Results of Frequency Domain Spectroscopy measurements are known to be largely influenced by environmental conditions, such as the temperature. Because field measurements, last hours after de-energizing the transformer, the ambient temperature may affect the results. Especially in cold regions of the world, extreme care are required to interpret the results when performing tests at relatively low surrounding temperatures. A better understanding and analysis of the dielectric test results are therefore only possible with a clear understanding of the physical behavior of the insulation system in response to the ambient conditions. In this contribution, the dielectric behavior of a composite oil paper insulation system has been explained from the properties of Debye basic model. A series of experiments have been performed under controlled laboratory conditions with preset moisture content inside the insulation. The equivalent circuit parameters of a laboratory made oil paper condenser bushing model were obtained using a non-linear optimization procedure. Since the dielectric parameters values are geometry dependent, poles, calculated from resistances and capacitances, were used as they are independent of the geometry. It was shown that the poles can be regarded as parameters able to be used for insulation condition assessment.

In the last two decades, some new methods, based on the characterization of the dielectric response of transformer insulation in both time and frequency domains are being used by power utilities for assessment of the condition of power equipments. From fields and laboratory investigations, these techniques were found to be highly operating conditions dependant. During normal operating conditions, the temperature inside a transformer is much higher than ambient, depending upon the operating condition. Because field measurements last hours after switching off the transformer, the final temperature may be much lower than the initial. Thus at onsite measurements the water migration is commonly running, the transformer is in a non equilibrium state. This transient can lead to mistaken interpretation of insulation condition. In the current research work, a systematic investigation on the influence of thermal transients on the results of polarization and depolarization current measurements is presented. A series of experiments have been performed under controlled temperature conditions on an oil impregnated bushing model. Precautions that can be taken to minimize thermal transient effects are discussed.

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.

Considered as the heart of electrical power transmission and distribution networks, power transformers are essential part of the electricity transmission grid. Among the condition monitoring and fault diagnosis tools for these machines, dissolved gas analysis (DGA) has proven its effectiveness in their early detection and classification of faults. Up to date, many methods have been proposed in the literature for the interpretation of DGA data, classified into traditional and intelligent methods. This paper proposes a two-steps hybrid method, which uses the strengths of both methods. The approach uses the evolutionary k-means clustering algorithm based on the genetic algorithm for subset formation and subset analysis by human expertise. In the diagnostic procedure, to determine the condition of a sample, the subset to which it belongs is first identified and then the corresponding diagnostic sub-model is applied. The proposed method has been implemented with 595 DGA data, tested on 254 DGA data and validated on the International Electrotechnical Commission (IEC) TC10 database. Their performances were evaluated and compared with existing traditional, intelligent and hybrid methods. From the results obtained with the IEC TC10 database, the newly proposed approach depicts the best overall diagnosis accuracies. Indeed, the best performance is achieved with the proposed method compared to other models in the literature, with diagnostic accuracy of 98.29% compared to 88.89% of the Gouda triangle method, to 88.03% of the Hyosun Corporation gas ratio method or to 86.32% of the three ratios technique.

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.

This research is concerned with the analysis of leakage current, LC, of a porcelain standard station insulator during a glaze ice accretion. The results obtained show that the leakage current during ice accumulation goes through two distinct periods characterized by specific LC waveforms and frequency harmonics. Laboratory tests demonstrated that the duration of the first period is directly dependent on the icing rate. Hence, measuring the duration of the first period is equivalent to measuring the icing rate. However, this requires the determination of either the transition between the two periods or the detection of the onset of the ice accumulation. The results show that icing rate measurement can be achieved by using results of leakage current analysis, like the time evolution of the third and fifth harmonic, as well as the phase angle difference between leakage current and applied voltage. This analysis makes it possible to characterize the severity of an ice accumulation on a porcelain station insulator.