With the development of fiber optic sensing technology, optical fiber sensors have been widely used in online monitoring of power transformers. To investigate the influence of aging fiber sheaths on transformer oil, two kinds of special optical fibers with thermoplastic polyester elastomer (TPEE) and poly tetra fluoroethylene (PTFE) as sheaths underwent thermally accelerated aging in transformer oil at 130 °C. The volume resistivity, dielectric dissipation factor (DDF), and breakdown voltage of the oil were measured to indicate insulation strength. The water content and acid value of the oil were measured and fitted to predict the aging tends. The thermal aging characteristics of the oil were quantitatively compared and results showed two kinds of optical fibers could exacerbate all the physical and chemical parameters of oil, and the TPEE sheath had a more significant impact on the oil. The reasons contributing to such phenomenon were analyzed using Fourier transform infrared spectroscopy (FTIR) and scanning electron microscopy (SEM). Both TPEE and PTFE were depolymerized in high-temperature transformer oils, producing water and small molecule hydrocarbon groups. The accumulation of small hydrocarbon groups promoted positive feedback of pyrolysis in the oil. The free hydrogen produced by oil pyrolysis increased the acidity of the oil, which in turn increased the solubility of the water produced by sheath depolymerization. The chain depolymerization of TPEE was more severe than that of PTFE, further exacerbating the deterioration of TPEE-containing oil.

Winding overheating is a common fault in a transformer. To detect the temperature, the most widely used method is a point-type measurement, but traditional measurement methods cannot obtain the whole temperature distribution in a transformer. Taking this into consideration, a new method with which to measure the temperature of transformer windings was proposed. Based on Raman scattering, a new fibre-optic composite winding model was developed. The feasibility of the model was verified by electrical as well as temperature, field simulation and power frequency resistance testing. To assess the practicality and accuracy of the new model, a distributed optical fibre temperature measurement platform was built, and a series of experiments were designed. According to the data collected, the temperature measurement error based on the method could be limited to 1 °C while the positioning accuracy error was within 1 m, which meant that the new approach can satisfy the requirements of transformer winding temperature measurement and locate hot spots in the winding.

The temperature distribution and deformation of the transformer windings cannot be measured in a distributed manner by the traditional method and failure location cannot be performed. To solve these problems, we present a transformer winding temperature and strain based on a distributed optical fibre sensing detection method. The design of the optical fibre winding composite model is developed and simulated winding temperature rise test and local deformation test distinguish between measuring the winding temperature and the strain curve. The test results show that the distributed optical fibre can transmit wire strain efficiently. Optical fibres, in the process of winding, have a certain pre-stress. Using the Brillouin–Raman joint measuring method, one can effectively extract the optical fibre temperature and strain information and measure the length of the winding direction of the temperature and strain distribution curve to a temperature measurement precision of ±2 °C and strain detection accuracy of ±50 με. The system can carry out local hot spot and deformation localisation, providing new ideas for the transformer winding state monitoring technology.

At present, transformer winding strain monitoring is divided mainly into off-line detection and on-line detection. Due to the interference of the complex electromagnetic environment, on-line detection has not been widely used. Although off-line detection is more mature, it can not accurately judge the winding strain form. Based on the above problems, this research investigated a strain gauge strain detection method based on distributed fiber optic sensing, and proposes a winding strain identification method based on the S-transform and an extreme learning machine (ELM). First, the deformation of the winding in the process of transformer operation is simulated, and the corresponding Brillouin frequency shift is collected. Then, the time-frequency analysis of the strain signal is carried out using an S-transform, and the transformed time-frequency feature is extracted as the input sample to the neural network. An ELM was used for training identification. Experimental results show that the method can effectively identify the common winding deformation form, and that the recognition effect is better and the accuracy is high.

The uncertainty of the evaluation information is likely to affect the accuracy of the evaluation, when conducting a health evaluation of a power transformer. A multilevel health assessment method for power transformers is proposed in view of the three aspects of indicator criterion uncertainty, weight uncertainty, and fusion uncertainty. Firstly, indicator selection is conducted through the transformer guidelines and engineering experience to establish a multilevel model of transformers that can reflect the defect type and defect location. Then, a Gaussian cloud model is used to solve the uncertainty of the indicator criterion boundary. Based on association rules, AHP, and variable weights, the processed weights are calculated from the update module to obtain comprehensive weights, which overcomes the uncertainty of the weights. Improved DSmT theory is used for multiple evidence fusion to solve the high conflict and uncertainty problems in the fusion process. Finally, through actual case analysis, the defect type, defect location, and overall state of the transformer of the device are obtained. By comparing with many defect cases in a case-study library, the evaluation accuracy rate is found to reach 96.21, which verifies the practicability and efficiency of the method.

Acetylene detection plays an important role in fault diagnosis of power transformers. However, the available dissolved gas analysis (DGA) techniques have always relied on bulky instruments and are time-consuming. Herein, a high-performance acetylene sensor was fabricated on a microhotplate chip using In₂O₃ as the sensing material. To achieve high sensing response to acetylene, Pd–Ag core-shell nanoparticles were synthesized and used as catalysts. The transmission electron microscopy (TEM) image clearly shows that the Ag shell is deposited on one face of the cubic Pd nanoseeds. By loading the Pd–Ag bimetallic catalyst onto the surface of In₂O₃ sensing material, the acetylene sensor has been fabricated for acetylene detection. Due to the high catalytic performance of Pd–Ag bimetallic nanoparticles, the microhotplate sensor has a high response to acetylene gas, with a limit of detection (LOD) of 10 ppb. In addition to high sensitivity, the fabricated microhotplate sensor exhibits satisfactory selectivity, good repeatability, and fast response to acetylene. The high performance of the microhotplate sensor for acetylene gas indicates the application potential of trace acetylene detection in power transformer fault diagnosis.