF POWER TRANSFORMER INSULATION AND ITS TEMPERATURE ON DIELECTRIC RESPONSE MEASUREMENTS

This paper investigates the modelling of dielectric response measurements for power transformer condition assessment. The insulation of a power transformer consists of oil and cellulose (insulation paper). The dielectric properties of both materials generally change during the life of the transformer and dielectric measurements have therefore been applied to assess the quality of the transformer insulation system. Recently, a new method, the Frequency Domain Spectroscopy (FDS) measurements, has been applied which is based on the measurements of the dielectric response of the insulation in the frequency domain. In this paper, the effect of transformer insulation geometry and the insulation system temperature on the transformer insulation properties are studied. Different transformer insulation geometrical arrangements and their temperatures are simulated using the dielectric spectroscopy program MODS from General Electrical company (GE). It is used to model different cases of insulation geometry arrangement by changing the percentage ratio of oil and paper of insulation.

Monitoring and diagnostics of power transformer insulation

Liberalization of the energy market drives utilities to a more cost-effective power system. Power transformers are the most complex, important, and critical components of the transition and distribution power systems. Insulation system is the key component of life extension, better availability and higher reliability of a transformer. In order to achieve both decreasing operational cost and reliable service condition-based maintenance is needed. Monitoring and diagnostics methods and techniques, for insulation condition assessment of power transformers, are described. Date base and knowledge rules diagnostics management system, in internet oriented environment, is outlined. .

Condition assessment of power transformers using genetic-based neural networks

Genetic-based neural networks (GNNs) for the assessment of the condition of power transformers are presented. The GNNs automatically tune the network parameters, connection weights and bias terms of the neural networks, to yield the best model according to the proposed genetic algorithm. Due to the global search capabilities of the genetic algorithm and the highly nonlinear mapping nature of the neural networks, the GNNs can identify complicated relationships among the dissolved gas contents in the transformers insulation oil and hence the corresponding fault types. The proposed GNNs have been tested on the diagnostic records of the Taipower Company and compared with a fuzzy logic diagnosis system, artificial neural networks and a conventional method. The test results show that the proposed GNNs improve the diagnostic accuracy and the learning speed of the existing approaches.

Review of condition assessment of power transformers in service

As transformers age, their internal condition degrades, which increases the risk of failure. To prevent these failures and to maintain transformers in good operating condition is a very important issue for utilities. Traditionally, routine preventative maintenance programs combined with regular testing were used. The change to condition-based maintenance has resulted in the reduction, or even elimination, of routine time-based maintenance. Instead of doing maintenance at a regular interval, maintenance is only carried out if the condition of the equipment requires it. Hence, there is an increasing need for better nonintrusive diagnostic and monitoring tools to assess the internal condition of the transformers. If there is a problem, the transformer can then be repaired or replaced before it fails. An extensive review is given of diagnostic and monitoring tests, and equipment available that assess the condition of power transformers and provide an early warning of potential failure.

Fuzzy learning vector quantization networks for power transformer condition assessment

To improve the assessment capability of power transformers, this paper proposes a new intelligent decision support system based on fuzzy learning vector quantization (HVQ) networks. In constructing the system, a fuzzy-based classifier is designed to divide the historical data for dissolved gas analysis (DGA) into various categories with different levels of gas attributes. For each category of gas attributes, a learning vector quantization (LVQ) network is trained to be responsible for the classification of the potential faults due to insulation deterioration. The assessment approach has been tested on the DGA data from Taiwan Power Company (TPC) and compared with the previous fuzzy diagnosis system and the existing multi-layered backpropagation based artificial neural networks (BPANN) methods. Remarkable classification accuracy and far less training efforts of the proposed approach are achieved in this paper.