Oil-filled Power Transformers Research Articles

Copper-in-oil dissolution and copper-on-paper deposition behavior of mineral insulating oils

Copper-in-oil dissolution and copper-on-paper deposition may affect the insulation of oil-filled power transformers, shunt reactors and high voltage bushings. Dissolved copper increases the dielectric losses in oil, and copper deposition can dramatically increase insulating papers' conductivity. The available literature does not report researches where the mechanisms of dissolution of copper in oil and copper deposition on the surface of solid insulation (Kraft paper) have been elucidated. This study is aimed at the investigation of the tendency of some commercially available unused mineral oils of different compositions to dissolve copper and to deposit it in the cellulose phase. Different conditions of reaction, presence of oxidation inhibitor (of the hindered-phenols family), addition of triazole-type compound (used as corrosion inhibitor) and general oil properties were studied to investigate their influence on copper concentration in oil and onto paper after artificial aging in controlled conditions.

Combined Molecularly Imprinted Polymer and Surface Plasmon Resonance Transduction in Plastic Optical Fiber for Monitoring Oil-filled Power Transformers

Abstract In this work the feasibility of an optical method for an early and high sensitivity detection of chemical agents in oil-filled power transformer is reported. It concerns the assessment of accelerated ageing of transformer insulating systems, primarily constituted of oil, paper and cellulose through an online detection of cellulose degradation by-products. The proposed optical device exploits the Surface Plasmon Resonance (SPR) excitation in a plastic optical fiber (POF) platform. High selectivity is obtained by a joint detection with a Molecularly Imprinted Polymer (MIP) layer, very specific for one of the most significant indicators of the transformer possible failure, furfural, in contact with the SPR active surface. Preliminary results of tests, carried out on calibrated furfural concentration diluted in transformer oil, confirm the feasibility of the proposed optical approach.

Hydrodynamics and heat transfer in cooling channels of oil-filled power transformers with multicoil windings

The analysis of heat transfer and oil flow in power transformers at natural convection of cooling oil is carried out by numerical simulation of the fluid dynamic problem in axisymmetrical formulation (CFD-approach). The effects of self-organization structure of oil flowing in the form of the unidirectional flow in the groups of horizontal channels between winding coils have been revealed.The flow features have an influence on the thermal state of winding coils of the transformer with the natural cooling system. At such oil flowing, in different channels, the heat transfer coefficient varies within the limits of 50–100 W/(m2 K), and the radial component of velocity is changed over the range of 2.1·10−4–2.2·10−3 m/s. Thus the region with maximum temperature values is removed towards oil flowing along the coil radius.

Oil-Filled Power Transformers Fault Diagnosis Based on Fuzzy-DRNN

In recent years, improved three-ratio is an effective method for transformer fault diagnosis based on Dissolved Gas Analysis (DGA). In this paper, a simple dynamic neural network named as diagonal recurrent neural network (DRNN) is used to resolve the online fault diagnosis problems for oil-filled power transformer based on DGA. Because of the characteristic of improved three-ratio boundary is lack of matching, fuzzy logic in fault diagnosis is presented also to deal with the data of the neural network inputs. DRNN is used to model the fault diagnosis structure, the fuzzy logic is used to improve the faults diagnose reliability. In addition, some cases are used to show the capability of the suggested method in oil-filled power transformers fault diagnosis.

Medium Voltage Smart Grid: Experimental Analysis of Secondary Substation Narrow Band Power Line Communication

This paper is focused on the possibility of employing narrow band power line communication (PLC) in medium voltage (MV) distribution networks. The topic is investigated by means of both simulations and experimental tests, which were carried out in the distribution network of Ustica Island. In detail, a two-way MV communication was tested between two secondary substations connected by an MV cable power line. Each substation has a by-pass connection at MV bus-bars and an MV/LV oil filled power transformer. In this paper, the MV and the PLC systems under test are described and the experimental tests are presented, which were carried out to evaluate the parameters of the simulation model. The aforesaid model is described and a comparison is shown between simulation and results of experiments, which were carried out in the presence of voltage net, i.e., 24 kV. Finally, the possibility is investigated of a reliable communication and the faster achievable bit-rate; the analysis is performed by means of further experimental results of several communication tests, which were carried out with different modulation techniques.

Power Transformers Fault Diagnosis Based on Fuzzy-RBF Neural Network

Dissolved Gas Analysis (DGA) is a popular method to detect and diagnose different types of faults occurring in power transformers. Improved three-ratio is an effective method for transformer fault diagnosis used in recent years. This paper applies appropriate Artificial Neural Networks (ANN) to resolve the online fault diagnosis problems for oil-filled power transformer based on improved three-ratio. Because of the characteristic of improved three-ratio boundary is too absolute, a method using fuzzy math theory to deal with the data of the neural network input is also proposed. A major kind of neural network, i.e. radial basis function neural network (RBFNN), is used to model the fault diagnosis structure. In addition, to improve the convergence speed, an improved gradient descent algorithm is used in training RBFNN. Through on-line monitoring the concentrations of the dissolved gases, the proposed diagnostic system can offer a way to interpret the incipient faults. The simulation diagnosis demonstrates the effectiveness and veracity of the proposed method.

Buoyancy driven flow in counter flow heat exchangers

The temperature distribution, the buoyancy head and the flow rate have been studied in a counter flow heat exchanger having buoyancy driven flow on at least one side. The assumptions made for heat flux distribution are varied and the resulting effects on the flow rate and fluid temperatures are studied. A network model is used to simulate the temperature distribution and oil flow rates in an oil-filled power transformer cooled by radiators. It is found that for operating conditions normally found for mineral oil the counter flow assumptions for heat flux distribution gives approximately the same results as assuming uniform heat flux. When a more viscous oil type is used or the radiators are placed lower than normal relative to the heat generating parts, the counter flow assumptions give more reliable results.

Oil-Filled MV/LV Power-Transformer Behavior in Narrow-Band Power-Line Communication Systems

The aim of this paper is to characterize oil-filled power transformers in the narrow-band power-line communication (PLC) frequency range of 50–150 kHz. A new model is proposed for such type of a transformer, in the aforementioned frequency range of interest, in order to study its influence on the signal transmission and to investigate the possibility that the PLC signal can cross the power transformer itself. The new model is simpler than those previously proposed in literature, and it can be used in the whole frequency range available for NB PLC according to the CENELEC EN 50065-1 A-band. In order to validate the model, simulation and measurement results are compared for four oil-filled power transformers with different rated power values. A complete PLC system in the case of a medium-voltage (MV) power network is also simulated, in order to perform a preliminary evaluation of the influence of power transformers on the communication system. The MV overhead power line, the oil-filled power transformers, the MV signal coupling network, and two ST7540 frequency shift keying (FSK) power-line transceivers are included in the simulated system. The performances of the complete PLC communication system are evaluated in the case of both MV to low voltage (LV) and LV to MV transmissions. The analysis is carried out by evaluating the signal attenuation, which is computed as the ratio between the received and transmitted voltage signals, in the case of different line lengths and carrier frequency values.

Fiber laser sensor based on a phase-shifted chirped grating for acoustic sensing of partial discharges

Acoustic emission monitoring is often used in the diagnosis of electrical and mechanical incipient faults in the high voltage apparatus. Partial discharges are a major source of insulation failure in electric power transformers, and the differentiation from other sources of acoustic emission is of the utmost importance. This paper reports the development of a new sensor concept — a fiber laser sensor based on a phase-shifted chirped fiber grating — for the acoustic emission detection of incipient faults in oil-filled power transformers. These sensors can be placed in the inner surface of the transformer tank wall, not affecting the insulation integrity of the structure and improving fault detection and location. The performance of the sensing head is characterized and compared for different surrounding media: air, water, and oil. The results obtained indicate the feasibility of this sensing approach for the industrial development of practical solutions.

Prediction and evaluation of the cooling performance of radiators used in oil-filled power transformer applications with non-direct and direct-oil-forced flow

Lifetime and reliability of a power transformer are highly dependent on cooling modes as excessive heat generation is the major cause of deteriorating insulation performance. For these reasons, optimal cooling design is necessary to avoid operating faults and materials degradation due to thermal damage. This paper presents prediction and experimental study on the cooling performance of radiators used in oil-filled power transformer applications with non-direct flow (ONAN) and direct-oil-forced flow (ODAN). Radiator temperature distribution and cooling performance was predicted using theoretical calculations, then validated using CFD simulation results. In the experiment, cooling capacity was evaluated with the ONAN and the ODAN flow. For ODAN flow, the maximum cooling capacity was enhanced 20.1% more than ONAN flow. These results confirm that the prediction and the evaluation of radiator cooling performance can be applied to design optimization of cooling mode for oil-filled power transformer applications.

Make Use of DGA to Carry Out the Transformer Oil-Immersed Paper Deterioration Condition Estimation with Fuzzy-Logic

Remaining Life of the oil-immersed transformer is decided due to deterioration of the winding insulation paper (WIP). The DGA method is conventionally used to estimate the WIP deterioration status condition. This paper presented the four status conditions assessment of paper deterioration for oil-immersed transformer using fuzzy-logic (FL). In this paper the correlation between accumulated values of carbon dioxide (CO2) and carbon monoxide (CO) with insulation resistance in oil-filled power transformers is studied using FL. The authors have estimated the insulation paper deterioration condition using proposed method for 20 transformers or more. As a result, appropriate maintenance scenario can be planned.

Particle swarm optimization-least squares support vector regression based forecasting model on dissolved gases in oil-filled power transformers

This paper presents a forecasting model based upon least squares support vector machine (LS-SVM) regression and particle swarm optimization (PSO) algorithm on dissolved gases in oil-filled power transformers. First, the LS-SVM regression model, with radial basis function (RBF) kernel, is established to facilitate the forecasting model. Then a global optimizer, PSO is employed to optimize the hyper-parameters needed in LS-SVM regression. Afterward, a procedure is put forward to serve as an effective tool for forecasting of gas contents in transformer oil. The application of the proposed model on actual transformer gas data has given promising results. Moreover, four other forecasting models, derived from back propagation neural network (BPNN), radial basis function neural network (RBFNN), generalized regression neural network (GRNN) and support vector regression (SVR), are selected for comparisons. The experimental results further demonstrate that the proposed model achieves better forecasting performance than its counterparts under the circumstances of limited samples.

Mandrel-Based Fiber-Optic Sensors for Acoustic Detection of Partial Discharges—a Proof of Concept

Acoustic emission monitoring is often used in the diagnosis of electrical and mechanical incipient faults in high-voltage apparatus. Partial discharges are a source of failure in power transformers, and the differentiation from other sources of acoustic emissions is of the utmost importance. This paper reports the development of a new sensor concept-mandrel-based fiber-optic sensor-for the detection of incipient faults in oil-filled power transformers, taking direct measurements inside a transformer. These sensors can be placed in the inner surface of the transformer tank wall, not affecting the insulation integrity of the structure, and improving fault detection and location. The applicability of these acoustic sensors in air, water, and oil is investigated and the paper presents the promising results obtained, which will enable the industrial development of practical solutions.

Preliminary results for dissolved gas levels in a vegetable oil-filled power transformer

In this article the concentrations of dissolved gases in the FR3 fluid of the new power transformer, measured during factory testing and the early months of operation, are compared with those found in earlier laboratory research. It is hoped that this comparison will assist industry in defining more precisely the dissolved gas concentrations characteristic of the normal operation of a power transformer using FR3 fluid as insulation.

Extrinsic and intrinsic fiber optic interferometric sensors for acoustic detection in high-voltage environments

Incipient fault diagnosis is closely related to insulation condition assessment. A great number of methods are available for condition monitoring and diagnosis of power transformer insulation systems, but only few of them can take direct measurements inside the transformer. Fiber optic sensors can be applied to incipient fault diagnosis. In particular, acoustic sensors have been developed for detection and location of partial discharges in oil-filled power transformers. We report the study of extrinsic and intrinsic fiber Fabry-Perot sensors that can be used to detect the acoustic waves that are generated by a partial discharge inside a power transformer. A comparative analysis is done to determine the best sensing head configuration and some methods to improve the parameter readout sensitivity are proposed. The sensing head behaviour when immersed in different fluids (air, water, and oil) is also investigated.

Improving detection sensitivity for partial discharge monitoring of high voltage equipment

Partial discharge (PD) measurements are an important technique for assessing the health of power apparatus. Previous published research by the authors has shown that an electro-optic system can be used for PD measurement of oil-filled power transformers. A PD signal generated within an oil-filled power transformer may reach a winding and then travel along the winding to the bushing core bar. The bushing, acting like a capacitor, can transfer the high frequency components of the partial discharge signal to its earthed tap point. Therefore, an effective PD current measurement can be implemented at the bushing tap by using a radio frequency current transducer around the bushing-tap earth connection. In addition, the use of an optical transmission technique not only improves the electrical noise immunity and provides the possibility of remote measurement but also realizes electrical isolation and enhances safety for operators. However, the bushing core bar can act as an aerial and in addition noise induced by the electro-optic modulation system may influence overall measurement sensitivity. This paper reports on a machine learning technique, namely the use of a support vector machine (SVM), to improve the detection sensitivity of the system. Comparison between the signal extraction performances of a passive hardware filter and the SVM technique has been assessed. The results obtained from the laboratory-based experiment have been analysed and indicate that the SVM approach provides better performance than the passive hardware filter and it can reliably detect discharge signals with apparent charge greater than 30 pC.

Knowledge-based diagnosis of partial discharges in power transformers

The abstraction of meaningful diagnostic information from raw condition monitoring data in domains where diagnostic expertise and knowledge is limited and constantly evolving presents a significant research challenge. Expert diagnosis and location of partial discharges in high voltage electrical plant is one such domain. This paper describes the functionality of a knowledge-based decision support system capable of providing engineers with a comprehensive diagnosis of the defects responsible for partial discharge activity detected in oil-filled power transformers. Plant data captured from partial discharge (PD) sensors can be processed to generate phase-resolved partial discharge (PRPD) patterns. This paper proposes a means of abstracting the salient features characterizing the observed PRPD patterns. Captured knowledge describing the visual interpretation of these patterns can be applied for defect diagnosis and location. The knowledge-based PRPD pattern interpretation system can support on-line plant condition assessment and defect diagnosis by presenting a comprehensive diagnosis of PD activity detected and classification of the defect source. The paper also discusses how the system justifies its diagnosis of the PD activity to offer the expert greater confidence in the result, a feature generally absent in 'black-box' pattern recognition techniques. The incremental approach exhibited by the system reflects that of a PD expert's visual interpretation of the PRPD pattern. The paper describes how this functional system design has evolved from the approach taken by PD experts to the visual interpretation of PRPD patterns.

Cellulose insulation in oil-filled power transformers: part II maintaining insulation integrity and life

This paper examines the practical aspects of the usage of cellulose insulation in transformers with regard to maintaining the integrity over long periods of time and preserving and extending insulation life. The factors that relate to loss of life are examined and life plots are discussed in more detail.

Cellulose insulation in oil-filled power transformers: Part I - history and development

This paper presents a brief discussion on the development of electrical grade paper/pressboard for transformer use from the raw materials, improvements made, and particularly the use of thermal upgrading agents to extend the useful life of transformers

A fuzzy ARTMAP fault classifier for impulse testing of power transformers

The work presents an artificial intelligence (AI) based impulse test technique for oil filled power transformers. Determination of exact nature and location of faults, during impulse testing of large power transformer is of practical importance to the transformer manufacturers as well as designers. The presently available impulse test techniques more or less depend on expertise of the test personnel, and in many cases lead to ambiguity and controversy. The new AI approach presented in the paper overcomes the limitations of conventional test methods. This new technique relies on high discrimination power and excellent generalization ability of fuzzy neural networks in complex pattern classification problem. The proposed method employs a fuzzy ARTMAP pattern recognition technique to recognize the frequency responses of the winding admittance of high voltage transformers under healthy and different faulty conditions of winding insulation, and learns to establish the correlations between the nature and physical location of occurrence of an internal insulation fault in a transformer winding and its associated frequency response. The technique was tested on the winding model of typical high voltage transformer and yielded high diagnostic accuracy by successful detection and discrimination of faults of different nature and different site of occurrence in the high voltage winding.