Condition Monitoring Of Power Transformers Research Articles

Internet of Things (IoT) System for Diagnosing Power Transformer Faults using the IEC 60076-1 Standard: Review

The evaluation of Internet of Things (IoT), which are contracted to research the adoption or acceptance of IoT technology, is deemed to be a subject of vital importance. Studying the adoption or admissibility of IoT technology is not a brand-new study area, and numerous scholars have undertaken the task. The current and upcoming generations of Internet are being shaped by the Internet of Things (IoT). The main aim of this survey, contrasted with previous surveys, is to systematically evaluate the IoT technology research in condition monitoring of power transformers to proffer a thorough cursor that can facilitate scholars to accomplish additional research in IoT acceptance. Almost all appliances may now be remotely monitored and connected to the internet thanks to the Internet of Things. Power transformers play a crucial role in electrical distribution networks, and their reliable operation is paramount. The existing diagnostic tests outlined in the IEC 60076-1 standard are essential for ensuring transformer health but suffer from manual procedures, leaving them susceptible to human error. This traditional method not only introduces the potential for inaccuracies due to human involvement but also incurs significant operational costs and delays. The need to physically dispatch technicians to transformer sites for routine diagnostics hampers timely fault detection and preventive maintenance. Therefore, there exists a pressing need to modernize and enhance the diagnostic procedures by integrating emerging technologies like the Internet of Things (IoT). This paper will review the most recent techniques for transformer health tracking system using IoT and highlight the contemporary approaches. A total of 50 primary research literature that was published between 2018 and 2024 were surveyed in this review paper.

Evaluation of Optical Fiber Macrobendings in Temperature Sensor Dedicated for Power Transformer Monitoring

Optical fiber sensing techniques are recognized as very promising in diagnostic and condition monitoring of power transformers. According to the IEC standard 60076-2, the winding hot-spot temperature can be designated with the optical fiber sensor. In this paper, the investigation of the influence of macrobending of the optical fiber temperature sensor on the sensing performance is presented. The obtained results prove that the optical fiber sensor wrapped six times around the 14 mm cylinder still provides temperature sensing abilities.

Research on Online Gas Volume Monitoring Technology of Intelligent Gas Relay

The gas relay is the main non-electrical protection device of the transformer, which plays an important role. Traditional detection methods can not reflect the gas relay level, and there will be gas relay false alarms in operation and maintenance. Firstly, the working principle of the gas relay was analyzed, and a non-conductive capacitance sensor was proposed to realize the real-time monitoring of gas volume in the gas relay. Then, a new online monitoring scheme for intelligent gas relays was constructed through the selection analysis and optimization design of the gas protection module and liquid-level measurement module. Based on the changing state of gas volume in the gas relay, a more accurate and timely fault diagnosis of the power transformer was realized, and the prototype processing was completed. Finally, the feasibility of the volumetric testing method was verified by comparing the two conditions of different liquid-level heights and different ambient temperatures. The proposed method is of great significance for discovering the latent faults of transformers and realizing the condition monitoring of oil-immersed power transformers, which ensures the safety of power grid equipment and personnel.

An Approach Based on Transfer Learning to Lifetime Degradation Rate Prediction of the Dry-Type Transformer

Lifetime prediction of the power transformer plays an important role in maintaining the stable operation of power equipment. However, due to the complexity of insulation structure degenerative process, the existing methods featuring high cost and low precision are not effective enough in transformer life time prediction. Meanwhile, how to effectively and promptly respond to a new prediction scenario of insufficient and limited data is a common challenge for all the data-driven prediction methods. To address these concerns, a prediction approach of a back adoptive adjustment transfer learning scheme (BAATL) is proposed for lifetime degradation prediction of the dry-type transformer. The power transformer condition monitoring data of Supervisory Control and Data Acquisition system is conducted as the data driven. A deep neural network, a transfer learning module and a back adjustment module are constructed to realize feature extraction, domain adaptation and prediction network optimization. The proposed scheme is able to improve prediction accuracy and resolves the problems and drawbacks of traditional prediction methods, and presents its superior portability and application potential in the case of data shortage and scenario change. With authentic datasets, simulation tests performed on the condition monitoring data of dry-type transformers prove the effectiveness of the proposed scheme.

Dissolved Gas Analysis and Application of Artificial Intelligence Technique for Fault Diagnosis in Power Transformers: A South African Case Study

In South Africa, the growing power demand, challenges of having idle infrastructure, and power delivery issues have become crucial problems. Reliability enhancement necessitates a life-cycle performance analysis of the electrical power transformers. To attain reliable operation and continuous electric power supply, methodical condition monitoring of the electrical power transformer is compulsory. Abrupt breakdown of the power transformer instigates grievous economic detriment in the context of the cost of the transformer and disturbance in the electrical energy supply. On the condition that the state of the transformer is appraised in advance, it can be superseded to reduced loading conditions as an alternative to unexpected failure. Dissolved gas analysis (DGA) nowadays has become a customary method for diagnosing transformer faults. DGA provides the concentration level of various gases dissolved, and consequently, the nature of faults can be predicted subject to the concentration level of the gases. The prediction of fault class from DGA output has so far proven to be not holistically reliable when using conventional methods on account of the volatility of the DGA data in line with the rating and working conditions of the transformer. Several faults are unpredictable using the IEC gas ratio (IECGR) method, and an artificial neural network (ANN) has the hindrance of overfitting. Nonetheless, considering that transformer fault prediction is a classification problem, in this work, a unique classification algorithm is proposed. This applies a binary classification support vector machine (BCSVM). The classification precision is not reliant on the number of features of the input gases dataset. The results indicate that the proposed BCSVM furnishes improved results concerning IECGR and ANN methods traceable to its enhanced generalization capability and constructional risk-abatement principle.

Data Driven Transformer Thermal Model for Condition Monitoring

Condition monitoring of power transformers, which are key components of electrical power systems, is essential to identify incipient faults and avoid catastrophic failures. In this paper machine learning algorithms, i.e., nonlinear autoregressive neural networks and support vector machines, are proposed to model the transformer thermal behavior for the purpose of monitoring. The thermal models are developed based on the historical measurements from nine transformers comprised of two 180-MVA units, four 240-MVA units and three 1000-MVA units. The data consist of load profile, tap position, winding indicator temperature (WTI) measurement, ambient temperature, wind speed and solar radiation. The results are validated against field measurements, and it is clearly demonstrated that the alternative algorithms surpass the IEEE Annex G thermal model. An incipient thermal fault identification algorithm is then proposed and successfully used to identify an issue using measurements taken in the field. This algorithm could be used to alert the operator and plan intervention accordingly.

Condition Monitoring of Power Transformer By Using Fuzzy Logic

Abstract: This work did focuses on problem identification due to faults in power transformers during operation by using dissolved gas analysis such as key gas, IEC ratio, Duval triangle techniques, and fuzzy logic approaches. Then, the condition of the power transformer is evaluated in terms of the percentage of failure index and internal fault determination. Fuzzy logic with the key gas approach was used to calculate the failure index and identify problems inside the power transformer.

Comparative study of electrical test methods on detecting transformer faults

Condition monitoring of distribution and power transformers is of utmost importance to utilities due to cost effectiveness concerns. The common faults that occur in transformers are short circuits and winding deformation. To date, there are many established test methods used to detect these faults. In this study, three test methods (insulation resistance (IR), transformer turn ratio (TTR), and frequency response analysis (FRA)) were compared to assess their effectiveness in detecting short circuit and winding deformation in a three-phase transformer. Based on the results, the three test methods were found to be capable of detecting short circuits. However, only TTR and FRA can detect winding deformation, and FRA can further indicate which phase is faulty. Therefore, it is concluded that FRA is more effective in detecting short circuit and winding deformation of a three-phase transformer.

Transformer Oil Diagnosis Based on a Capacitive Sensor Frequency Response Analysis

Condition monitoring of large power transformers is of paramount importance in power systems. One of the most common indicators of transformer aging is moisture content in oil. In this article, a capacitive sensor able to online measuring moisture content is presented. The output of this sensor is recorded using a Frequency Response Analysis (FRA) device. Comparing the FRA results of a degraded oil with a reference FRA test, moisture content can be determined. A prototype sensor has been designed, built and tested. The performance of this sensor was simulated and laboratory tested with different moisture content oil samples. The results confirm that the proposed sensor can determine moisture content in oil. Therefore, it allows estimating oil degradation.

Application of Operating Deflection Shapes to the Vibration-Based Mechanical Condition Monitoring of Power Transformer Windings

Operating deflection shapes (ODSs) are used for visualizing the vibration patterns of a structure. In this study, ODSs are applied to diagnose the mechanical condition of power transformer windings. First, a synchronous calibration measuring system is developed to simultaneously acquire tank vibration signals and excitation voltage signals. To perform the synchronous calibration, the phase of the excitation voltage signals is set as the reference. Next, the no-load test, load test, and variable load test are conducted on a 50-kVA transformer. The histogram of oriented gradient (HOG) and Pearson correlation coefficient (CC) are proposed to describe the differences between the different ODSs. Experimental results show that the ODSs are insensitive to the operating voltage and current, but sensitive to the clamping pressure of the windings. Field results further verify the significant change in the ODS after winding deformation. Finally, the partial ODS, which is suitable for practical transformers, is proposed to locate winding mechanical defects in power transformers. The proposed method can improve the efficiency and accuracy of the vibration-based mechanical condition monitoring of power transformers.

Novel particle-based model of negative corona in oxygen for investigation on emission of electromagnetic waves

In recent years, the ultrahigh frequency (UHF) partial discharge (PD) measurement technique has gained more attention as an effective approach towards condition monitoring of power transformers. In this contribution, a novel model for the simulation of negative corona, based on particles' movement, is presented and implemented in COMSOL Multiphysics. Unlike existing models of PD, this model aims at an accurate representation of the phenomenon from an electromagnetic (EM) viewpoint. Following a discussion on the theoretical fundamentals of PD, the model containing the mechanisms and processes that occur during the PD phenomenon and lead to propagation of EM waves is discussed. Subsequently, simulation results are analyzed and previously ambiguous properties of negative corona are interpreted and some efforts are done to validate the theory using experimental measurement.

Review of Fiber Optic Diagnostic Techniques for Power Transformers

Diagnostic and condition monitoring of power transformers are key actions to guarantee their safe operation. The subsequent benefits include reduced service interruptions and economic losses associated with their unavailability. Conventional test methods developed for the condition assessment of power transformers have certain limitations. To overcome such problems, fiber optic-based sensors for monitoring the condition of transformers have been developed. Flawlessly built-up fiber optic-based sensors provide online and offline assessment of various parameters like temperature, moisture, partial discharges, gas analyses, vibration, winding deformation, and oil levels, which are based on different sensing principles. In this paper a variety and assessment of different fiber optic-based diagnostic techniques for monitoring power transformers are discussed. It includes significant tutorial elements as well as some analyses.

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 administered by supply a coffee voltage signal of varying frequencies to the transformer windings and measuring both the input and output signals. These two signals give the specified response of the ratio is named the transfer function of the transformer from which both the magnitude and phase are often obtained. Frequency response is change as measured by SFRA techniques may indicate a phase transition inside the transformer, then causes of fault identified and investigation is required for root cause analysis.

Simultaneous radial deformation and partial discharge detection of high‐voltage winding of power transformer

Condition monitoring of power transformers has attracted a lot of attention due to their important roles in determining power system reliability. Radial deformation of power transformer winding should be monitored online to prevent more damages to winding and possible transformer outage. The ultra-high-frequency (UHF) stepped-frequency synthetic-aperture radar imaging method has recently been proposed as an online method for high-voltage winding radial deformation detection. On the other hand, partial discharge (PD) as another defect of the transformer winding propagates UHF signals in the transformer environment, which should be detected online. In this study, the practical application of a designed monitoring system is investigated for simultaneous online detection of both PD and radial deformation defects in a real three-phase power transformer. In addition, radial deformation is localised through a monitoring system designed based on the proposed method. Using this system, only one set of antennas is needed to detect both defects which lead to reducing the number of installed antennas and economic application of the monitoring system.

K-ORTALAMALAR KÜMELEME YÖNTEMİ İLE GÜÇ TRANSFORMATÖRLERİNİN BAKIM STRATEJİLERİNİN BELİRLENMESİ

Teknolojik gelişmelere bağlı olarak elektrik enerjisine olan ihtiyacın hızla artması sebebiyle, güç transformatörlerinde oluşabilecek arızaların sosyal hayata ve ekonomik gelişime etkilerinin kritik bir öneme sahip olduğu açıktır. Çeşitli sebeplere bağlı olarak, güç transformatörlerinde oluşabilecek arızalar, sensörler ve bakım-onarım yöntemleri sayesinde, meydana gelmeden veya başlangıç aşamasında tespit edilebilmektedir. Bu makalede, meydana gelmiş arıza istatistikleri incelenerek, transformatörlerin yaşları, yüklenme oranları ve güçleri dikkate alınarak uygun bakım-onarım yönteminin belirlenmesine yönelik bir çalışma yapılmıştır. Belirli bir transformatör grubuna uygun bakım-onarım yönteminin belirlenmesine yönelik bir yaklaşım olması açısından K-ortalamalar algoritması tercih edilmiştir. Kümeleme sonuçları yorumlanmış ve güç transformatörlerine uygun bakım-onarım yöntemleri belirlenmiştir.

An Alternative Approach for Oil-immersed High Voltage Power Transformer Dissolved Gas Analysis Diagnostic Techniques

This article presents alternative analyzing method of extracted dissolved gases related to insulating oil of power transformers. Analysis of soluble and free gas is one of the most commonly used troubleshooting methods for detecting and evaluating equipment damage. Although the analysis of oil-soluble gases is often complex, it should be expertly processed during maintenance operation. The destruction of the transformer oil will produce some hydrocarbon type gases. The development of this index is based on two examples of traditional evaluation algorithms along with fuzzy logic inference engine. Through simulation process, the results of the initial fractures in the transformer are obtained in two ways by the "Duval Triangle method” and "Rogers’s ratios". In continue, three digit codes containing the fault information are created based on the fuzzy logic inference engine to achieve better results and eliminate ambiguous zones in commonly used methods, especially in the “Duval Triangle method”. The proposed method is applied to 80 real transformers to diagnose the fault by analyzing the dissolved oil based on fuzzy logic. The results illustrate the proficiency of this alternative proposed algorithm. Finally, with utilization of a neural network the alternative practical inference function is derived to make the algorithm more usable in the online condition monitoring of power transformers.

A review on fault detection and condition monitoring of power transformer

A review on fault detection and condition monitoring of power transformer

A General Methodology for Evaluation and Classification of Oil-Immersed Power Transformers: Application to Electrical and Physicochemical Parameters

Condition monitoring of power transformers is of vital importance to prevent electricity supply stoppages and reduce power plant maintenance costs. To that end, the use of techniques to evaluate and classify the condition of these devices is highly recommended in order to obtain good quality information for their proper maintenance planning. This article presents and details a general methodology for the creation of methods to evaluate and classify these devices, by means of computational modeling and optimization. The results indicate a higher than 93% accuracy rate compared to that of numerical evaluations and symbolic classifications expected by experts, thus demonstrating the applicability of the proposed methodology, which is found to be superior in comparisons against Computational Intelligence and Statistical Learning methods.

Improved power transformer condition monitoring under uncertainty through soft computing and probabilistic health index

Condition monitoring of power transformers is crucial for the reliable and cost-effective operation of the power grid. The health index (HI) formulation is a pragmatic approach to combine multiple information sources and generate a consistent health state indicator for asset management planning. Generally, existing transformer HI methods are based on expert knowledge or data-driven models of specific transformer subsystems. However, the effect of uncertainty is not considered when integrating expert knowledge and data-driven models for the system-level HI estimation. With the increased dynamic and non-deterministic engineering problems, the sources of uncertainty are increasing across power and energy applications, e.g. electric vehicles with new dynamic loads or nuclear power plants with de-energized periods, and transformer health assessment under uncertainty is becoming critical for accurate condition monitoring. In this context, this paper presents a novel soft computing driven probabilistic HI framework for transformer health monitoring. The approach encapsulates data analytics and expert knowledge along with different sources of uncertainty and infers a transformer HI value with confidence intervals for decision-making under uncertainty. Using real data from a nuclear power plant, the proposed framework is compared with traditional HI implementations and results confirm the validity of the approach for transformer health assessment.

Study on Winding Looseness of Power Transformer Based on Vibration

The vibration of transformer is closely related to windings, so researching on vibration signal of power transformer has potential to realize online detection of winding looseness. In this paper, a series of tests have been made to obtain vibration signal by setting winding looseness on a 10/0.4kV three-phase transformer. A linear fitting equation has been obtained by fitting the amplitude of the 100Hz component of the vibration signal under different short-circuit currents. The results show that the slope of the fitting equation will increase when the winding becomes loose. The research results show the potential of this method in condition monitoring of power transformer.