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Abstract A key factor in ensuring the efficient and safe operation of power transformers is the early and accurate diagnosis of incipient faults. Among the tools available to achieve this goal, dissolved gas analysis (DGA) is widely used by power transformers' maintenance professionals. It is a preventive maintenance tool, used for condition monitoring, fault diagnosis and unplanned outage prevention. With the development of artificial intelligence (AI), many intelligent‐based methods using AI tools have been proposed in the literature for DGA data interpretation. Although these methods achieve high diagnostic accuracies and improve DGA efficiency, they are generally complicated and the research documented in these publications is difficult to replicate. Traditional DGA‐based methods are simple, easy to understand and implement, and widely used by power transformers' maintenance professionals. Many methods proposed in recent years overcome the limitations of the pioneer methods and are increasingly effective. The authors present a detailed and comprehensive literature review of the traditional DGA‐based methods for mineral oil‐immersed power transformer faults diagnosis. This review also addresses ways to improve the efficiency of the available traditional methods. Some pitfalls that need to be taken into account to improve the efficiency of the DGA‐based diagnostic methods are also presented.

In this contribution, a simple non-destructive characterization of aging degree of oil-paper insulation materials based on the reflectance is proposed. Samples of cellulose Kraft paper having different thicknesses, were thermally aged in a mineral insulating oil and a synthetic ester with a controlled aging history. The degree of polymerization of the non-aged and aged paper samples was measured according to ASTM D4243 to monitor the cellulose degradation. In addition, the samples were optically analyzed to assess changes in paper’s reflectance. The reflectance spectra of the thermally aged paper samples were statistically analyzed using linear, single variable, and multi-variable analyses by considering eight popular variables. This enables correlating the reflectance to the degree of polymerization and identifying a suitable regression model. Appropriate variable interaction has been performed among which two best-fit models with goodness of fit ≥ 0.9 have been identified. The estimation of the cellulose paper’s DP using the proposed models is reported. The experimental results show that the proposed approach can be used in characterizing aging degree of oil-paper insulation and has the potential to be implemented online as an effective monitoring technique.

The power transformer is a critical component in any transmission and distribution grid. This vital machine faces new thermal stresses arising from challenges related to energy transition along with the ever-increasing load. Understanding and predicting transformer thermal behavior is fundamental to optimizing operation and maintenance, and consequently ensuring the system’s reliability. Transformer thermal modeling (TTM) has garnered significant attention among engineers and researchers. Various approaches to TTM exist, including physical, semi-physical, physical-based numerical, and artificial intelligence (AI)-based models, with the latter being relatively unexplored in the literature. This contribution presents a comprehensive review of AI-based applications for transformer thermal modeling, examining commonly used techniques, inputs, and outputs. Perspectives in the field are discussed, with a focus on gray-box and adaptive models. The impacts of AI-based models in developing digital transformer twins are also explored. Prominent models in TTM include artificial neural networks and fuzzy systems, with support vector regression also featuring among the techniques utilized. Load and ambient temperature are primary inputs in top-oil temperature predictions, while top-oil temperature is crucial for hot-spot temperature predictions. Incorporating historical data is increasingly common in both cases. This review serves as a guide for researchers interested in TTM and highlights perspectives for future developments. AI-based applications offer powerful tools for TTM and, despite present challenges, hold significant potential for transformation in the field.

In the era of Industry 4.0, digital transformation has spurred the swift advancement of technologies, including intelligent predictive maintenance scheduling, prognostics and health management. The accurate prediction of remaining useful life plays a crucial role in these technologies as it extends power equipment’s safe operational duration and decreases the maintenance costs associated with unforeseen shutdowns. Also, the increased accessibility of data for monitoring system conditions has paved the way for the more immense adoption of machine learning models in prognostics and health management for power transformers. At the moment, with the ever-increasing demand for electricity, there is a corresponding increase in the degradation processes of power transformers. Transformers insulation system and more importantly, the paper insulation happens to be the principal part where the degradation is prominent. Therefore, an accurate prediction of the insulating paper condition through its degree of polymerization is required to guarantee the reliability of power transformers. In this regard, the predictions, reliability, and health monitoring of this power equipment can be actualized by modeling the degradation of transformer insulation paper through several machine learning frameworks. In this view, this review paper has been drafted not only to serve as a guide for researchers interested in the fields of transformer insulation system fault prognosis but also to offer insights into potential research directions as existing literature in modeling and evaluating transformer paper insulation is presented.

The rapid development of self-driving vehicles requires integrating a sophisticated sensing system to address the various obstacles posed by road traffic efficiently. While several datasets are available to support object detection in autonomous vehicles, it is crucial to carefully evaluate the suitability of these datasets for different weather conditions across the globe. In response to this requirement, we present a novel dataset named the Canadian Vehicle Datasets (CVD). Subsequently, we present deep learning models that use this dataset. The CVD comprises street-level videos which were recorded by Thales, Canada. These videos were collected with high-quality cameras mounted on a vehicle in the Canadian province of Quebec. The recordings were made during daytime and nighttime, capturing weather conditions such as hazy, snowy, rainy, gloomy, nighttime and sunny days. A total of 10000 images of vehicles and other road assets are extracted from the collected videos. A total of 8388 images were annotated with corresponding generated labels 27766 with their respective 11 different classes. We analyzed the performance of the YOLOv8 model trained using the existing RoboFlow dataset. Then, we compared it with the model trained on the expanded version of RoboFlow using the proposed weather-specific dataset, CVD. Final values of improved accuracy of 73.26 %, 72.84 %, and 73.47 % (Precision/Recall/mAP) were reported upon adding the proposed dataset. Finally, the model trained on this diverse dataset exhibits heightened robustness and proves highly beneficial for both autonomous and conventional vehicle operations, making it applicable not only in Canada but also in other countries with comparable weather conditions.

In most industrialized countries, power transformers built several decades ago are approaching the end of their operational lifespan. The ongoing energy transition, focused on developing 100% renewable energy sources and accelerating global transportation electrification, further exacerbates these assets. Combined with rising electricity demand, there is an increasing risk of critical transformers’ degradation acceleration. In this context, understanding the aging mechanisms of the insulation system inside these essential assets, which form the core of every energy network, becomes paramount for today’s managers and engineers responsible for their operations. The acids generated through oil oxidation can be classified into two categories: low molecular weight acids (LMAs), which are inherently more hydrophilic and consequently have a greater impact on the degradation rate of solid insulation through hydrolysis, and high molecular weight acids (HMAs), which do not significantly contribute to the degradation of paper insulation. This study specifically addresses the impact of acids generated through oil oxidation—focusing on LMAs. New oil samples were infused with different ratios of LMAs before impregnation. The impregnated paper samples underwent thermal aging at 115 °C. Different physicochemical and dielectric properties were investigated. The investigations revealed that oils blended with formic acid exhibited more adverse effects on the insulation system compared to other LMAs. This information is essential for industry professionals seeking to mitigate the risks associated with transformer degradation and extend the lifespan of these critical assets during the energy transition.

Modern power grids are undergoing a significant transformation with the massive integration of renewable, decentralized, and electronically interfaced energy sources, alongside new digital and wireless communication technologies. This transition necessitates the widespread adoption of robust online diagnostic and monitoring tools. Sensors, known for their intuitive and smart capabilities, play a crucial role in efficient condition monitoring, aiding in the prediction of power outages and facilitating the digital twinning of power equipment. This review comprehensively analyzes various sensor technologies used for monitoring power transformers, focusing on the critical need for reliable and efficient fault detection. The study explores the application of fiber Bragg grating (FBG) sensors, optical fiber sensors, wireless sensing networks, chemical sensors, ultra-high-frequency (UHF) sensors, and piezoelectric sensors in detecting parameters such as partial discharges, core condition, temperature, and dissolved gases. Through an extensive literature review, the sensitivity, accuracy, and practical implementation challenges of these sensor technologies are evaluated. Significant advances in real-time monitoring capabilities and improved diagnostic precision are highlighted in the review. It also identifies key challenges such as environmental susceptibility and the long-term stability of sensors. By synthesizing the current research and methodologies, this paper provides valuable insights into the integration and optimization of sensor technologies for enhancing transformer condition monitoring and reliability in modern power systems.

Transformers are fundamental and among the most expensive electrical devices in any power transmission and distribution system. Therefore, it is essential to implement powerful maintenance methods to monitor and predict their condition. Due to its many advantages—such as early detection, accurate diagnosis, cost reduction, and rapid response time—dissolved gas analysis (DGA) is regarded as one of the most effective ways to assess a transformer’s condition. In this contribution, we propose a new probabilistic hierarchical intelligent system consisting of five subnetworks of the radial basis functions (RBF) type. Indeed, hierarchical classification minimizes the complexity of the discrimination task by employing a divide-and-conquer strategy, effectively addressing the issue of unbalanced data (a significant disparity between the categories to be predicted). This approach contributes to a more precise and sophisticated diagnosis of transformers. The first subnetwork detects the presence or absence of defects, separating defective samples from healthy ones. The second subnetwork further classifies the defective samples into three categories: electrical, thermal, and cellulosic decomposition. The samples in these categories are then precisely assigned to their respective subcategories by the third, fourth, and fifth subnetworks. To optimize the hyperparameters of the five models, the Linde–Buzo–Gray algorithm is implemented to reduce the number of centers (radial functions) in each subnetwork. Subsequently, a single-layer perceptron is trained to determine the optimal synaptic weights, which connect the intermediate layer to the output layer. The results obtained with our proposed system surpass those achieved with another implemented alternative (a single RBF), with an average sensitivity percentage as high as 96.85%. This superiority is validated by a Student’s t-test, showing a significant difference greater than 5% (p-value < 0.001). These findings demonstrate and highlight the relevance of the proposed hierarchical configuration.

An On-Load Tap Changer (OLTC) that regulates transformer voltage is one of the most important and strategic components of a transformer. Detecting faults in this component at early stages is, therefore, crucial to prevent transformer outages. In recent years, Hydro Quebec initiated a project to monitor the OLTC’s condition in power transformers using vibro-acoustic signals. A data acquisition system has been installed on real OLTCs, which has been continuously measuring their generated vibration signal envelopes over the past few years. In this work, the multivariate deep autoencoder, a reconstruction-based method for unsupervised anomaly detection, is employed to analyze the vibration signal envelopes generated by the OLTC and detect abnormal behaviors. The model is trained using a dataset obtained from the normal operating conditions of the transformer to learn patterns. Subsequently, kernel density estimation (KDE), a nonparametric method, is used to fit the reconstruction errors (regarding normal data) obtained from the trained model and to calculate the anomaly scores, along with the static threshold. Finally, anomalies are detected using a deep autoencoder, KDE, and a dynamic threshold. It should be noted that the input variables responsible for anomalies are also identified based on the value of the reconstruction error and standard deviation. The proposed method is applied to six different real datasets to detect anomalies using two distinct approaches: individually on each dataset and by comparing all six datasets. The results indicate that the proposed method can detect anomalies at an early stage. Also, three alarms, including ignorable anomalies, long-term changes, and significant alterations, were introduced to quantify the OLTC’s condition.

As the focus tilts toward online detection methodologies for transformer oil aging, bypassing challenges associated with traditional offline methods, such as sample contamination and misinterpretation, fiber optic sensors are gaining traction due to their compact nature, cost-effectiveness, and resilience to electromagnetic disturbances that are typical in high-voltage environments. This study delves into the sensitivity analysis of intensity-modulated plastic optical fiber sensors. The investigation encompasses key determinants such as the influence of optical source wavelengths, noise response dynamics, ramifications of varying sensing lengths, and repeatability assessments. Our findings highlight that elongating sensing length detrimentally affects both linearity response and repeatability, largely attributed to a diminished resistance to noise. Additionally, the choice of the optical source wavelength proved to be a critical variable in assessing sensor sensitivity.

The aim of this research is to predict the mechanical properties along with the behaviors of standard insulating paper used in power transformers under thermal aging. This is conducted by applying an artificial neural network (ANN) trained with a multiple regression model and a particle swarm optimization (MR-PSO) model. The aging of the paper insulation is monitored directly by the tensile strength and the degree of polymerization of the solid insulation and indirectly by chemical markers using 2-furfuraldehyde compound content in oil (2-FAL). A mathematical model is then developed to simulate the mechanical properties (degree of polymerization (DPV) and tensile index (Tidx)) of the aged insulation paper. First, the datasets obtained from experimental results are used to create the MR model, and then the optimizer method PSO is used to optimize its coefficients in order to improve the MR model. Then, an ANN method is trained using the MR-PSO to create a nonlinear correlation between the DPV and the time, temperature, and 2-FAL values. The acquired results are assessed and compared with the experimental data. The model presents almost the same behavior. In particular, it has the capability to accurately simulate the nonlinear property behavior of insulation under thermal aging with an acceptable margin of error. Since the life expectancy of power transformers is directly related to that of the insulating paper, the proposed model can be useful to maintenance planners.

This paper reports the experimental and theoretical investigations of particle levitation voltage on thermally aged ester fluid, under AC and harmonic AC voltages, in the presence of both electric and magnetic fields (130 mT and 160 mT). The results indicate a higher sensitivity to identify partial discharge (PD) initiated due to particle movement in aged ester fluids with an ultra-high frequency (UHF) sensor than the fluorescent fiber technique. The cause for the reduction in sensitivity of PD detection due to the fluorescent fiber technique with thermally aged fluid is analyzed using steady-state fluorescent measurement. The reduction in the levitation voltage noticed under high-frequency AC voltages is much more severe than its impact under the fundamental frequency of AC supply voltage. In addition, the presence of a magnetic field reduces the magnitude of levitation voltage substantially. The UHF signals generated due to particle movement-initiated discharges with aged ester fluids indicate a shift in its dominant frequency of 0.9 GHz under the absence of a magnetic field to around 0.6 GHz with the effect of a magnetic field.

Abstract In this contribution, a new dissolved gas analysis (DGA) method combining key gases and ratio approaches for power transformer fault diagnostic is presented. It is based on studying subsets and uses the five main hydrocarbon gases including hydrogen (H2), methane (CH4), ethane (C2H6), ethylene (C2H4), and acetylene (C2H2). The proposed method uses 475 samples from the dataset divided into subsets formed from the maximum and minimum(s) concentrations of the whole dataset. It has been tested on 117 DGA sample data and validated on the International Electrotechnical Commission (IEC) TC10 database. The performance of the proposed diagnostic method was evaluated and compared with the following diagnostic methods: IEC ratios method, Duval's triangle (DT), three ratios technique (TRT), Gouda's triangle (GT), and self‐organizing map (SOM) clusters. The results found were analysed by computer simulations using MATLAB software. The proposed method has a diagnosis accuracy of 97.42% for fault types, as compared to 93.16% of TRT, 96.58% of GT method, 97.25% of SOM clusters method and 98.29% of DT method. However, in terms of fault severity, the proposed method has a diagnostic accuracy of 90.59% as compared to 78.90% of SOM clusters method, 83.76% of TRT, 88.03% of DT method, and 89.74% of GT method.

The detection of On-Load Tap-Changer (OLTC) faults at an early stage plays a significant role in the maintenance of power transformers, which is the most strategic component of the power network substations. Among the OLTC fault detection methods, vibro-acoustic signal analysis is known as a performant approach with the ability to detect many faults of different types. Extracting the characteristic features from the measured vibro-acoustic signal envelopes is a promising approach to precisely diagnose OLTC faults. The present research work is focused on developing a methodology to detect, locate, and track changes in on-line monitored vibro-acoustic signal envelopes based on the main peaks extraction and Euclidean distance analysis. OLTC monitoring systems have been installed on power transformers in services which allowed the recording of a rich dataset of vibro-acoustic signal envelopes in real time. The proposed approach was applied on six different datasets and a detailed analysis is reported. The results demonstrate the capability of the proposed approach in recognizing, following, and localizing the faults that cause changes in the vibro-acoustic signal envelopes over time.

Insulating Liquids are widely used for their electrical and thermal properties in power apparatuses, particularly at the level of liquid-filled transformers. With the shift in engineering aspects towards sustainable development, it is important to find a sustainable solution with ecofriendly nature. Therefore, alternative (biodegradable) liquids are of high importance in the global transformer communities. In the present study, the alternative dielectric fluids (ester-based) feasibility for potential regeneration with Fuller’s earth is investigated. The experimental results are confined to the reclamation temperature as well as the ratio of Fuller's earth (the sorbent) and the liquid. A suitable laboratory treatment apparatus is designed and is adopted in this study. Promising measurements to comment on the effectiveness of the treatment have been performed at controlled treatment temperature and sorbent-liquid ratio with the ASTM 7150-13 as a reference norm.The results of this study allowed 80°C and 1 g/30 ml as affirmative conditions for the present experimental conditions. Diagnostic measurements include turbidity, particle counter, and UV spectrophotometry before and after treatments. It is inferred that fuller’s earth is not a promising sorbent for the reclamation of ester liquids.

Frequency response analysis (FRA) is being employed worldwide as one of the main methods for the internal condition assessment of transformers due to its capability of detecting mechanical changes. Nonetheless, the objective interpretation of FRA measurements is still a challenge for the industry. This is mainly attributable to the lack of complete data from the same or similar units. A large database of FRA measurements can contribute to improving classification algorithms and lead to a more objective interpretation. Due to their destructive nature, mechanical deformations cannot be performed on real transformers to collect data from different scenarios. The use of simulation and laboratory transformer models is necessary. This research contribution is based on a new method using Finite Element Method simulation and a lumped element circuit to obtain FRA traces from a laboratory model at healthy and faulty states, along with an optimization method to improve capacitive parameters from estimated values. The results show that measured and simulated FRA traces are in good agreement. Furthermore, the faulty FRA traces were analyzed to obtain the characterization of faults based on the variation of the lumped element’s parameters. This supports the use of the proposed method in the generation of faulty frequency response traces and its further use in classifying and localizing faults in the transformer windings. The proposed approach is therefore tailored for generating a larger and unique database of FRA traces with industrial importance and academic significance.

The degradation of the insulation system in liquid-filled power transformers is a serious concern for electric power utilities. The insulation system’s ageing is accelerated by moisture, acids, oxidation products, and other decay particles (soluble and colloidal). The presence of these ageing by-products is detrimental to the insulation system and may further lead to premature ageing and serious consequences. The ageing mechanisms of oil-paper insulation are complex, highly interrelated, and strongly temperature-dependent. The operating temperature of the transformer insulating system has a direct relationship with the loading profile. The major aspect that is witnessed with the fluctuating temperatures is moisture migration and subsequent bubble evolution. In other words, gas bubbles evolve from the release of water vapor from the cellulosic insulation wrapped around the transformer windings. The models presented in the existing standards, such as the IEC Std. 60076-7:2018 and the IEEE Std. C57.91:2011, are mainly based on the insulation temperature, which acts as a key parameter. Several studies have investigated the moisture dynamics and bubbling phenomenon as a function of the water content in the paper and the state of the insulation system. Some studies have reported different prototypes for the estimation of the bubble inception temperatures under selected conditions. However, there are various attributes of the insulation system that are to be considered, especially when expanding the models for the alternative liquids. This paper reviews various evaluation models reported in the literature that help understand the bubbling phenomenon in transformer insulation. The discussions also keep us in the loop on the estimation of bubbling behavior in alternative dielectric liquids and key attributable factors for use in transformers. In addition, useful tutorial elements focusing on the bubbling issue in transformers as well as some critical analyses are addressed for future research on this topic.

Frequency response analysis is a powerful tool for mechanical fault diagnostics in power transformers. However, interpretation schemes still today depend on expert analyses, mainly because of the complex structure of power transformers. One of the fundamental shortcomings of experimental investigations is that mechanical deformations cannot be managed on real transformers to obtain data for different scenarios because they are too destructive. To address this issue in a systematic way, the current research used a specially designed laboratory transformer model that allows mechanical defects to be introduced so its frequency response can be evaluated under different conditions. The key feature of this model is the non-destructive interchangeability of its winding sections, allowing reproducibility and repeatability of frequency response measurements. Numerical indices were compared over key performance indicators (linearity, sensitivity and monotonicity). The analysis indicated that comparative standard deviation offered promising results for evaluation of mechanical deformations on the laboratory winding model given its monotonic behaviour, sensitivity and linear increase with fault severity. Additionally, support vector machine learning, radial basis function neural network and the statistical k-nearest neighbour method were used for fault classification with different strategies and configurations. While limited data from different transformers are used in the available literature, the approach discussed here considers 371 measurements from the same transformer model. The test results are supportive and demonstrate great accuracy when machine learning is used for winding fault classification.

From the recent past, biodegradable dielectric fluids are becoming popular across the global utilities for use in oil-filled transformers. To date, several utilities have started using biodegradable insulating fluids for new and retro-filled power and distribution transformers. The intent of this article is to compare and analyze various aspects that are involved in the successful operation and maintenance of mineral oil and ester filled transformers. The comparative analysis in this article is aimed at properties, condition monitoring, diagnostic, and reclamation aspects of ester filled transformers. The imperative and pertinent standards, including IEEE Std C57.106, IEEE Std C57.104, IEEE Std 637, IEEE Std C57.147, and IEEE Std C57.155 remain the target objectives for potential analysis. It is hoped that this critical analysis will be useful for utility and condition monitoring engineers in understanding several key aspects of ester fluids vis-à-vis traditional insulating fluids. The present analysis might also be useful for researchers and industry interested in alternative dielectric fluids for transformer insulation technology.

Insulation cellulose paper is a basic measure for a power transformer’s remaining useful life, and its advantageous low cost, electrical, and mechanical properties have made it an extensive insulation system when impregnated in a dielectric liquid. Cellulose paper deteriorates as a result of ageing due to some chemical reactions like pyrolysis (heat), hydrolysis (moisture), and oxidation (oxygen) that affects its degree of polymerization. The condition analysis of cellulose paper has been a major concern since the collection of paper samples from an operational power transformer is almost impossible. However, some chemicals generated during cellulose paper deterioration, which were dissolved in dielectric liquid, have been used alternatively for this purpose as they show a direct correlation with the paper’s degree of polymerization. Furthermore, online and non-destructive measurement of the degree of polymerization by optical sensors has been proposed recently but is yet to be available in the market and is yet generally acceptable. In mitigating the magnitude of paper deterioration, some ageing assessments have been proposed. Furthermore, researchers have successfully enhanced the insulating performance of oil-impregnated insulation paper by the addition of various types of nanoparticles. This study reviews the ageing assessment of oil-paper composite insulation and the effect of nanoparticles on tensile strength and electrical properties of oil-impregnated paper insulation. It includes not only significant tutorial elements but also some analyses, which open the door for further research on the topic.

Despite major progress in the design of power transformers, the Achilles’ heel remains the insulation system, which is affected by various parameters including moisture, heat, and vibrations. These important machines require extreme reliability to guarantee electricity distribution to end users. In this contribution, a fiber optic sensor (FOS), consisting of a Fabry–Perot cavity made up of two identical fiber Bragg gratings (FBGs), is proposed, to monitor the temperature and vibration of power transformer windings. A phase shifted gratings recoated sensor, with multilayers of polyimide films, is used to monitor the moisture content in oil. The feasibility is investigated using an experimental laboratory transformer model, especially fabricated for this application. The moisture contents are well correlated with those measured by a Karl Fisher titrator, while the values of temperature compare well with those recorded from thermocouples. It is also shown that the sensors can be used to concurrently detect vibration, as assessed by sensitivity to the loading current. The possibility of dynamically measuring humidity, vibrations, and temperatures right next to the winding, appears to be a new insight that was previously unavailable. This approach, with its triple ability, can help to reduce the required number of sensors and therefore simplify the wiring layout.

The service of mineral insulating oils for power transformer insulation and cooling aspects cannot be disavowed. However, the continued use of mineral oils is questionable due to environmental unfriendliness and the divestment from fossil fuels. This has provoked the quest for green alternative insulating liquids for high-voltage insulation. Natural esters are among the remaining alternatives that are renewable and environmentally friendly. Regardless of their environmental and technical merits, natural esters have some limitations that are slowing down their total acceptance by transformer owners and utilities. Critical limitations and concerns include esters’ pour point, viscosity, oxidative stability, and ionization resistance. In this work, the state of the art of “natural esters for transformers” is explored with the aim of potential improvements. The sections of the article are geared towards technical viewpoints on improving the overall workability and serviceability of natural esters in high-voltage applications. A comprehensive review of the existing literature is achieved, based on performance improvements of the natural ester using “additives” and “chemical modification”. The authors hope that this report may be helpful to transformer owners as well as influence the progression of natural esters for power transformer applications.

Electrical energy is polymorphic, with voltage levels varying between a few volts to MVs and frequencies from a few Hz to MHz [...]

With the growth of the world’s population and faster-developing industries, larger amounts of electric energy are needed [...]

Transformers play an essential role in power networks, ensuring that generated power gets to consumers at the safest voltage level. However, they are prone to insulation failure from ageing, which has fatal and economic consequences if left undetected or unattended. Traditional detection methods are based on scheduled maintenance practices that often involve taking samples from in situ transformers and analysing them in laboratories using several techniques. This conventional method exposes the engineer performing the test to hazards, requires specialised training, and does not guarantee reliable results because samples can be contaminated during collection and transportation. This paper reviews the transformer oil types and some traditional ageing detection methods, including breakdown voltage (BDV), spectroscopy, dissolved gas analysis, total acid number, interfacial tension, and corresponding regulating standards. In addition, a review of sensors, technologies to improve the reliability of online ageing detection, and related online transformer ageing systems is covered in this work. A non-destructive online ageing detection method for in situ transformer oil is a better alternative to the traditional offline detection method. Moreover, when combined with the Internet of Things (IoT) and artificial intelligence, a prescriptive maintenance solution emerges, offering more advantages and robustness than offline preventive maintenance approaches.

Ester-based dielectric fluids have gained widespread popularity for applications in high voltage apparatus. Synthetic and natural esters have been subjected to research for decades vis-à-vis mineral insulating oils around the world. Although many researchers favor the application of ester fluids, utilities are still uncertain and application of these alternatives remains a challenge. The intent of this survey is to present recent research progress and highlight the state of the art of key aspects that should be emphasized in future research. The contemporary research scenarios pertaining to the performance of ester fluids versus mineral oils, miscibility, and retrofilling of insulating fluids are discussed. In addition, pre-breakdown phenomena, usage of esters in on-load tap changers, environmental and fire resistance properties, and use of esters in cold climates are also discussed. Importantly, challenges and future aspects that should be investigated to improve the existing knowledge of ester dielectric fluids for applications in transformer technology are highlighted.

This paper summarizes experimental findings of the regenerative capability of nitrogen on Fuller's earth. Regenerating new oil from aged oil involves several cycles requiring a large volume of Fuller's earth. The use of large amounts of Fuller's earth is not economically profitable for companies that carry out regenerative operations to service transformer oils and can have environmental implications, since Fuller's earth is usually used to reclaim transformer oil in a one-time batch basis then disposed of. To improve the situation, a new technique to reduce the number of regeneration cycles is proposed. It is shown that Fuller's earth can be improved by purging it with continuous flowing of a fluid, preferably dry nitrogen.

In the last decades, a large focus is being placed on the sustainability and safety of the power transformer spectrum. Ester liquids, which have interesting properties such as high fire point and biodegradability, are gaining needed attraction. Since in-service condition, thermal aging deteriorates the physicochemical and electrical properties of liquid dielectrics, it is important to study their long-term behavior. In this contribution, the pre-breakdown and breakdown behavior of ester fluids (synthetic and natural) under AC stress are investigated. Important characteristics, such as partial discharge pre-inception voltage, partial discharge inception voltage, breakdown voltage, average streamer velocity, and inception electric field, were assessed. The influence of the radius of curvature (of high voltage needle electrode) as well as the thermal degradation of typical ester liquids are also discussed. Mineral oil was also included in the tests loop as a benchmark for comparative purposes. It is found that the pre-inception voltage of ester liquids was, in most cases, higher than that of mineral oil. For a given radius of curvature, the streamer inception and breakdown voltages decreased with thermal aging. During the streamer initiation, the electric field at the electrode tip decreased with the increase in the radius of curvature. The velocity of the streamers seems to increase with the decrease in the radius of curvature. The period of vulnerability, the so-called “delay time”, seems to be independent of the aging or the radius of curvature for a given condition of the liquid.

In addition to public health measures, including social distancing, masking, cleaning, surface disinfection, etc., ventilation and air filtration can be a key component of a multi-pronged risk mitigation strategy against COVID-19 transmission indoors. Electrostatic precipitators (ESP) have already proved their high performance in fluid filtration, particularly in industrial applications, to control exhaust gas emissions and remove fine and superfine particles from the flowing gas, using high-voltage electrostatic fields and forces. In this contribution, a high-voltage electrostatic sanitizer (ESS), based on the electrostatic precipitation concept, is proposed as a supportive measure to reduce indoor air infection and prevent the spread of COVID-19 coronavirus. The finite element method (FEM) is used to model and simulate the proposed ESS, taking into account three main mechanisms involving in electrostatic sanitization, namely electrostatic field, airflow, and aerosol charging and tracing, which are mutually coupled to each other and occur simultaneously during the sanitization process. To consider the capability of the designed ESS in capturing superfine particles, functional parameters of the developed ESS, such as air velocity, electric potential, and space charge density, inside the ESS are investigated using the developed FEM model. Simulation results demonstrate the ability of the designed ESS in capturing aerosols containing coronavirus, precipitating suspended viral particles, and trapping them in oppositely charged electrode plates.

The reliability and efficiency of power grids directly contribute to the economic well-being and quality of life of citizens in any country. This reliability depends, among other things, on the power lines that are exposed to different kinds of factors such as lightning, pollution, ice storm, wind, etc. In particular, ice and snow are serious threats in various areas of the world. Under certain conditions, outdoor equipment and hardware may experience various problems: cracking, fatigue, wear, flashover, etc. In actual fact, a variety of countermeasures has been proposed over the past decades and a certain number have been applied by utilities in various countries. This contribution presents the status and current trends of different techniques against atmospheric icing of power lines. A snapshot look at some significant development on this topic over the last four decades is addressed. Engineering problems in utilizing these techniques, their applications, and perspectives are also foreseen. The latest up-to-date review papers on the applications and challenges in terms of PhD thesis, journal articles, conference proceedings, technical reports, and web materials are reported.

In this contribution, a mathematical model allowing for the prediction of the AC surface arc propagation on polluted insulators under non-uniform electric field is proposed. The approach is based on the experimental concept of Claverie and Porcheron. The proposed model, which makes it possible to reproduce the surface electric discharge, includes a condition for arrest of the propagating discharge. The electric field at the tip of the discharge is the key parameter governing its random propagation. A finite element approach allows for mapping of the electric field distribution while the discharge propagation process is simulated in two dimensions. The voltage drop along the arc discharge path at each propagation step is also taken into account. The simulation results are validated against experimental data, taking into account several electro-geometric parameters (distance between electrodes, pollution conductivity, radius of high-voltage electrode, length of the plane electrode). Good agreement between computed and experimental results were obtained for various test configurations.

Frequency response analysis (FRA) is a powerful and widely used tool for condition assessment in power transformers. However, interpretation schemes are still challenging. Studies show that FRA data can be influenced by parameters other than winding deformation, including temperature. In this study, a machine-learning approach with temperature as an input attribute was used to objectively identify faults in FRA traces. To the best knowledge of the authors, this has not been reported in the literature. A single-phase transformer model was specifically designed and fabricated for use as a test object for the study. The model is unique in that it allows the non-destructive interchange of healthy and distorted winding sections and, hence, reproducible and repeatable FRA measurements. FRA measurements taken at temperatures ranging from −40 °C to 40 °C were used first to describe the impact of temperature on FRA traces and then to test the ability of the machine learning algorithms to discriminate between fault conditions and temperature variation. The results show that when temperature is not considered in the training dataset, the algorithm may misclassify healthy measurements, taken at different temperatures, as mechanical or electrical faults. However, once the influence of temperature was considered in the training set, the performance of the classifier as studied was restored. The results indicate the feasibility of using the proposed approach to prevent misclassification based on temperature changes.

Transformers are generally considered to be the costliest assets in a power network. The lifetime of a transformer is mainly attributable to the condition of its solid insulation, which in turn is measured and described according to the degree of polymerization (DP) of the cellulose. Since the determination of the DP index is complex and time-consuming and requires the transformer to be taken out of service, utilities prefer indirect and non-invasive methods of determining the DP based on the byproduct of cellulose aging. This paper analyzes solid insulation degradation by measuring the furan concentration, recently introduced methanol, and dissolved gases like carbon oxides and hydrogen, in the insulating oil. A group of service-aged distribution transformers were selected for practical investigation based on oil samples and different kinds of tests. Based on the maintenance and planning strategy of the power utility and a weighted combination of measured chemical indicators, a neural network was also developed to categorize the state of the transformer in certain classes. The method proved to be able to improve the diagnostic capability of chemical indicators, thus providing power utilities with more reliable maintenance tools and avoiding catastrophic failure of transformers.

This paper proposes time-domain analytical expressions of the instantaneous pulsating torque components in a synchronous machine air gap when supplied by a load-commutated-inverter (LCI) system. The LCI technology is one of the most used variable frequency drives when very high power and low speed are required in applications such as pipeline recompression and decompression, as well as liquefied natural gas compression. In such applications, synchronous motors are used because of their high efficiency resulting from a separated supply of the current to their rotor through the excitation circuit. These applications usually have long and flexible shafts, which are very sensitive to torsional vibration excitation when their natural frequencies interact with any external torque applied to the shaft. A torsional analysis is required by international standards to assess the survivability of the shaft through the overall speed range of the motor. Therefore, the magnitude and frequencies of the motor air-gap torque are needed for such evaluation. The proposed developments are supported by numerical simulations of LCI systems in a large range of operation range. From the simulation results, torque harmonic families are derived and expressed in a parametric form, which confirm the accuracy of the proposed relationships.

Machine learning is widely used as a panacea in many engineering applications including the condition assessment of power transformers. Most statistics attribute the main cause of transformer failure to insulation degradation. Thus, a new, simple, and effective machine-learning approach was proposed to monitor the condition of transformer oils based on some aging indicators. The proposed approach was used to compare the performance of two machine-learning classifiers: J48 decision tree and random forest. The service-aged transformer oils were classified into four groups: the oils that can be maintained in service, the oils that should be reconditioned or filtered, the oils that should be reclaimed, and the oils that must be discarded. From the two algorithms, random forest exhibited a better performance and high accuracy with only a small amount of data. Good performance was achieved through not only the application of the proposed algorithm but also the approach of data preprocessing. Before feeding the classification model, the available data were transformed using the simple k-means method. Subsequently, the obtained data were filtered through correlation-based feature selection (CFsSubset). The resulting features were again retransformed by conducting the principal component analysis and were passed through the CFsSubset filter. The transformation and filtration of the data improved the classification performance of the adopted algorithms, especially random forest. Another advantage of the proposed method is the decrease in the number of the datasets required for the condition assessment of transformer oils, which is valuable for transformer condition monitoring.

The 2018 IEEE International Conference on High Voltage Engineering and Application (ICHVE 2018) was organized by the National Technical University of Athens, Greece and endorsed by the IEEE Dielectrics and Electrical Insulation Society [...]

Among the main causes of outdoor insulation failures is their poor specifications in terms of leakage distances. This happens when the selected criteria are unable to cope with all the stresses imposed by the changes in environmental pollutions. Therefore, it is important for utilities to fully understand the actual pollution characteristics of the service environment in which the insulators are operating. In this paper, the pollution severity and performance of some 13.2 kV ceramic insulators, sampled in different areas of a Canadian aluminum factory, are assessed. The investigations were performed taking into account the influence of air humidity. Various characteristics were investigated to assess the pollution levels of the insulators, such as equivalent salt deposit density (ESDD) and non-soluble deposit density (NSDD), surface resistance, and leakage current characteristics (density, 3rd harmonic amplitude, and phase). It was witnessed that the insulators, collected around the factory, were much more polluted in comparison to the initial expectation. The pollution level should not be considered static due to the environmental parameters’ dynamics. Lessons to learn: the reliability of an electrical grid is dependent on components whose own reliability is strongly affected by external factors, of which there is often a poor awareness. If care is not taken to re-evaluate the post-installation pollution levels of the insulators, the light may simply turn out!

Operational factors are known to affect the health of an in-service power transformer and to reduce the capabilities and readiness for energy transmission and distribution. Hence, it is important to understand the degradation rate and corresponding behavioral aspects of different insulating fluids under various fault conditions. In this article, the behavior of mineral oil and two environmentally friendly fluids (a synthetic and a natural ester) are reported under arcing, partial discharges, and thermal fault conditions. Arcing, partial discharges and thermal faults are simulated by 100 repeated breakdowns, top oil electrical discharge of 9 kV for five hours, and local hotspots respectively by using different laboratory-based setups. Some physicochemical properties along with the gassing tendency of fresh and aged insulating liquids are investigated after the different fault conditions. UV spectroscopy and turbidity measurements are used to report the degradation behavior and dissolved gas analysis is used to understand the gassing tendency. The changes in the degradation rate of oil under the influence of various faults and the corresponding dissolved gasses generated are analyzed. The fault gas generations are diagnosed by Duval’s triangle and pentagon methods for mineral and non-mineral oils. It is inferred that; the gassing tendency of the dielectric fluids evolve with respect to the degradation rate and is dependent on the intensity and type of fault.

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.

Oil/paper insulation degradation in transformers involves chemical and physical changes in the materials. Some of the chemical reactions involve very reactive intermediates called free radicals. Free radicals play a major role in a wide variety of ageing processes. The detection of these reactive species in oil may, in principle, provide useful information for monitoring oil degradation. This manuscript details a laboratory technique, which determines the relative content of free radicals in insulating oils of petroleum origin by a spectrophotometric method. Free radicals may be formed in oils under operating or test conditions. The procedure enables the determination of the relative concentration of free radicals, which can act as the precursors of decay products such as charge carriers, oxidized molecules, as well as polymerization products. The technique involves using a reactive free radical reagent, 2,2-diphenyl-1-picrylhydrazyl (DPPH), added to oil to assess free radical concentration. This method is applicable to new, reclaimed, or used oils as well as naturally or artificially oxidized oil (the cause of aging can be chemical, physical, or electrical). In this contribution, free radicals were assessed following electrical discharge application in oil.

Oxidation of oil/paper insulation initiates premature aging and introduces carboxylic acids with eventual increase in oil acidity, which hampers the properties of the oil. In this paper, a membrane separation technology-based purification process for aged insulation oil has been evaluated and reported. The intent of the present study is to eliminate carboxylic acids, dissolved decay contents and other colloidal contamination present in aged oil and enhance the useful life of oil. The potential of the membrane treatment process has been demonstrated using Ultraviolet Visible Infrared Spectroscopy and Fourier Transform Infrared Spectroscopy diagnostic measurements for oil and membrane. Additionally, membrane retention properties like membrane flux, retention coefficient, sorption time and membrane mass have been analyzed to understand the treatment process. To further evaluate the performance of the membrane and effectiveness of the treatment process, acidity, dielectric dissipation factor, relative permittivity, and resistivity measurements of the oil before and after filtration have been also reported. The proposed membrane purification method has been tested for Algerian utility in-service oil samples. It is inferred that, membrane filtration method is a simple and effective method for treatment of aged oils and aids in extending the remnant life of the oil. The procedure is economically attractive because of increasing prices for transformer liquids, cost effective and environmentally sounds.

Frequency Domain Spectroscopy (FDS) is an effective tool allowing assessing the condition of oil-paper insulation system in power equipment. However, results from these measurements are known to be greatly influenced by various parameters, including insulation aging, moisture content, and insulation geometry/volume, together with environmental condition such as temperature. In this contribution, a series of experiments have been performed under controlled laboratory conditions. The dielectric response of the oil impregnated paper, along with the degree of polymerization and moisture content, were monitored. Since dielectric parameters are geometry dependent, poles (independent of the geometry) which depends on resistivity and permittivity, were considered to assess the condition of the insulation. From the investigations performed on new and aged samples, it is shown that poles (P) can be regarded as insulation aging indicator. It is also shown that a per unit value based on the Dielectric Dissipation Factor (DDF), measured in the frequency range from 1 to 1000 Hz can be correlated to moisture content in the insulation paper.

The reliability of the power grid system directly contributes to the economic well-being and the quality of life of citizens in any country. [...]

This paper presents the development of a new bi-arc dynamic numerical model for predicting AC critical flashover voltage (FOV) of ice-covered extra-high voltage (EHV) insulators. The proposed model is based on a generic calculation algorithm coupled with commercial finite element method software designed to solve the Obenaus/Rizk model. The proposed model allows one to implement the Nottingham and Mayr approaches and compare the results obtained as a function of the arcing distance, the freezing water conductivity, and the initial arc length. The validation of the model demonstrated high accuracy in predicting the FOV of ice-covered post-type insulators and its capability to simulate the interaction of the two partial arcs during the flashover process. In particular, the results showed that the Nottingham approach is sensibly more accurate than the Mayr one, especially in simulating the dynamic behavior of the partial arcs during the flashover process. Based on the encouraging results obtained, a multi-arc calculation algorithm was proposed using the bi-arc dynamic numerical model as a basis. The basic idea, which consists in dividing the multi-arc model in several bi-arc modules, was not implemented and validated but will serve as a promising concept for future work.

This paper investigates the influence of arc velocity and propagation criteria on the parameters of a dynamic numerical mono-arc model used to predict flashover voltage of ice-covered insulators. For that purpose, a generic algorithm has been developed which, coupled with a Finite Element commercial software, permits us to solve the mono-arc Obenaus equation. The versatility of the proposed algorithm allows to implement three different arc propagation criteria and five different arc velocity criteria, as well as to compute the corresponding flashover voltage, arc velocity and leakage current. Moreover, this algorithm permits to propose a new arc velocity criterion based on numerical calculation instead of analytical formulation as proposed in literature.

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Aging of a power transformer’s insulation system produces carboxylic acids. These acids—acetic, formic and levulinic—are absorbed by the paper insulating material, thus accelerating the degradation of the whole insulation system. In this contribution, the effect of these acids on the aging of oil-impregnated paper insulation used in power transformer is reported. A laboratory aging experiment considering different concentrations of these three acids was performed to assess their effect on the insulation system’s degradation. Each acid was individually mixed with virgin oil, and a mixture of acids was also blended with oil. The paper’s degradation was assessed by the degree of polymerization (DPv). It was found that the DPv of paper aged with formic acid decreased much faster in comparison to the other acids.

It is now well-established that moisture in the oil paper insulation used in power and instrument transformers significantly reduces the transformers’ lifetimes, and can eventually lead to premature failure. This moisture should, therefore, always be removed, not only during production but also after repairs. At the final stage of manufacturing, the drying process should be carried out to remove water and air vacuoles contained in the cellulose-based paper before impregnation. Successful drying helps increase the residual life of transformers, because the presence of moisture and air vacuoles accelerates the aging/degradation process of the oil paper insulation. Proper estimation of residual moisture before impregnation and the determination of the time required for drying play key roles in the time-consuming process of drying. In this paper, the disadvantages of inadequate drying are addressed, followed by a mathematical approach to model the paper drying process. A mathematical model describing the kinetics of drying according to temperature, initial moisture, paper weight, final moisture, and extraction rate is proposed. This model also estimated the amount of moisture removed at the end of the drying process.

The condition of the internal cellulosic paper and oil insulation are of concern for the performance of power transformers. Over the years, a number of methods have been developed to diagnose and monitor the degradation/aging of the transformer internal insulation system. Some of this degradation/aging can be assessed from electrical responses. Currently there are a variety of electrical-based diagnostic techniques available for insulation condition monitoring of power transformers. In most cases, the electrical signals being monitored are due to mechanical or electric changes caused by physical changes in resistivity, inductance or capacitance, moisture, contamination or aging by-products in the insulation. This paper presents a description of commonly used and modern electrical-based diagnostic techniques along with their interpretation schemes.

A power transformer outage has a dramatic financial consequence not only for electric power systems utilities but also for interconnected customers. The service reliability of this important asset largely depends upon the condition of the oil-paper insulation. Therefore, by keeping the qualities of oil-paper insulation system in pristine condition, the maintenance planners can reduce the decline rate of internal faults. Accurate diagnostic methods for analyzing the condition of transformers are therefore essential. Currently, there are various electrical and physicochemical diagnostic techniques available for insulation condition monitoring of power transformers. This paper is aimed at the description, analysis and interpretation of modern physicochemical diagnostics techniques for assessing insulation condition in aged transformers. Since fields and laboratory experiences have shown that transformer oil contains about 70% of diagnostic information, the physicochemical analyses of oil samples can therefore be extremely useful in monitoring the condition of power transformers.