Power Transformer Failure Research Articles

FAILURE ANALYSIS OF A POWER TRANSFORMER USING DISSOLVED GAS ANALYSIS – A CASE STUDY

Reliable and continued performance of power Transformer is the key to profitable generation and transmission of electric power. Failure of a large power transformer not only results in the loss of very expensive equipment, but it can cause significant guarantied damage as well. Replacement of that transformer can take up to a year if the failure is not disastrous and can result in tremendous revenue losses and fines. A Power Transformer in operation is subjected to various stresses like thermal stress and electrical stress, resulting in liberation of gases from the hydrocarbon mineral oil which is used as insulant and coolant. Dissolved Gas Analysis is a technique used to assess incipient faults of the transformer by analyzing specific dissolved gas concentrations arising from the deterioration of the transformer. DGA is used not only as a diagnostic tool but also to track apparatus failure. In this case study the fault and defects that occurred in 400kV/220kV/132kV/66kV Sub Station can be found by DGA.

DEVLOPEMENT OF THE DYNAMIC RESISTANCE MEASUREMENT (DRM) METHOD FOR CONDITION ASSESSMENT OF OLTC

Many failure of power transformer are due to the failure of On Load Tap Changer. If we apply some diagnosis test during the function of OLTC, some of degradation effects can be assess. This paper concentrates on OLTC diagnostic method based on the measurement of dynamic resistance. Some experiments were applied to the OLTC using DRM. With the result of these experiments, it becomes possible to assess the condition of OLTC contacts and transient resistance.

A new diagnostic method to support standard frequency response analysis assessments for diagnostics of transformer winding mechanical failures

The standard frequency response analysis (FRA) is not fully efficient for diagnostics of mechanical failures in power transformers. The article recommends that physical electrical parameters of the transformers should be analyzed in addition to support the current FRA assessment.

Typowe przyczyny uszkodzeń eksploatacyjnych transformatorów energetycznych

Typical causes of operational failures in power transformers working in National Grid are presented in the paper. The main reasons for these failures have been discussed on the basis of post-failure transformer tests run during last 20 years. The failure statistics has constituted the grounds for making a list of typical failures and indicating transformer groups characterized by highest failure rates. The selected examples of power transformers' typical failures have been demonstrated, caused by damages to insulation systems, bushings. and on-load tap changers (OLTCs). Typical causes of operational failures in power transformers working in National Grid

Mathematical Model and Regression Analysis of Acoustic Emission Signals Generated by Partial Discharges

An improved mathematical model describing acoustic emission (AE) signals generated by different types of partial discharges (PD) that occur in electric power transformer insulation system is presented in the paper. AE signals are analyzed within the AE method as applied for power transformer failure detection due to occurrence of PD. There are several types of basic defects, which are characterized by different types of PD. The mathematical model presented here is crucial for numerical analyses and simulations, where it acts as the function describing the acoustic source in an acoustic model of power transformer insulation system. The regression procedure was performed based on empirical AE signals, registered in a laboratory experiment. The AE signals are described by a mathematical model being a multi-parameter function, which involve both the time domain and the frequency domain. Goodness of the model was evaluated based on analysis of 480 data samples in the time, frequency and time-frequency domains. Also coherence between the registered and modeled signals was calculated. It was stated that the improved model fits very well to the real data, although, due to high level of noise embodied in signals registered in experiments, the coherence values remain low. Moreover, analyses of the estimated data were performed and some example results are presented in this paper. Based on the achieved outcomes a collection of parameter values was prepared for each of the eight considered PD basic types. One can simple use it now in a numerical model for simulation of AE signal source generated by specified type of PD, what corresponds to a particular power transformer insulation system failure. Furthermore, the regression procedure presented in this paper can be easily transferred to any other types of AE sources including processes of compression, tension and cracking.

Fault Prediction Based on Dissolved Gas Concentration from Insulating Oil in Power Transformer Using Neural Network

Reliable and continued performance of power transformer is the key to profitable generation and transmission of electric power. Failure of a large power transformer not only results in the loss of expensive equipment, but it can cause significant guarantied damage as well. Replacement of that transformer can take up to a year if the failure is not disastrous and can result in tremendous revenue losses and fines. A power transformer in operation is subjected to various stresses like thermal stress and electrical stress, resulting in liberation of gases from the hydrocarbon mineral oil. Dissolved gas analysis is a technique used to assess incipient faults of the transformer by analyzing specific dissolved gas concentrations arising from the deterioration of the transformer. DGA is used not only as a diagnostic tool but also to track apparatus failure. In this research work the dissolved gas values measured for a 230kV / 110kV power transformer which are obtained from electricity board are used as references to the developed neural network. The neural network is trained and the transformer faults are predicted. The trained neural network shows the good performance for the prediction of fault in a 230kV/110kV power transformer.

전력용변압기의 구조진동해석 및 능동소음제어

Most cases of power transformer failure are caused by physical factors linked to the transient vibrations of multiple 120Hz combinations. In addition, the noise generated in the transformer from this vibration not only directly contributes to the worsening of the work environment but also causes psychological stress, resulting in the worsening of the workers efficiency and of the living environment of the inhabitants around the power plant. Thus, to remedy these problems, the mechanical-excitation forces working on a power transformer were categorized in this study, and the mechanical-damage mechanism was identified through the vibration transfer paths acting on machines or structures. In addition, a study on active noise cancellation in a transformer using the FXLMS algorithm was conducted to develop a system that is capable of multiple-sound/channel control, which resulted in the active noise reduction effect when applied on the field.

New SFRA measurement interpretation methodology for the diagnosis of power transformers

The analysis of the frequency response by the Sweep Frequency Response Analysis method (SFRA) is a diagnosis technique that can detect displacements, deformations and other mechanical and electrical failures in power and distribution transformers. One of the main disadvantages of the method is the lack of an international agreement regarding the methodology for measurement analysis, which is usually done by experts in the field, who normally make the diagnosis with the aid of statistical parameters such as correlation coefficient and standard deviation, or parameters derived from modeling the frequency response as a complex transfer function, represented by poles and zeros, or poles and residues. This paper presents a new methodology for SFRA measurement analysis, which makes use of the properties of various types of parameters and expert knowledge through the application of fuzzy causal diagnosis.

A Novel AE Based Algorithm for PD Localization in Power Transformers

In this paper, a novel algorithm for PD localization in power transformers based on wavelet de-noising technique and energy criterion is proposed. Partial discharge is one of the main failures in power transformers. The localization of which could be very useful for maintenance systems. Acoustic signals due to a PD event are transient, irregular and non-repetitive. So wavelet transform is an efficient tool for this signal processing problem that gives a time-frequency demonstration. First, different wavelet based de-noising methods are analyzed. Then, a reasonable structure for threshold value determining and applying manner on signals is presented. Evaluated errors are good evidences for choices. Next, applying the elimination low energy frequency bands is discussed and developed as a de-noising method. Time differences between signals are used for PD localization. Different ways in time arrival detection are introduced and a novel approach in energy criterion method is presented. At the end, the quality of algorithm is verified through the different assays in lab.

Review of Power Transformer Mechanical Condition Assessment Techniques

Today’s trend of global electricity market has created a competitive environment in power industry. To reduce operational cost, optimize usage of critical equipments, to improve reliability and customer service, fast and enhanced diagnostic techniques are being developed and utilized in power industry. In recent years, transformer fault diagnosis has become an interesting research area.This paper presents the literature review done in the area of power transformer designs, failure causes and effects and on existing and new diagnostic techniques to generate the baseline for the development of mechanical condition assessment system. The survey has included the 90 technical reports and papers from CIGRE, IEEE transactions and conferences along with standards and books based on power transformer conditioning and monitoring. In this paper an attempt has been made to analyze, generate real data based approach for development of enhanced and automated diagnostic system for mechanical faults detection.

Analysis and Simulation of Fault Characteristics of Power Switch Failures in Distribution Electronic Power Transformers

This paper presents research on the voltage and current distortion in the input stage, isolation stage and output stage of Distribution Electronic Power transformer (D-EPT) after the open-circuit and short-circuit faults of its power switches. In this paper, the operational principles and the control methods for input stage, isolation stage and output stage of D-EPT, which work as a cascaded H-bridge rectifier, DC-DC converter and inverter, respectively, are introduced. Based on conclusions derived from the performance analysis of D-EPT after the faults, this paper comes up with the effects from its topology design and control scheme on the current and voltage distortion. According to the EPT fault characteristics, since the waveforms of relevant components heavily depend on the location of the faulty switch, it is very easy to locate the exact position of the faulty switch. Finally, the fault characteristics peculiar to D-EPT are analyzed, and further discussed with simulation on the Saber platform, as well as a fault location diagnosis algorithm.

Feature extraction and stage classification of cavity discharge development in transformer oil-paper insulation

SUMMARY Characteristic study of cavity discharge is important in detecting and diagnosing internal failure of power transformers. Based on the kernel principal component analysis method, eight new feature parameters are extracted from 29 statistical parameters, which are commonly used in the characteristic study of partial discharges. Redundancy and computational complexity for applications are greatly reduced. Using cluster analysis method, new parameters are proved to be effective in the application of stage classification in the cavity discharge development. Four stages are classified: incipient discharge, unstable discharge, consistent discharge, and disruptive discharge. Copyright © 2013 John Wiley & Sons, Ltd.

Analysis of internal winding short circuit faults in power transformers using transient finite element method coupling with external circuit equations

SUMMARYInterturn short circuit faults are the leading cause of power transformer failures. If not quickly detected, these faults usually develop into more serious faults, which would result in irreversible damage to the transformer, unexpected outages, and consequential costs. The aim of this research is to obtain a better understanding of the physical behavior of the power transformers in the presence of interturn faults as well as to discover the best indicators for detection of interturn short circuit faults. To this end, a time stepping finite element model of power transformer has been developed to analyze the transient behavior of a real power transformer when the transformer is working under winding short circuit fault conditions. The transient model, coupled with external circuit equations, allows us to simulate the dynamic behavior of the transformer with the real power supply and external loads connections. The FEM computations show the ability of the electromagnetic flux inside the transformer, transformer terminal currents, and circulating current in the shorted turns as useful monitoring parameters for transformer fault detection. Copyright © 2013 John Wiley & Sons, Ltd.

Research on Method of State Evaluation and Fault Analysis of Dry-Type Power Transformer Based on Self-Organizing Neural Network

Dry-type power transformer was used widely because of its advantages. But unplanned outage effect to construct a strong intelligent power grid because of various stress. Dry-type power transformer’s fault repair time is long and impossible to repair. So it is very important to realize state maintains of dry-type transformer through state monitor and diagnosis. Based on current diagnostic methods, this paper proposed using self-organizing neural network to realize dry-type power transformer the key point temperature parameters of grading evaluation and then to realize the real-time state evaluation and analysis of failure causes. Study results to prolong the dry-type power transformer life and its design production provide theoretical guidance, in order to reduce and avoid dry-type power transformer failure.

A Coupled Thermal-Electromagnetic FEM Model to Characterize the Thermal Behavior of Power Transformers Damaged By Short Circuit Faults

This research work is an initiative to characterize the thermal behavior of power transformers in presence of winding short circuit faults as one of the most important causes of failures in power transformers. The paper contributes for this matter by accurately estimating the excessive power losses and temperature rise due to winding short circuit faults, through a circuit-magnetic-thermal FEM coupling method. The magnetic-circuit coupled FEM model of the faulty transformer allows the computation of the circulating current in the shorted turns, flux distribution in the transformer and the power dissipated by Joule effect in the shorted region of the winding. Once the local losses, required as heat sources for the thermal analysis, are calculated, a coupled electromagnetic-thermal FEM model of the transformer is developed to solve the transient heat flow equations and obtain the transient thermal characterization of the transformer damaged by short circuit faults. At each time step in the coupled transient FEM thermal model, a local linking of thermal and then steady state AC magnetic computations is carried out, as a means of better representation of the transformer dynamic thermal behavior. 

Outage data analysis of utility power transformers based on outage reports during 2002–2009

Statistical analysis of failures and forced outages of power transformers constitute an important basis for asset management of these transformers. Results of the statistical analysis can be used, for example, to enhance utility reliability, influence transformer design and technology, and improve maintenance and condition monitoring practices. In addition, various methods for transformer reliability evaluation require that the expected values of component outage rates, outage durations, and repair durations be known. In this paper, outage data are obtained from the Egyptian Electricity Transmission Company (EETC). This work presents outage data analysis over eight years, from 2002 to 2009, for 1922 (average number) transformers in voltage populations ranging from 33kV to 500kV and MVA rating from 5MVA to 500MVA. Forced outages due to correct and false action of transformer’s protection systems are carefully considered. Outage data analysis is conducted according to two basic phases. In the first phase, failure and repair analysis of transformers is performed while in the second phase impact of transformer outages on customers is assessed. Percentage average number of failures (%AANF) and annual average repair time (AART) per transformer are used to represent the failure and repair data of power transformers. Two indicators are used to represent the impact of transformer outages on customer interruptions. These indicators are the annual average interrupted MW (AAIMW) and annual average customer-interruption duration (AACID). A summary of the main outcomes of the work presented in this paper is provided in the conclusions’ section; however, it is worthy to be mentioned here that the fire-fighting systems are responsible for the highest number of false trips in all voltage subpopulations except the 220kV subpopulation where the dominant cause of false trips is the busbar protection. Therefore, it is recommended to improve the maintenance and design of this protection equipment to reduce the failure rate of power transformers.

Statistical Health Grade System against Mechanical failures of Power Transformers

A health grade system against mechanical faults of power transformers has been little investigated compared to those for chemical and electrical faults. This paper thus presents a statistical health grade system against mechanical faults in power transformers used in nuclear power plant sites where the mechanical joints and/or parts are the ones used for constraining transformer cores. Two health metrics—root mean square (RMS) and root mean square deviation (RMSD) of spectral responses at harmonic frequencies—are first defined using vibration signals acquired via in-site sensors on fifty-four power transformers in several nuclear power plants in sixteen months. We then investigate a novel multivariate statistical model, namely copula, to statistically model the populated data of the health metrics. The preliminary study shows that the proposed health metrics and statistical health grade system are feasible to monitor and predict the health condition of the mechanical faults in the power transformers.

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.

Forecasting dissolved gases content in power transformer oil based on weakening buffer operator and least square support vector machine–Markov

The early detection of potential power transformer failures can ensure the safe operation of transformers. So it is practical to develop the early-fault-forecasting technology for transformers. Dissolved gas analysis (DGA) in power transformer is a significant basis for transformer insulation fault diagnosis, which provides full evidence for general internal transformer hidden dangers. But because of the stochastic growth and the small quantity of time-sequence data, forecasting the accurate dissolved gases content in power transformer oil is a complicated problem until now. Least square support vector machine (LSSVM) has been successfully employed to solve regression problem of nonlinearity and small sample. Aiming at improving the primitive shock and disturbance of time-sequence data, this paper firstly introduces the weakening buffer operator to attenuate its randomness. Then, in order to decrease the forecasting error and maximize the total forecasting precision, the Markov chain, which can well reflect the randomness produced by the system involved with many complex factors, is presented to modify the values forecasted by LSSVM. The experimental results indicate that the proposed model can achieve greater forecasting accuracy than GRNN and LSSVM model under the circumstances of small sample.

A Study of K- Factor Power Transformer Characteristics by Modeling Simulation

Harmonic currents generated by nonlinear loads can cause overheating and premature failure in power transformers. K-factor transformers are specially designed to accommodate harmonic currents and offer protection against overheating caused by harmonics. They minimize harmonic current loss and have an additional thermal capacity of known limits. According to IEEE C57-110, the winding eddy current losses are considered proportional to the harmonic current squared times its harmonic number. K-factor is only an indicative value and the authors' main objective in this paper is to study the effect of harmonics on oil filled transformer and to simulate harmonic behavior using Matlab Simulink. A case study is simulated in order to investigate K-factor values with pumping loads, with and without the use of harmonic filters. Results are compared with measured values.