Electric Power Transformers Research Articles

Transformer oil temperature sensing utilizing bundle plastic optical fiber sensor

A bundle plastic optical fiber (POF) that works based on an intensity modulation technique is experimentally demonstrated to sense the temperature of transformer oil. The sensor was developed using a bundle POF that is located perpendicular to an aluminum reflective film with an airgap cavity between these two elements. The simplicity of the architecture allows the development of an economical optical sensor system. To avoid interference effects by other substances in the oil, the sensor head is encapsulated with a metal protecting tube. The temperature measurement was realized in this study by monitoring the output light intensity in the visible light spectrum. For linearity range from 40 °C to 75 °C, the tested sensor exhibits a sensitivity of 0.0064 °C−1, a linearity coefficient of 0.95 and a resolution of 1.56 °C. These results demonstrate the suitability of the developed sensor for temperature oil monitoring in an electrical power transformer system.

Patent Research of Systems for Monitoring the Technical Condition of Electric Motors and Transformers

The article presents the main causes of malfunctions of electric motors and power transformers at industrial enterprises, the main technical parameters of electrical equipment, providing information on the technical condition. Comparison of systems for monitoring the technical condition of electrical equipment from different manufacturers is made.

Enhancing heat transfer in power transformer radiators via thermo-fluid dynamic analysis with periodic thermal boundary conditions

The efficient dissipation of energy losses in electrical power transformers is crucial to prevent overheating of their windings and ensure proper functioning. In this study, we investigate the thermal design of a 30 [MVA], 132/34.5/13.8 [kV] power transformer radiator operating in Oil Natural-Air Natural mode by using Computational Fluid Dynamics (CFD) simulations. We introduce artificial body forces in the momentum Navier-Stokes equation to analyze the thermo-fluid dynamic performance of secondary transverse flows. Additionally, we propose a numerical scheme to solve the thermal equation with periodic boundary conditions using a reduced length of the oil channel, allowing for the decoupling of the thermal and fluid dynamic problems. To enhance the heat transfer, we propose the use of turbulators and wall indentation arrangements. Through our simulations, we analyze the effects of these enhancements on the cooling capacity of the transformer radiator. Our results demonstrate that these modifications increase the heat transfer coefficient and improve the cooling capacity of the radiator. Furthermore, we propose manufacturing considerations for the turbulators and wall indentation arrangements to ensure their practicality. Our findings provide valuable insights into the thermal design of power transformer radiators and demonstrate the effectiveness of turbulators and wall indentation arrangements in enhancing their cooling capacity.

Machine Learning Algorithms Fusion Based on DGA Data for Improving Fault Diagnosis of Electrical Power Transformer

Abstract Dissolved Gas Analysis (DGA) continues to be widely recognized as a valuable method in recent times for the early identification of issues in oil-filled power transformers. It has gained extensive adoption as a primary approach for the early discovery of these issues, relying on the analysis of dissolved gases. This contributes to enhancing the dependability of electrical systems. This paper proposes an efficient fusion method based on DGA data using the two best Machine Learning algorithms, the neural network (MLP), the naïve Bayes (NB) throughdata input vector ppm, a percentage input vector, and an Logarithmic input vector. The fusion method predictively combined the two classifiers and obtained a statistical evaluation: accuracy, recall, precision, and F-measure higher than both classifiers separately. The proposed fusion method was evaluated for performance using a test database and compared with conventional and smart methods. Results showed that the proposed model outperformed both traditional and intelligent methods in terms of diagnostic accuracy when using percentage and logarithmic input vectors. The Prediction Based Fusion (PBF) vector Percentages achieved an accuracy rate of 97.22%, while PBF vector Logarithmic achieved an accuracy rate of 95.83%. These rates were higher than those achieved by traditional methods, such as the Modified RRM/CEGB method 91.67% and Modified RRM/IEC method 90.28%. Additionally, the proposed model surpassed the accuracy rates of intelligent methods, such as CSUS ANN 88.89% and Conditional Probability 93.06%.

Connected system for monitoring electrical power transformers using thermal imaging

The stable supply of electricity is essential for the industrial activity and economic development as well as for human welfare. For this reason, electrical system devices are equipped with monitoring systems that facilitate their management and ensure an uninterrupted operation. This is the case of electrical power transformers, which usually have monitoring systems that allow early detection of anomalies in order to prevent potential malfunctions. These monitoring systems typically make use of sensors that are in physical contact with the transformer devices and can therefore be affected by transformer problems. In this work we demonstrate a monitoring system for electrical power transformers based on temperature measurements obtained by means of thermal cameras. Properly positioned, the cameras provide thermal data of the transformer, the incoming and outgoing lines and their surroundings. Subsequently, by appropriate image processing, it is possible to obtain temperature series to monitor the transformer operation. In addition, the system stores and processes thermal data in external equipment (placed in locations other than the transformers) and is equipped with a communications module that allows secure data transmission independent of the power grid. This aspect, along with the fact that there is no need to have physical contact with the transformer, make this approach safer and more reliable than standard approaches based on sensors. The proposed system has been evaluated in 14 stations belonging to the Spanish power grid, obtaining accurate and reliable temperature time series.

Condition Assessment of Natural Ester-Mineral Oil Mixture Due to Transformer Retrofilling via Sensing Dielectric Properties.

Mineral oil (MO) is the most popular insulating liquid that is used as an insulating and cooling medium in electrical power transformers. Indeed, for green energy and environmental protection requirements, many researchers introduced other oil types to study the various characteristics of alternative insulating oils using advanced diagnostic tools. In this regard, natural ester oil (NEO) can be considered an attractive substitute for MO. Although NEO has a high viscosity and high dielectric loss, it presents fire safety and environmental advantages over mineral oil. Therefore, the retrofilling of aged MO with fresh NEO is highly recommended for power transformers from an environmental viewpoint. In this study, two accelerated aging processes were applied to MO for 6 and 12 days to simulate MO in service for 6 and 12 years. Moreover, these aged oils were mixed with 80% and 90% fresh NEO. The dielectric strength, relative permittivity, and dissipation factor were sensed using a LCR meter and oil tester devices for all prepared samples to support the condition assessment performance of the oil mixtures. In addition, the electric field distribution was analyzed for a power transformer using the oil mixtures. Furthermore, the dynamic viscosity was measured for all insulating oil samples at different temperatures. From the obtained results, the sample obtained by mixing 90% natural ester oil with 10% mineral oil aged for 6 days is considered superior and achieves an improvement in dielectric strength and relative permittivity by approximately 43% and 48%, respectively, compared to fresh mineral oil. However, the dissipation factor was increased by approximately 20% but was at an acceptable limit. On the other hand, for the same oil sample, due to the higher molecular weight of the NEO, the viscosities of all mixtures were at a higher level than the mineral oil.

Convective heat transfer behavior and AC dielectric breakdown voltage of electric power transformer oil with magnetic colloidal nano-fluid: An experimental study

This research reports on an experimental investigation into the alternating insulation breakdown voltage (AC) and convective heat transfer behavior of nano-oil for application in electrical transformers. The base fluid of the examined nano-oil is nitro libra type transformer oil, one of the common mineral oils used in transformers. It contains iron oxide magnetic colloidal nanoparticles. Upon fabrication of the experimental apparatus in the lab, an experimental study on convection heat transfer is performed under laminar flow conditions and with a continuous heat flux applied to the wall. BA100 breakdown voltage measurement instrument based on the IEC 60156 standard is also used for alternating breakdown voltage testing. The current findings from the experimental investigation of convective heat transfer are compared with and verified by the experimental results available in the literature. The experimental results demonstrate that the convective heat transfer coefficient of the iron oxide magnetic nanoparticle-prepared nano-oil is on average 4.51% higher than that of base oil. These particles have an average size of 23 nm and a volume concentration of 0.1%. Additionally, compared to base oil, nano-oil with a volume concentration of 0.1% has a 23.8% higher dielectric breakdown voltage.

A Brief Review of the Impregnation Process with Dielectric Fluids of Cellulosic Materials Used in Electric Power Transformers

In the manufacturing of power transformers, the impregnation of the solid electric insulation systems (cellulosic materials) with a dielectric liquid is a key issue for increasing the breakdown voltage of the insulation, and this prevents the apparition of partial discharges that deteriorate the insulation system. After introducing the problem, this article presents the theory of impregnation and later carries out a bibliographical review. Traditionally, mineral oils have been used as the dielectric liquid in electrical transformers, but for environmental (low biodegradability) and safety (low ignition temperature) reasons, since the mid-1980s, their substitution with other ester-type fluids has been studied. However, these liquids have some drawbacks, including their higher viscosity (especially at low temperatures). This property, among other aspects, makes the impregnation of cellulosic materials, which is part of the transformer manufacturing process, difficult, and therefore this tends to lengthen the manufacturing times of these machines.

Characterization of electrical 1-phase transformer parameters with guaranteed hotspot temperature and aging using an improved dwarf mongoose optimizer

Parameters identification of Electric Power Transformer (EPT) models is significant for the steady and consistent operation of the power systems. The nonlinear and multimodal natures of EPT models make it challenging to optimally estimate the EPT’s parameters. Therefore, this work presents an improved Dwarf Mongoose Optimization Algorithm (IDMOA) to identify unknown parameters of the EPT model (1-phase transformer) and to appraise transformer aging trend under hottest temperatures. The IDMOA employs a population of solutions to get as much information as possible within the search space through generating different solution’ vectors. Furthermore, the Nelder–Mead Simplex method is incorporated to efficiently promote the neighborhood searching with the aim to find a high-quality solution during the iterative process. At initial stage, power transformer electrical equivalent extraction parameters are expressed in terms of the fitness function and its corresponding operating inequality restrictions. In this sense, the sum of absolute errors (SAEs) among numerous factors from nameplate data of transformers is to be minimized. The proposed IDMOA is demonstrated on two transformer ratings as 4 kVA and 15 kVA, respectively. Moreover, the outcomes of the IDMOA are compared with other recent challenging optimization methods. It can be realized that the lowest minimum values of SAEs compared to the others which are 3.3512e−2 and 1.1200e−5 for 15 kVA and 4 kVA cases, respectively. For more assessment for the proposed optimizer, the extracted parameters are utilized to evaluate the transformer aging considering the transformer hottest temperature compared with effect of the actual parameters following the IEEE Std C57.91 procedures. It is proved that the results are guaranteed, and the transformer per unit nominal life is 1.00 at less than 110 °C as per the later-mentioned standard.

Testing of Palm Oil-Based Electric Power Transformer Insulation Oil as a Renewable Energy Source

Reducing petroleum resources and environmental problems, alternative insulating oils with biodegradable characteristics have become an alternative. Esterification of palm-based oil is expected to provide better and optimum electrical properties. The esterification of palm oil will be carried out as a test sample and observed for its electrical, physical and chemical properties such as PD characteristics, breakdown voltage, dissipation factor, viscosity, flash point, etc. The effect of thermal aging on oil samples will also be carried out to simulate real situations in power transformer applications. Then after obtaining the best formulation from the sample it will be used as insulating oil in electric power transformers (100kVA). The purpose of this research is to apply esterified palm oil as an electric power transformer oil. The method used in this research includes field research. Field research includes taking oil samples, measuring the breakdown voltage of transformer oil, and the color of transformer oil. The results obtained are insulating oil for electric power transformers based on palm oil. Palm oil has the advantage of being an environmentally friendly, biodegradable and renewable oil compared to the transformer oil used today which is based on petroleum. With the realization of transformer oil based on palm oil, it is hoped that it can provide great economic opportunities for Indonesia as one of the largest palm oil producers in the world and can also provide added value and increase the competitiveness of the palm oil industry in the future.

Electrical Power Transformer Damage on the Power Grid in Benin: Causes, Consequences and Solution Approaches

The transformer exploits the phenomenon of electromagnetic induction of wound conductors. One of its important properties is that it can act as an impedance matcher. Faults on the electrical network often affect the grounding resistances of power transformers and eventually damage them. Benin's electricity network, like those of other countries in the world, is frequently tested by electrical transformer damage. Through this work, from the static data obtained from the SBEE (Beninese Electric Energy Company), the state of the transformers installed on the electricity network, from 2016 to 2022, is realized. The analysis of their different operating conditions and their installation and protection methods has made it possible to identify the main causes of their damage. Their consequences on the transport and distribution of electrical energy in Benin are highlighted. It appears from this work that most of the faults are due to overvoltage due to discharges, overloads and short circuits. The poles most affected are those on poles (H61) followed by those on frames. Finally, a series of recommendations are proposed to minimize the number of defective transformers.

Effective approaches to remote asset monitoring with acoustic emission

This paper presents the effectiveness of remote asset monitoring using acoustic emission. We will discuss different aspects of implementing an effective remote asset monitoring operation, such as considerations for the deployment of instrumentation in the field, instrumentation, communications, benefits of automatic data transfer from the monitoring system to a secure server, continuous automatic verification of hardware integrity, and automatic notification of potential damage or conditions affecting the asset integrity occurring. We will provide an in-depth view of the cloud monitoring implementation processes, and the use of machine learning and other data analytics techniques to identify abnormal acoustic emission data patterns. The application of these tools to specific case studies in electric power transformers, power steam generators and civil infrastructure assets will be presented. Finally, we will illustrate how these capabilities help to provide timely, actionable information to the asset owner.

Magnetic indicators for evaluating plastic strains in electrical steel: Toward non-destructive assessment of the magnetic losses

Electric steel is widely used for magnetic conversion of electrical energy such as in electric motors and power transformers. Electric steel sheets are cut using mechanical methods (such as punching) during manufacturing. These methods induce local plastic strains, which lead to overall degradation in magnetic performance.Plasticity and magnetic losses are physically related in electrical steel. It is impossible to measure the magnetic losses on the production line using standard methods. Non-destructive testing (NDT) based on magnetization mechanisms is a promising alternative, giving access to local plastic strains and indirectly to local magnetic losses.In this study, a setup was designed to stimulate the magnetization mechanisms separately while maintaining the same experimental conditions. The magnetization processes related to the domain wall kinetics were revealed to be more correlated to plastic strain. The magnetic incremental permeability hysteresis area and maximal value, and the coercivity associated with Barkhausen noise were found to be the best plastic strain indicators. Additional tests in an NDT context confirmed the correlations, the equal applicability of the proposed indicators, and their viability in predicting static hysteresis loss.

Mössbauer study of the material of the electric power transformers

Mössbauer study of the material of the electric power transformers

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.

Application of the Analysis of Variance (ANOVA) in the Interpretation of Power Transformer Faults

Electrical power transformers are the most exorbitant and tactically prominent components of the South African electrical power grid. In contrast, they are burdened by internal winding faults predominantly on account of insulation system failure. It is essential that these faults must be swiftly and precisely uncovered and suitable measures should be adopted to separate the faulty unit from the entire system. The frequency response analysis (FRA) is a technique for tracking a transformer’s mechanical integrity. Nevertheless, classifying the category of the fault and its gravity by benchmarking measured FRA responses is still backbreaking and for the most part, anchored in personnel proficiency. This work presents a quantum leap to normalize the FRA interpretation procedure by suggesting an interpretation code criteria based on an empirical survey of transformers ranging from 315 kVA to 40 MVA. The study then proposes an analysis of variance (ANOVA) based interpretation tool for diagnosing the statistical significance of FRA fingerprint and measured profiles. The latter cannot be relied upon by an expert or by the naked eye. Additionally, descriptive FRA frequency sub-region data statistics are proposed to evaluate the shift in both the magnitude and measuring frequency characteristics to formulate the recommended interpretation code criteria. To corroborate the code criteria by incorporating ANOVA and descriptive statistics, the study presents various case studies with unknown FRA profiles for fault diagnosis. The results constitute proof of the reliability of the proposed code criteria and a proposed hybrid of ANOVA and descriptive statistics.

The additional power losses investigation in electric power transformers 10/0.4 kW of rural electrical networks

The calculating results of the electricity load losses components in power transformers 10/0.4 kW of rural municipal networks of 0.38 kW is presented. Statistical processing of the obtained data has been carried out. We obtained the shares of the electricity load losses components in power transformers 10/0.4 kW of rural municipal networks of 0.38 kW with the simultaneous effect of asymmetric currents, higher harmonics of current and excess reactive power.

Engineering and design of a novel high-current source for testing low-voltage electrical equipment

This article presents the specification and design of a low-voltage high-current source with a capacity of up to 65 kA and 1 kV for testing electrical equipment such as low-voltage power circuit breaker switchgear, metal-enclosed switchgear, transformers, and cables, among others. Tests that can be performed with the proposed source include the short-circuit withstand on the low-voltage side of transformers, verification in temperature rise limits on electrical power connectors and transformers, and other compliance tests on power circuit breaker switchgear and controlgear assemblies. The design results from the comparison of general topologies of high-current sources according to the literature and test requirements established in current IEEE and IEC standards. In addition, the low-voltage high-current source emerged as a need for the Colombian electricity sector and will help comply with the test guidelines for switchgear and other low-voltage equipment, according to the national standard, also contributing to reducing costs of conducting tests abroad. The specification of the source resulted in the definition of three operating ranges for different types of tests, equipment, voltage, and current levels. Moreover, the general scheme of the low-voltage high-current source is provided with a description of the major components, namely, the novel medium- and low-voltage power modules based on the parallel and series arrangements of toroidal transformers to obtain levels of current and voltage of up to 65 kA and 1 kV, and the switching and protection, control, and measurement systems. Finally, a discussion of the results is presented, and the conclusions are highlighted. © 2017 Elsevier Inc. All rights reserved.

Left‐truncated and right‐censored field failure data: Review of parametric analysis for reliability

AbstractIn field reliability analyses, a data collection period is given to monitor the failure events from the field. Left‐truncation arises due to early failures occurring before the data collection period, and right‐censoring arises for late failures occurring beyond the monitoring period. Naïve analyses of left‐truncated and right‐censored data lead to biased estimation of the population lifetime of interest. A variety of models and methods have been developed to analyze the left‐truncated and right‐censored data for field reliability analyses. The goal of the paper is to review the existing models and methods for fitting left‐truncated and right‐censored data. Our review includes the existing statistical models, such as the exponential, Weibull, lognormal, gamma, Gompertz, Lomax, and spline models. We comprehensively review the statistical issues of maximum likelihood estimation, model selection, residual lifetime prediction, and Bayesian methods. Some of these methods are illustrated through the field reliability analysis of the electric power transformer dataset.

Intelligent Expert System for Diagnosing Faults and Assessing Quality of Power Transformer Insulation Oil by DGA Method

Accurate assessment of oil and fault diagnostics of electrical power transformer insulation for lifelong endurance are the key issues addressed in this research. The durability of a transformer is significantly determined by the quality of its insulation oil, which deteriorates over time due to temperature fluctuations and moisture content. Protecting transformers from potential failure through the early and precise diagnosis of faults and efficient assessment of oil quality during the actual conduct of the operation can avoid sizeable economic losses. The ANFIS Expert System that uses intelligent software plays an important role. The dissolved gas analysis (DGA) in oil is reliable for diagnosing faults and assessing insulation oil quality in transformers. Transformer power plant protection teams often suffer sudden breakdowns that lead to massive damage and huge financial losses. The oil in transformers must be appropriately treated to circumvent such failures. In this research, an ANFIS Expert System was used to diagnose faults and assess insulation oil status and quality in power transformers. by the Rogers ratio method depending on the DGA in oil, a suitable treatment was identified. The graphical user interface from the MATLAB environment was used and proven effective for fault diagnosis and oil quality evaluation. The training algorithm can assess oil quality according to the specifications of the IEEE standard C57-104 and the IEC standard 60599.