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The stable operation of transformers is an important guarantee of power system security. The temperature distribution of main insulation is different in long‐term operation. The temperature difference between the local hottest spot temperature and the average oil temperature in the transformer exceeds 30 K. Therefore, the oil‐paper insulation inside the transformer will exhibit non‐uniform ageing after long‐term operation. At present, frequency domain dielectric response (FDS) technology is widely used in transformer ageing assessment. However, the existing assessment methods are only used for overall insulation ageing assessment. The average moisture content and polymerization degree of the overall insulation between each phase winding are obtained. But it still cannot be used to assess the non‐uniform ageing of the transformer's main insulation. The radial, axial, and two directions non‐uniform ageing models have been made in the laboratory in this study. The FDS and physicochemical properties of non‐uniform ageing models were tested. The variation law of FDS for non‐uniform ageing models was analysed. Meanwhile, the dielectric relaxation model of non‐uniform ageing for main insulation was established. The method of frequency division iterative calculation was proposed. Combined with the Fessler moisture balance formula of ageing oil‐paper insulation, the quantitative assessment method for ageing parameters of the non‐uniform ageing model can be realized. Compared the calculated results with the chemical analysis results, the relative error of the polymerization degree calculation is less than 4%, and the error of the moisture content calculation is less than 0.3%. The calculation results can meet the needs of the engineering apply. Therefore, this method can realize the quantitative assessment of transformer main insulation ageing, which is of great significance to assess the transformer insulation state. [ABSTRACT FROM AUTHOR]

This study reports experimental study of cellulose performance in mixture of mineral and synthetic ester (SE) oils. Solid insulation impregnated with mineral oil (MO) and mixture oil has been subjected to accelerated thermal stresses to understand the deterioration rate of cellulose. Diffusion of oil–moisture mixture into paper and corresponding variation in dielectric strength of paper has been examined. Compatibility of solid insulation with MO and mixture oil is analysed by thermal expansion coefficients, X-ray diffraction analysis and dissolved decay products. Furthermore, chemical functional groups present in aged oils are identified by Fourier transform infrared spectroscopy analysis. It is observed that the performance of cellulose insulation is improved with addition of SE to MO. This is achieved by higher water solubility of esters, which enables higher moisture consumption during hydrolysis and keeps cellulose dry. [ABSTRACT FROM AUTHOR]

Transformer oil is widely used in high‐voltage transformers as a cooling and insulation medium. Electrical and thermal stresses expose transformer oil to degradation, which may produce dissolved decay products (DDPs) that are partially adsorbed on the insulation paper and accelerate the degradation of oil and paper. An evanescent field D‐shaped plastic optical fibre (POF) sensor was fabricated using the side‐polishing technique to detect DDPs in transformer oil. A laser light source and power meter were used in the prototype to measure the output power of the POF sensor. The measurement of DDPs was conducted on seven samples of insulating oil collected from the field. The sensor performance was evaluated based on different sensing areas, and the sensor sensitivity was measured. Results show an excellent correlation between the output power and DDPs. A correlation analysis was conducted amongst sensor output power, transformer oil refractive index and area under the absorbance curve. The performance of the sensor demonstrates improved linearity and sensitivity of 97.49% and 26.6779 µW/RIU, respectively. The POF sensor is easily fabricated, low cost and can be used for real‐time monitoring of high‐voltage transformers. [ABSTRACT FROM AUTHOR]

The general method for identifying the partial discharge type in a power transformer is based on their fingerprints in the form of phase-resolved discharge patterns. In the case of multiple defects, traditional clustering methods can be applied for separation of active sources. However, such an approach is impractical for online real-time monitoring due to the very large data size. In this paper a new method using stream clustering is introduced. The method separates the active sources by processing the signal once it is captured, then only a synopsis of the discharge data is stored. Two stream clustering algorithms: Density Grids and DenStream are employed. Through measurements obtained from laboratory experimental setups (corona, surface discharge, transformer defect model) performance of the proposed algorithms are evaluated. It is shown that stream clustering method is able to separate the constituent components involved in the stream of a multi-source discharge signal without the need to store a large amount of information. The performance of the Density Grids method depends on a limited number of features that it can accommodate. In comparison, the DenStream method can capture more features which enable better separation of active sources at the expense of longer processing time. [ABSTRACT FROM AUTHOR]

The impulse voltage wave invading a transformer is not the double exponential impulse wave (DEIW) specified by the IEC but a non-standard waveform with oscillation attenuation characteristics, and this fact leads to doubts about the scientificity and optimality of insulation coordination. A key solution for this issue is the establishment of an equivalent relationship between the measured non-standard impulse voltage wave and the DEIW. In this study, a test platform of multiparameter impulse voltage was set up. Then, the authors obtained the 50% breakdown voltage (U50%) of the oil-impregnated paper (OIP) for transformer under the bipolar oscillatory attenuated impulse wave (BOAIW) and the DEIW with different waveform parameters. The effects of waveform parameters on the breakdown characteristics of the OIP were finally revealed. On the basis of experimental data and several mathematical methods, the authors established the U50% prediction mathematical models for the OIP under the BOAIW and DEIW. Based on the prediction models and the microfluctuation characteristics of measured overvoltage's waveform parameters, the authors proposed a method for evaluating the waveform equivalence. The characteristics of voltage amplitude and change rate were regarded as the two key factors for the waveform equivalence of this method. [ABSTRACT FROM AUTHOR]

An incipient short-circuit fault does not cause a severe condition in the steady-state operation of transformers, but in transient conditions -- the lightning or switching -- it can result in an extended turn-to-turn short-circuit fault (TSCF) and eventually in transformer outage. This study aims to determine and localise small TSCFs using the frequency response analysis. For this purpose, an appropriate mathematical model is developed, which can accurately simulate the winding transient behaviour. Using this model, turn-to-turn short-circuit patterns are extracted for different turn-to-turn faults in the winding; then, the fault location is determined by comparing the measured faulty signal with mathematical short-circuit patterns. In this study, a heuristic index is proposed to identify small TSCF locations. The presented solutions are implemented on a real winding with a nominal rating of 1600 kVA and a nominal voltage of 20/0.4 kV. [ABSTRACT FROM AUTHOR]

Ultra-high-frequency (UHF) method is an effective approach to power transformer partial discharge (PD) detection. The feature parameters extracted from UHF PD signal can be applied to insulation defect type recognition. In this study, a novel feature parameters extraction method based on texture features in time-frequency (TF) representation is proposed. PD detections of four typical insulation defects were performed on a 110 kV oil-immersed power transformer. Time-domain waveform and corresponding TF representations or images of UHF PD signals were obtained. About 36 texture features were extracted from the grey-level co-occurrence matrix of TF images. The texture features were reduced into six new feature parameters by principal component analysis. These feature parameters were used as input of the support vector machine classifier for defect type recognition. The recognition accuracies of four kinds of typical defects reached 97.67, 97.00 97.67 and 98.33% proving that the proposed extracted feature parameters are suitable for insulation defect type recognition in power transformer. [ABSTRACT FROM AUTHOR]

An accurate estimation of moisture in oil-impregnated cellulose of a transformer is difficult due to the complex moisture dynamics inside the transformer, which is highly influenced by the temperature and dimension of the transformer. In this study, a novel particle tracing method is proposed for estimating the moisture in transformers. Different from the conventional approaches using Fick's diffusion law, the proposed method formulates moisture diffusion from a microscopic view of water particles' motion. Especially, the transmission probability of water particles is employed to correlate the microscopic particles' motion with the macroscopic moisture distribution. The proposed particle tracing method is applied to model moisture dynamics in both pressboard specimens and a prototype transformer. Extensive moisture diffusion and ageing experiments are carried out to verify the proposed method. [ABSTRACT FROM AUTHOR]

Frequency domain dielectric spectroscopy (FDS) is a non-destructive method widely used to evaluate the state of dielectric materials. Traditional FDS measurement is conducted in frequency sweep mode between 1 kHz and 1 mHz. However, this method requires long measurement time when sweeping in low frequencies (1 mHz-1 Hz), thereby limiting its field applications. Hammerstein model is selected as the mathematical model for insulation systems according to known property of dielectric and established based on system identification theory. An equivalent mathematical model fitting >97% with the insulation system is obtained. The equivalent model can be used to calculate dielectric response parameters and construct an FDS. The proposed method requires 10 min to obtain dielectric response parameters under any frequency excitation in the lowfrequency section. The measurement time decreases by up to 90% compared with that of traditional FDS. Comparison experiments are conducted between system identification method and traditional FDS measurement applied to different insulation systems, such as insulation pressboards, testing transformers, and field power transformers. Results show that frequency responses are consistent between the equivalent model and insulation system, ensuring the accuracy of FDS curve obtained by the proposed method. [ABSTRACT FROM AUTHOR]

The characteristics of coil are obtained through the following measurement: the root mean square (RMS) coil current, as a function of RMS coil voltage, and the coil loss, as a function of RMS coil voltage. These values were then transformed, using Dommel's method, into instantaneous characteristics of non-linear coil, in a form of a non-linear inductance, representing saturation effect; in parallel with the non-linear resistance representing coil loss. The instantaneous characteristics are composed of peak values of resistance current and voltage, and inductance current and flux. The uncertainties of the characteristics are defined through the uncertainty of the peak values. The uncertainty of instantaneous characteristics is calculated on an example of a coil realised as unloaded iron-cored toroid transformer with a strip-wounded core made of oriented transformer sheets (M5-type) using the adaptive Monte-Carlo method (MCM) and uncertainty framework of ‘Guide to the expression of uncertainty in measurement’ (GUM). Results reveal the peculiarities of both methods and the impact of the uncertainty of measured values on the uncertainty of instantaneous characteristics. [ABSTRACT FROM AUTHOR]

Digital measurement equipment such as electronic transformers enable real‐time functional control and monitoring systems to be built in power grids to accommodate the large‐scale access of distributed renewable energy to the grid. However, the current offline calibration of electronic transformers is difficult to implement; therefore, an online calibration method for electronic transformers must be developed to ensure their accuracy. The design of a clamp‐on two‐stage current transformer (CTS‐CT) with a passive compensation loop is presented herein. The compensation loop is utilised to reduce both the amplitude and phase errors, thereby achieving high accuracy. The formulas for calculating the resistance and capacitance of the passive compensation loop are presented. The expanded uncertainties (95% confidence level) of the proposed CTS‐CT are estimated to be lower 0.03% for the amplitude error and 1′ for the phase error. Experimental results indicate that the proposed CTS‐CT prototype is not affected by the external magnetic field nor the opening and closing times, thereby enabling good‐quality online calibration for field applications. Moreover, the proposed CTS‐CT adopts neither an active compensatory circuit nor software compensation; therefore, the manufacturing of the CTS‐CT is steady and simple, and the calibration procedures are simplified. [ABSTRACT FROM AUTHOR]

For insulating liquids in power transformers, the development of low viscous vegetable oil based liquids has been proposed in this work. The viscosity reduction of vegetable oil based liquids has been achieved by processing of the natural esters with different levels of concentration of aromatic solvent of benzyl benzoate. Two categories of vegetable oil based liquids, namely edible such as sunflower oil, palm oil and sesame oil, and non edible such as honge oil, neem oil and punna oil are considered for this investigation. The analysis is also extended to investigate the influence of benzyl benzoate on other critical properties of vegetable oil based liquids such as breakdown voltage, flash point, moisture content, acidity, and density. From the experimental results, it is revealed that inclusion of benzyl benzoate has shown considerable reduction in viscosity of vegetable oil based liquids. Also based on properties of processed natural esters, results have given away the positive encouragement towards the development of low viscous insulating liquids with combined benzyl benzoate and natural esters for application in high voltage power transformers. [ABSTRACT FROM AUTHOR]

The DC conductivity of insulating oil is key to converter transformers, in which AC and DC electrical fields simultaneously exist. Research on the influence of the electric field on oil conductivity can optimise the insulation design of converter transformers. Although numerous studies have been conducted on the conductance mechanism of non‐polar liquids, the explicit mechanism and properties of oil conductivity under a high electrical field remain unclear. This study developed an optimised conductance model considering charge carrier elimination, dissociation, and charge injection process. Thereafter, the authors chose three types of oils for converter transformers and measured the variations in oil conductivity and ion mobility under different electric fields with 0–10 kV/mm range. The result indicates that ion mobility will increase under a high electric field owing to the EHD effects. The conductivity of the insulating oil follows a U‐shaped pattern with an increasing electric field, thereby generally matching the optimised theoretical model. Therefore, the decrease in conductivity is mainly the result of the elimination process of free ions, whereas the increase is the result of the injection process that occurs on the electrodes. [ABSTRACT FROM AUTHOR]

The sweep frequency response analysis (SFRA) technique is based on measuring the power transformer electrical transfer functions over a wide frequency range to identify the abnormal areas prior to serious failure. This study presents the proposed diagnostic adjusted R‐square technique to identify the transformer faults using the SFRA technique. The SFRA measurements are ranged up to and beyond 1 MHz. The proposed technique is used to calculate the relative factor for adjusted R‐square coefficient between the actual data and new measurement data sets. The results of the proposed method are evaluated and compared with existing Chinese Standard factor DL/T 911‐2016 and CCF factor of IEEE Std. C57.149™ Standard indicates that the suggested methodology is more reliable to identify and analyse the internal faults of the transformers. This technique is considered to be effective indicator to predict the internal physical condition inside the transformer, which moves the field forward. [ABSTRACT FROM AUTHOR]

Partial discharge (PD)-based diagnosis is extensively employed in condition assessment of electrical equipment. In the case of multiple PD sources, discrimination of mixed signals is significant for reliable PD interpretation. To improve the separation performance of three or more PD sources, a multi-step discrimination method is proposed. The cumulative energy functions are exploited to characterise wave shapes of PD signals, and width and sharpness features are extracted and classified to separate mixed patterns. With the objective of maximising a novel evaluation parameter of separation capability, the oblique line and length of structure element are optimised in the feature extraction stage. For the multi-step discrimination method, the feature extraction and clustering procedures are repeatedly applied to the whole dataset, sub-classes, sub-subclasses etc., until no more clusters are generated. To evaluate the separation performance of the proposed algorithm, a mathematical model for PD pulse is proposed, which is the multiplication of Heidler enveloping function and an oscillating function whose frequency spectrum confirms Gaussian distribution. In the end, the multi-step discrimination method is tested with PD current pulses and ultra-high-frequency signals of three artificial defects in transformer and gas-insulated system, and the results prove the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]