Ester based dielectric fluids have gained wide popularity for applications in high voltage apparatus. Synthetic and natural esters have been subjected to research for decades vis-à-vis mineral insulating oils across the globe. Despite many researchers are affirmative towards the application of ester fluids, utilities are still under a dilemma and application of these alternatives is still a contemporary challenge. The intent of this survey is to present the recent research progress and highlights the state of the art for the key aspects that need to be emphasized in future research. The contemporary research scenario pertaining to performance of ester fluids, miscibility, and retro filling of insulating fluids vis-à-vis mineral oils are discussed. Furthermore, the topic of pre-breakdown phenomena of ester fluids, usage of esters on load tap changers, environmental and fire properties, use of the esters in cold climatic regions are also discussed. Importantly, challenges and future aspects that are to be investigated to improve the existing knowledge of ester dielectric fluids for applications in transformer technology are emphasized.

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.

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.

Dissolved gas analysis (DGA) of insulating oils is one of the most popular methods to detect incipient faults in power transformers. However, appropriate feature selection is crucial for accurately detecting incipient faults using DGA data. Another issue is the unavailability of a balanced DGA dataset, which can hamper the fault classification accuracy. Considering these two issues, this article proposes a novel and accurate fault classification framework using gas ratios as features obtained from the DGA data of power transformers. The obtained unbalanced DGA data was initially balanced using the synthetic minority oversampling technique (SMOTE) in the data pre-processing stage. Following this, an efficient feature selection algorithm, namely, recursive feature elimination (RFE) was used to select the best possible features prior to the fault classification using three benchmark machine learning (ML) classifiers, namely, ${k}$ -nearest neighbor (KNN), multiclass support vector machines (SVMs), and extreme gradient boost (XGBoost). The proposed classification model was tested on the DGA data obtained from the local power utility and on the benchmark IEC TC-10 database. Investigations revealed that the proposed classification model delivered detection accuracy of 98.84% and 97.43%, respectively. The proposed method may be reliably used to diagnose incipient faults in power transformers.

In this article, a robust three-phase grid synchronization technique has been proposed for rapid detection of fundamental frequency, phase, and amplitude. The widely accepted phase locked-loop (PLL) algorithms possess complex architectures and require tedious tuning process for attaining a good stability margin. In order to surpass the shortfalls of PLL algorithms, a computationally efficient, stable, and open-loop scheme has been reported in this article. A novel two consecutive samples based frequency estimator is developed for fast detection of the fundamental frequency. Moreover, an efficient hybrid prefiltering approach is implemented based on the demodulation of the grid voltage signal. Additionally, the combination of a delayed signal cancellation operator and a band-pass filter allowed rapid rejection of dc-offset and harmonics, respectively. In the event of a grid voltage imbalance, the instantaneous symmetrical component method is a rescuer for the rejection of the fundamental negative sequence component without any delay. Subsequently, overall transient response time of the scheme is observed to be improved. On the other hand, the fundamental positive sequence component facilitates the estimation of amplitude and phase angle information. Importantly, the dynamic performance of the proposed scheme has been experimentally validated in presence of various grid disturbances.

The present work is focused on the oxidation aspects (focused on gelling) of natural esters and its influence on the performance of transformer insulation systems. Even though natural esters are not suitable for breathing units, the scenario considered simulates a defect in the sealing system and thus bringing the liquid in direct contact with atmospheric air. Commercially available natural ester liquids are thermally degraded under open beaker conditions in the presence of cellulose with a controlled aging history. The accelerated aging conditions are maintained until the formation of sol and gel is evident in the bulk of the liquid. Later, the insulating liquids and insulating papers are analyzed to understand the behavioral impact of sol and gel on the insulation. The discussions in this article are supported by the physicochemical and electrical characterizations of the degraded liquids and papers. The characterizations include Fourier transform infrared spectroscopy (FTIR), ac breakdown voltage (BDV) failure probability rate, X-ray diffraction (XRD), degree of polymerization (DP), and viscosity. It is found that before the formation of gelling, sol is witnessed in the bulk of the liquid. The presence of the sol highly influences the viscosity of the liquid and aids in rapid acceleration of the oxidation process. It is understood that that the gelling phenomena in natural esters do not have a noticeable impact on the liquid BDV failure rate and paper degradation.