Transformer Fleet Research Articles

Methodology for the optimal replacement of power transformers based on their health index

Transformer life is critical to ensuring the reliability and stability of the power system. However, making decisions about the optimal time to replace a transformer is challenging due to the complexity of the interactions between equipment aging and applied maintenance. This study employs a risk analysis method based on the health status of the equipment and the consequences of its failure to develop a mathematical optimization methodology aimed at identifying the optimal timing for transformer replacement or monitoring activities, thus facilitating effective preventive maintenance strategies. The methodology was developed in AMPL with the objective of minimizing the risk index of the transformer fleet, taking as a constraint a given annual budget. It was applied to a study case covering a period of six years for a fleet of 39 110 kV transformers using real data. The results presented show that the developed methodology provides valuable information to the power system operator who is thus able to manage the transformer fleet and ensure a safe and financially efficient operation of the grid, providing a list of the 10 most risky units and suggesting a replacement and improvement scheme for them.

Estimating the age of power transformers using the concentration of furans in dielectric oil

Transformers have an insulation system based on dielectric oil and paper. Several factors as shortcircuits, high load, or overheating influence the aging of the insulating system. Thus, these materials are decomposed during transformer operation, and its state determines the status of the transformer. The degree of polymerization of dielectric paper is an indicator of its real condition. The concentration of furanic compounds in oil offer an estimation of paper degradation. Moreover, these compounds are easy to measure from oil samples. This paper review equations which relate one or more properties of the insulating system with the life expectancy of the transformer. These equations were used in a fleet of transformers in order to estimate the accuracy of these mathematical models. Results show that these methods achieve the most reliable estimation when the transformers have operated less than 8 years. Nevertheless, when the transformers are older, the estimation of lifetime based on furanic compounds is quite erratic and should be improved.

Exploitation Perspective Index as a Support of the Management of the Transformer Fleet.

This paper presents an alternative approach to the Transformer Assessment Index (TAI) by proposing a relatively simple rating method called the Exploitation Perspective Index (EPI). The method provides two numerical indicators: the first reflects the overall technical condition of the particular unit, and the second shows the condition of the unit in the context of the entire fleet. The objective of the EPI method is to support the decision-making process regarding the technical condition assessment of each of the transformers in the target population, considering not only technical but also economic aspects of transformer maintenance. Application of the method is described step by step, including input data, parametrization of the weights, and interpretation of the output results it provides. The proposed method is evaluated by two representative use cases and compared with two other methods. As a result, EPI confirms its applicability, and it has already been successfully implemented by the electric power industry. EPI can be potentially freely adopted for any transformer fleet, as well as for the specific situation of the utility, by adjusting the relevant parameters.

A comprehensive methodology for the optimization of condition-based maintenance in power transformer fleets

A comprehensive methodology for the optimization of condition-based maintenance in power transformer fleets

State-of-the-art review on asset management methodologies for oil-immersed power transformers

Owing to their vital function and high cost, oil-immersed power transformers represent key links in electricity grids. While extensive effort has been invested by industry in developing various condition monitoring and fault diagnosis apparatuses, it is not economically feasible to apply rigorous inspection and extensive testing to all power transformer fleets in the network. The fundamental purpose of asset management is to balance cost and reliability of the system. Maintaining the reliability of power transformers involves regular condition monitoring, fault diagnosis and overall health assessment. In addition, estimating the remnant life of power transformers can help asset managers plan capital investments. By reviewing the methods used for power transformer asset management, assets integrity team can decide the most suitable maintenance strategy for each transformer in the network. This paper is aimed to present state-of-the-art review for power transformer asset management, health assessment and remnant life methodologies. While many review papers on transformer fault diagnosis have been published in the literature, not much attention was given to present a state-of-the-art review on the three modules of the transformer asset managements that include fault diagnosis, reliability assessment and life management modules. These three modules are comprehensively presented in this paper that also presents future challenges and improvement in this field of research.

Deep Learning-Based Transformer Moisture Diagnostics Using Long Short-Term Memory Networks

Power transformers play a crucial role in maintaining the stability and reliability of energy systems. Accurate moisture assessment of transformer oil-paper insulation is critical for ensuring safe operating conditions and power transformers’ longevity in large interconnected electrical grids. The moisture can be predicted and quantified by extracting moisture-sensitive dielectric feature parameters. This article suggests a deep learning technique for transformer moisture diagnostics based on long short-term memory (LSTM) networks. The proposed method was tested using a dataset of transformer oil moisture readings, and the analysis revealed that the LSTM network performed well in diagnosing oil insulation moisture. The method’s performance was assessed using various metrics, such as R-squared, mean absolute error, mean squared error, root mean squared error, and mean signed difference. The performance of the proposed model was also compared with linear regression and random forest (RF) models to evaluate its effectiveness. It was determined that the proposed method outperformed traditional methods in terms of accuracy and efficiency. This investigation demonstrates the potential of a deep learning approach for identifying transformer oil insulation moisture with a R2 value of 0.899, thus providing a valuable tool for power system operators to monitor and manage the integrity of their transformer fleet.

Influence of Data Balancing on Transformer DGA Fault Classification With Machine Learning Algorithms

The application of artificial intelligence algorithms for transformer incipient fault classification using dissolved gas analysis (DGA) is an interesting engineering approach. However, there are various factors that affect the performance of artificial intelligence algorithms. This article presents the influence of the data balancing approach on transformer DGA fault classification with the machine learning (ML) approach. In this work, a total of 4580 DGA samples from in-service transformers are considered for training various ML models. The main challenge for the DGA problem lies in the availability of the normal degradation transformer data and its uniformity corresponding to different faults is almost impossible. This is because DGA is not an exact science, but an empirical approach subjects to variability. Thus, it is a usual practice to apply data sampling techniques that largely influence the efficiency of the algorithms. The present work reports the impact of the data balancing schemes on the performance of the fault classification and demonstrates that a careful choice of the data sampling method and ML algorithm is essential for DGA problems. To demonstrate the global scale ability of the propose model, the model is tested on the IEC TC ten data (field inspection data), while these data are not exposed to the machine during the learning stage. The ability of the ADASYN method in significant enhancement of the global scale capability of AI-based transformer DGA fault classification is reported. This approach will be helpful for condition monitoring engineers in transformer insulation diagnosis in implementing the monitoring modules for large transformer fleets and understanding the insulation oil behavior over years.

Optimal exploitation of power transformer fleet considering loss of life and economic evaluation based on failure probability

• Loss of life and costs of a transformer fleet are optimised simultaneously. • An economic evaluation model using failure probability which is defined based on degree of polymerization is proposed. • Key ageing factors are considered in defining loss of life and modified cost models. • NSGA-II is employed for co-optimisation of the proposed objective function. Elaborating the technical and economic aspects of exploiting power transformers, this paper employs an objective function to optimise costs and loss of lives of transformers. Using non-dominated sorting genetic algorithm (NSGA-II), the objective function finds loading factors of power transformers which guarantee optimum values for both aspects. The proposed method is first performed on a transmission grid including 15 substations without considering failure probability of power transformers. Proving that the proposed method can optimise both technical and economic functions efficiently according to the results, the modified economic model which utilizes failure probability based on degree of polymerization is employed in the second step. It is shown that the economic evaluation considering failure probability and life expectancy of transformers leads to accurate and reliable values. The method helps utilities and companies to manage exploiting and loading of power transformer fleet (not just a single power transformer) and mitigate costs and loss of lives.

Feedforward Artificial Neural Network (FFANN) Application in Solid Insulation Evaluation Methods for the Prediction of Loss of Life in Oil-Submerged Transformers

In this work, the application of a feed-forward artificial neural network (FFANN) in predicting the degree of polymerization (DP) and loss of life (LOL) in oil-submerged transformers by using the solid insulation evaluation method is presented. The solid insulation evaluation method is a reliable technique to assess and predict the DP and LOL as it furnishes bountiful information in examining the transformer condition. Herein, two FFANN models are proposed. The first model is based on predicting the DP when only the 2-Furaldehyde (2FAL) concentration measured from oil samples is available for new and existing transformers. The second FFANN model proposed is based on predicting the transformer LOL when the 2FAL and DP are available to the utility owner, typically for the transformer operating at a site where un-tanking the unit is a daunting and unfeasible task. The development encompasses constructing numerous FFANN designs and picking networks with superlative performance. The training and testing procedures databank is based on the dataset of the 2FAL and DP from a fleet of transformers and measured from laboratory analysis. The correlation coefficient of 0.964 was ascertained when the DP was predicted using the 2FAL measured in oil. In the FFANN model, a correlation coefficient of 0.999 against the practical data where one can make a reliable prediction of transformer LOL concerning 2FAL was generated and the amount of DP present produced. This model can be used to predict the DP and LOL of new and existing transformers at the manufacturer’s premises and operating in the field, respectively. To the knowledge of the authors, no research work has been published addressing the methods proposed in this work.

A fuzzy inference-based approach for estimating power transformers risk index

Risk assessment is a key point for power system operators interested in the implementation of asset management systems. Among critical electrical assets, the power transformer is probably the most representative. This paper presents a novel method for estimating a risk index for power transformers. The proposed method is based on three Fuzzy Inference Systems (FIS), aimed to integrate expert criteria for the creation of rules and the definition of the membership functions of the entries. The first FIS is dedicated to the calculation of a transformer Health Index (HI), based on the results of the physical-chemical tests normally performed on the insulating liquid of the units. The second FIS is oriented to the determination of a Consequence Factor (CF) of the final failure of the power transformer, and it is determined considering aspects such as overload to other assets, average load supplied by the unit, critical loads supplied, oil volume, proximity to other buildings and penalties for the unavailability of the asset. The last FIS combines the results of the previous FIS, i.e., HI and CF, to provide an estimation of the transformer risk index. The methodology has been designed to perform management of power transformer fleets by allowing decisions to be made, regarding investments, based on risk assessment. The proposal has been tested on a fleet of 15 units. Obtained results show that the FIS for combining HI and the CF leads to more consistent risk indexes, than those obtained when performing the conventional operation of multiplication between HI and FC.

Analysis of Asset Management Models for a Transformer Fleet in the National Laboratory of Smart Grids (LAB+i)

The study of the degradation of power transformers in the electrical network has become a subject of relevant analysis by network operators and companies, associated with the probability of failures and operation quality. For this reason this paper firstly presents the classification of a set of 12 Asset Management models related to power transformers monitoring and, then, the application of three of them in three substations of the National Laboratory of Smart Grids (LAB+i) located at Bogot´a Campus of the Universidad Nacional de Colombia. As a result, the main challenges were identified concerning the Asset Management application in transformer fleets related to data availability and precision. Finally, it was identified that the development of an Asset Management model that uses non-invasive real-time measurements is needed for continuous monitoring of power systems and diagnosis.

The structure of the park of power transformers with a higher voltage of 6-10 Kv on the example of the electric grid organization of the branch of pjsc «Rosseti center»-«Orelenergo», serving rural electric networks

THE PURPOSE. Evaluation of the power transformer fleet 6-10/0,23-0,4 kV on the example of a branch of PJSC «Rosseti Center»-«Orelenergo».METHODS. In the work, based on the database of the branch of PJSC «Rosseti Center»-«Orelenergo», an analysis was made of the fleet of power transformers with a higher voltage of 6-10 kV in terms of their number, circuits and groups of connection of windings, rated power, terms of service, as well as energy efficiency classes, taking into account the current standards of the technical organization of PJSC «Rosseti».RESULTS. According to the results of the study, it was revealed that among the transformer fleet of the branch of PJSC «Rosseti Center»-«Orelenergo», the number of which is 6026 units, 4528 (73% of the total number) transformers have a circuit and a group of winding connections Y/Y0. The most numerous are transformers with rated capacities of 63 kVA, 100 kVA, 160 kVA, 250 kVA (respectively 853, 1454, 1252, 802 pieces of equipment). It was also revealed that only 268 transformers out of 6206, i.e. 4.3% of the total amount comply with the standard of PJSC «Rosseti» in terms of modern requirements for the level of energy efficiency class.CONCLUSION. A variant of the strategy for replacing power transformers in the branch of PJSC «Rosseti Center»-«Orelenergo» is proposed, within the framework of which trasformers with a given design, circuit and winding connection group, rated capacities and energy efficiency classes are replaced. The implementation of the strategy proposed in the work will make it possible to reduce total electricity losses by 2.3%, as well as increase the share of energy-efficient transformers from 4.3% to 20.4% in the branch of PJSC «Rosseti Center»-«Orelenergo».

Risk Assessment Algorithm for Power Transformer Fleets Based on Condition and Strategic Importance

In every electric power system, power transformers (PT) play a critical role. Under ideal circumstances, PT should receive the utmost care to maintain the highest operative condition during their lifetime. Through the years, different approaches have been developed to assess the condition and the inherent risk during the operation of PT. However, most proposed methodologies tend to analyze PT as individuals and not as a fleet. A fleet assessment helps the asset manager make sound decisions regarding the maintenance scheduling for groups of PT with similar conditions. This paper proposes a new methodology to assess the risk of PT fleets, considering the technical condition and the strategic importance of the units. First, the state of the units was evaluated using a health index (HI) with a fuzzy logic algorithm. Then, the strategic importance of each unit was assessed using a weighting technique to obtain the importance index (II). Finally, the analyzed units with similar HI and II were arranged into a set of clusters using the k-means clustering technique. A fleet of 19 PTs was used to validate the proposed method. The obtained results are also provided to demonstrate the viability and feasibility of the assessment model.

Identification and Application of Machine Learning Algorithms for Transformer Dissolved Gas Analysis

Power transformers represent one of the most abundant and expensive components in the electric power industry. Dissolved gas analysis (DGA) of transformer is the most widely accepted diagnostic tool across the globe to understand insulation incipient failures. Nevertheless, DGA fault gas interpretation is a remarkable challenge for transformer owners and utility engineers. Several computational techniques have been adopted for DGA fault classification along with offline methods. However, limited data availability, high ambiguity in DGA interpretation, suitability, and model accuracy are critical challenges in the DGA fault classification using computational techniques. In this work, highly diverse and large DGA data samples of in-service transformer fleets from five different utilities have been used to develop an efficient fault classification methodology. A total of 4580 DGA samples and IEC TC 10 database are used for training and testing, respectively, for various machine learning algorithms. Discussions on performance indicators and evaluation of several algorithms to verify the most suitable class algorithms are also the focus of this work. Furthermore, a best-performing model is identified based on various performance indicators. The hyperparameters of the best model are further tuned to achieve a most precise fault classification. It is inferred that non-parametric methods and non-linear SVM are best suitable for transformer DGA fault classification. Importantly, the rankings in the present study suggest that transformer DGA fault prediction is better with ensemble learning methods.

Comparison of the Thermal Performance of Mineral Oil and Natural Ester for Safer Eco-Friendly Power Transformers A Numerical and Experimental Approach

At present, most of the worldwide transformer fleet uses mineral oil as insulation fluid. However, the use of natural ester is playing an increasing role as safer and eco-friendly alternative to mineral oil. From the utilities’ perspective, the change in the mineral oil paradigm can be approached by replacing their assets by new eco-friendly transformers or refurbishing their assets, substituting only the insulating fluid. To make an informed decision, guidelines indicating how a transformer designed for mineral oil would behave when operating with natural ester, are of paramount importance. In the present research, temperature rise tests were carried out in a 15MVA ODAF core-type power transformer, under different operating conditions. Tests were run for mineral oil as insulating fluid and subsequently repeated for natural ester. To gain further insights on experimental results, thermal modelling of the transformer under the same test conditions was carried out, using Thermal-Hydraulic Network Models and Computational Fluid Dynamics techniques. From the present work it was found that the relationship between the thermal behaviour of the transformer using natural ester or mineral oil is not simple as it depends on the operating conditions of the transformer and on the geometry of the windings.

Optimal Decision Making in Electrical Systems Using an Asset Risk Management Framework

In this paper, a methodology for optimal decision making for electrical systems is addressed. This methodology seeks to identify and to prioritize the replacement and maintenance of a power asset fleet optimizing the return of investment. It fulfills this objective by considering the risk index, the replacement and maintenance costs, and the company revenue. The risk index is estimated and predicted for each asset using both its condition records and by evaluating the consequence of its failure. The condition is quantified as the probability of failure of the asset, and the consequence is determined by the impact of the asset failure on the whole system. Failure probability is estimated using the health index as scoring of asset condition. The consequence is evaluated considering a failure impact on the objectives of reliability (energy not supplied -ENS), environment, legality, and finance using Monte Carlo simulations for an assumed period of planning. Finally, the methodology was implemented in an open-source library called PywerAPM for assessing optimal decisions, where the proposed mathematical optimization problem is solved. As a benchmark, the power transformer fleet of the New England IEEE 39 Bus System was used. Condition records were provided by a local utility to compute the health index of each transformer. Subsequently, a Monte Carlo contingency simulation was performed to estimate the energy not supplied for a period of analysis of 10 years. As a result, the fleet is ranked according to risk index, and the optimal replacement and maintenance are estimated for the entire fleet.

Dealing With Data Uncertainty for Transformer Insulation System Health Index

Health index has been widely accepted as a powerful tool for monitoring the condition of power transformer insulation system based on various diagnostic parameters. While this approach has been extensively discussed in the literature, not much attention was given to provide effective solutions to the uncertainty in the used data. According to CIGRE 761, data quality issues may arise due to measurement accuracy as well as incompleteness and unavailability of the required data. Therefore, this article presents the implementation and evaluation of a certainty level model for transformer insulation system health index to deal with data uncertainty. The impact of data unavailability on the health index results is also investigated. Certainty level of the health index is determined by the criticality level of available data, and is reported along with the health index result. A method to handle unavailable data by predicting the oil interfacial tension (IFT) using Random Forest approach is also presented. The proposed certainty level model is designed to accommodate the predicted value of missed data into the health index model while considering its prediction accuracy. The robustness of the developed model is validated through its application in assessing the health condition of six in-service power transformers. The results indicate that by including the proposed certainty level and the prediction approach to eliminate the issue of uncertain and missed diagnostic data, an asset management decision can be taken on operating power transformer fleets with high level of confidence.

Artificial Intelligence-Based Power Transformer Health Index for Handling Data Uncertainty

Power transformer is a critical and expensive asset in electric transmission and distribution networks. It is essential to monitor the health condition of all power transformer fleet in such networks to avoid unwanted outages. The health index (HI) is a quick and efficient way to assess the condition of power transformers based on multi-criteria. While Power transformer HI method has been well presented in the literature, not much attention was given to handle the uncertainty and reliability of this method due to unavailability of used data. Therefore, this paper aims to tackle this issue through employing Artificial Intelligence (AI)-based techniques to reveal the health condition of power transformers with high accuracy and at the same time handling data uncertainty. The proposed HI approach assesses the power transformer insulation system based on oil quality, dissolved gas analysis (DGA), and paper condition. In this regard, collected data from 504, 150-kV transformers are used to establish the proposed AI-models. Seven AI algorithms including k-Nearest Neighbor (kNN), Support Vector Machine (SVM), Random Forest (RF), Naive Bayes (NB), Artificial Neural Network (ANN), Adaptive Boosting (AdaBoost), and Decision Tree are investigated. A performance comparison of the proposed AI-based HI models is carried out using the scoring-weighting-based HI method as the reference. Results show that RF model provides the best performance in predicting power transformer HI with an accuracy of 97.3%.

Improving the ageing rate of transformer fleet using imperialist competitive algorithm

Power transformers are vital to delivering energy in power grids and they bear considerable maintenance costs annually. One of the major issues for power transformers is the ageing of the insulation paper used in windings that affects the life of transformers significantly. Utilising an appropriate procedure for loading of transformers can reduce the ageing rate of insulation and can improve the life expectancy. Therefore, it is crucial to obtain the values for the ageing rate which are optimum and to prevent transformer fleet from accelerated ageing. Although techniques are available to calculate the ageing rate and loss of life of a single transformer, utilities looking into the transformer fleet ageing need a better tool to improve the ageing rate of all transformers together. The main contribution of this study is to provide a method for the transformer fleet ageing optimisation rather than single unit ageing approach. This study proposes a novel approach utilising different optimisation techniques that are capable of representing different factors in order to obtain an optimum ageing rate for a fleet of transformers. The optimum ageing rate can provide a technical tool for asset managers, utility engineers and owners for better power transformer fleet management.

Optimal scheduling of power transformers preventive maintenance with Bayesian statistical learning and influence diagrams

Transformer preventive maintenance scheduling—as an integral part of the power utility company’s asset–management process—if optimized properly affords considerable savings in maintenance costs and results in the increased reliability of transformer stations. However, situation is exacerbated by the considerable uncertainty involved in the assessment of the transformer health status, not necessarily associated with its age, as well as in the projected asset replacement and maintenance costs. There is also uncertainty inherent in the transformer tests as such. This paper proposes a novel transformer optimal preventive maintenance scheduling approach, with the use of the Bayesian statistical learning and a generalization of the Bayesian networks, for decision-making based on the expected utility hypothesis. Proposed approach, in essence, manages risk associated with taking different preventive actions by leveraging uncertain information regarding the transformer health and the expected costs of these actions. In this way, each prospective decision has a monetary value attached to it, which depends on the particular circumstances defined by the power utility company. Time-related effects are introduced into the decision-making process by the Bayesian updating of the prior belief concerning the transformer health with new information. Solving a decision problem amounts to determining an (optimal) strategy that maximizes the expected utility for the decision maker. On top of that, Bayesian approach, uniquely, offers explicit account for the full uncertainty associated with every step of the decision-making process. The proposed approach, and its advantages, will be demonstrated on a fleet of power transformers. It will be shown that model can predict with a 90% accuracy and a high confidence the transformer health index class from a small dataset. It will also be shown that it can predict with high expected profits which transformers in the dataset would benefit the most from the early intervention (recuperating around 25% of the projected costs and retaining over 90% of the projected savings).