Concentration In Transformer Oil Research Articles

Influence of Operational Defects and Hotspot Temperature on Methanol Concentration in Transformer Oil

This paper investigated the variation law of methanol concentration in transformer oil caused by operational defects and hotspot temperature from 500 kV field transformers. The correlations between various operational defects and methanol in oil are explained in detail for the first time. It is observed that three types of operational defects such as bushing heating, fan failure and breather failure can lead to the increase of methanol concentration in oil. Furthermore, the associations of annual load factor and hotspot temperature with methanol are investigated. Besides, various hotspot temperatures of nine transformers are calculated by the temperature rise guide. The results show that the methanol concentration in oil at the bottom of the winding can indicate the hotspot temperature of the winding. To verify this view, the correlation between methanol concentration and hotspot temperature is also proved by the implementation of accelerated non-uniform thermal aging experiments. In addition, the methanol concentration is found to be unevenly distributed along the axial height of the winding. This work not only provides a novel direction for the maintenance of transformer components, but also gives a further support for the distribution mechanism of methanol in insulating oil.

Prediction of dissolved gas concentration in transformer oil considering data loss scenarios in power system

The trend prediction of dissolved gas concentration in transformer oil can provide basis for transformer fault diagnosis, which is of vital significance to the safe operation of power system. However, due to the inevitable malfunction of monitoring equipment, it is difficult to collect all needed data in actual operation scenarios. Therefore, a method for predicting dissolved gas concentration in transformer oil for data loss scenarios is proposed based on Bayesian probabilistic matrix factorization (BPMF) and gated recurrent unit (GRU) neural network. Firstly, aiming at the problem of data loss in actual monitoring of dissolved gas in oil, BPMF is used to fill in the missing data. Then, a GRU neural network model is established to predict the trend of dissolved gas concentration in oil. Finally, the hyperparameters of the prediction model are selected and optimized by Bayesian theory. The examples show that this method can effectively fill in the missing part of the measured data. Compared with traditional prediction methods, the proposed method has higher prediction accuracy.

Monitoring and Condition Assessment of Insulation Paper of Distribution Transformers With Novel Oil Spectroscopy Method

Measuring the furan concentration in transformer oil is one of the most common methods of monitoring and evaluating insulation paper and estimating its degree of polymerization (DP). Here, methods for measuring furan concentration, especially ultraviolet-visible (UV-Vis) spectroscopy method, are reviewed on the old transformers oil. The results show that the excessive turbidity of old oils and noise at low wavelengths are the main disadvantages of this method, which reduce the measurements accuracy. The following solutions are proposed to overcome these disadvantages: 1) diluting insulation oil with hexane to reduce absorption intensity and 2) using average absorption at a given wavelength to reduce noise impact. These solutions are applied to the oil samples of ten old distribution transformer. It is observed that the correlation between 2-furaldehyde (2-FAL) concentration and average absorption intensity in 190–600 nm wavelength range is equal to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${R}^{{2}} =0.98$ </tex-math></inline-formula> . A mathematical equation for calculating 2-FAL concentration based on average absorption is presented. It has been shown that to reduce the test time, a 190–400 nm range is the optimal measurement wavelength range. The results show that the average difference between the values of DP in the proposed method and the direct measurement method is equal to 77.5.

Prediction of the dissolved gas concentration in power transformer oil based on SARIMA model

As an important equipment in the power system, the operating state of the power transformers is related to the safe operation of the entire system. It is of great importance to determine the transformer state and take countermeasures in advance. Dissolved gas analysis (DGA) is one of the most common methods for state evaluation of the oil-immersed transformer. Therefore, a time series forecasting model based on Seasonal Autoregressive Integrated Moving Average (SARIMA) model is presented to forecast the dissolved gas concentration in transformer oil in this paper. Firstly, the influence of the period parameter on the prediction effect and the correlation between the ambient temperature and the gas concentration are analyzed. Then, comparing the prediction results of the autoregressive (AR) model, the long short-term memory (LSTM) model and the SARIMA model on the trend component of the gas, it is found that the SARIMA model has the most accurate and stable performance. And the accuracy of the prediction results is higher when the external factors related to the prediction data are considered in the SARIMA model. Finally, the reliability of the SARIMA model for the recent prediction on actual data is verified. The case studies show that the SARIMA model can be the suitable method for predicting the future trend of the dissolved gas concentration in power transformer oil.

Portable NIR Spectroscopy Measuring Device for Transformer Oil DBPC Inhibitor Analysis

Monitoring the insulation system of power transformers is crucial in ensuring power transformers are operating in optimal conditions. Given the nondirect and destructive nature of conventional techniques, optical techniques have prevailed to be an effective alternative to monitor the condition of the insulation system. The recent development of small and compact optical components has also contributed to the utilization of optical techniques in onsite testing and online monitoring application. Therefore, this work proposed the measurement of inhibitor concentration in transformer oil by using a portable optical measuring device that utilizes near-infrared spectroscopy. The fundamental studies of measuring inhibitor in transformer oil by using a newly discovered wavelength at 1403 nm were validated, and its adaptation to a portable optical measuring device was discussed. A new mathematical model was formulated to estimate the inhibitor concentration in transformer oil samples on the basis of data obtained from the photodetector. Measurements and the conventional technique were conducted for performance comparison and prototype verification. Results showed a root-mean-square-error of 0.01708 and an average error of 2.11%. This proposed prototype opens up opportunities in onsite testing and online monitoring of inhibitor content concentration in transformer oil.

Prediction Model for Dissolved Gas Concentration in Transformer Oil Based on Modified Grey Wolf Optimizer and LSSVM with Grey Relational Analysis and Empirical Mode Decomposition

Oil-immersed transformer is one of the most important components in the power system. The dissolved gas concentration prediction in oil is vital for early incipient fault detection of transformer. In this paper, a model for predicting the dissolved gas concentration in power transformer based on the modified grey wolf optimizer and least squares support vector machine (MGWO-LSSVM) with grey relational analysis (GRA) and empirical mode decomposition (EMD) is proposed, in which the influence of transformer load, oil temperature and ambient temperature on gas concentration is taken into consideration. Firstly, GRA is used to analyze the correlation between dissolved gas concentration and transformer load, oil temperature and ambient temperature, and the optimal feature set affecting gas concentration is extracted and selected as the input of the prediction model. Then, EMD is used to decompose the non-stationary series data of dissolved gas concentration into stationary subsequences with different scales. Finally, the MGWO-LSSVM is used to predict each subsequence, and the prediction values of all subsequences are combined to get the final result. DGA samples from two transformers are used to verify the proposed method, which shows high prediction accuracy, stronger generalization ability and robustness by comparing with LSSVM, particle swarm optimization (PSO)-LSSVM, GWO-LSSVM, MGWO-LSSVM, EMD-PSO-LSSVM, EMD-GWO-LSSVM, EMD-MGWO-LSSVM, GRA-EMD-PSO-LSSVM and GRA-EMD-GWO-LSSVM.

Determination of the true water content, the nature of the impurities and their concentration in transformer oil

During the operation of transformers, the insulating oil is aging, therefore, there is the deterioration of its insulating properties, which is determined by multiple physico-chemical processes. The most dangerous factor, from the point of view of preserving the operational reliability and resource of a power transformer, is the moisture content of the insulation. Most methods for determining moisture content have measurement errors. These errors are largely dependent on impurities that are contained in the oil. The modified Fisher method proposed in the article allows determining the amount of impurities in the oil and the absolute moisture content. Conducted research allows also to determine the composition of impurities, to reveal the relationship between impurities and some parameters of transformer oil.

An MIP-Based Novel Capacitive Sensor to Detect 2-FAL Concentration in Transformer Oil

A novel capacitive sensor, which can detect 2-Furfuraldehyde (2-FAL, C5H4O2, in ppm order) in transformer oil, is developed. No such sensor is reported earlier in the literature. The 2-FAL is released in transformer oil with the ageing of the transformer and is a promising indicator for remaining useful life of the solid insulation. The sensor is fabricated by coating a molecularly imprinted polymer (MIP) made from polydimethylsiloxane (PDMS) on a copper electrode. The MIP coating has the template of 2-FAL molecules. On testing, the 2-FAL molecules in transformer oil are adsorbed on the templates, which results in a change in capacitance of the sensor. The sensor is tested in the range of 1–20 ppm for measuring 2-FAL concentration and shows an average sensitivity of 0.084 pF/ppm. The sensors are fabricated in different months (batches), and average capacitance values for different oil sample are presented. The sensor reliability is analysed using statistical analysis and repeatability study that shows repeatable results consistently.

Prediction Method for Power Transformer Running State Based on LSTM_DBN Network

It is of great significance to accurately get the running state of power transformers and timely detect the existence of potential transformer faults. This paper presents a prediction method of transformer running state based on LSTM_DBN network. Firstly, based on the trend of gas concentration in transformer oil, a long short-term memory (LSTM) model is established to predict the future characteristic gas concentration. Then, the accuracy and influencing factors of the LSTM model are analyzed with examples. The deep belief network (DBN) model is used to establish the transformer operation using the information in the transformer fault case library. The accuracy of state classification is higher than the support vector machine (SVM) and back-propagation neural network (BPNN). Finally, combined with the actual transformer data collected from the State Grid Corporation of China, the LSTM_DBN model is used to predict the transformer state. The results show that the method has higher prediction accuracy and can analyze potential faults.

Silver nano-bulks surface-enhanced Raman spectroscopy used as rapid in-situ method for detection of furfural concentration in transformer oil

Furfural concentration is an important indicator for the aging diagnosis of internal paper insulation in power transformers. Surface-enhanced Raman scattering (SERS) could directly perform rapid in-situ detection without complex preprocessing steps, which makes SERS suitable for online monitoring. The application of SERS in the detection of furfural concentration in transformer oil was investigated. First, sliver nano-bulks (AgNBs) were synthesized successfully on the copper foil surface by the galvanic displacement. The electric field distributions of AgNBs were analyzed theoretically using the COMSOL software, which indicated that the AgNBs were suitable for SERS application. Then, AgNBs were used as the SERS substrate to detect standard transformer oil samples with different furfural concentrations. As a result, the Raman spectral peak at 1571 cm−1 was selected as the characteristic SERS spectral peak of furfural dissolved in transformer oil for qualitative and quantitative analyses. Subsequently, the Raman spectral peak ratio I1571/I1597 was used as the furfural function in oil to construct the quantitative analysis model using the least squares method. Results showed that SERS method could be used to analyze the furfural dissolved in transformer oil quantitatively and effectively. With further improvements of the detection limit, the SERS method will provide a useful approach to achieving rapid in-situ analysis for furfural dissolved in transformer oil.

Cleaning Method for Status Monitoring Data of Power Equipment Based on Stacked Denoising Autoencoders

Currently, the cleaning process for power equipment monitoring data is cumbersome and often leads to loss of information. To address these problems, a data cleaning method based on stacked denoising autoencoder (SDAE) networks is proposed in this paper. SDAE networks have a strong ability to denoise and restore corrupted data and have a strong feature extraction capability. The status monitoring data of equipment under normal conditions are trained by SDAE to obtain the cleaning parameters and the reconstruction errors. An upper threshold of the reconstruction errors obtained from training samples is determined through Kernel density estimation. A tolerance window width is added to achieve rapid anomaly detection. The abnormal data are classified as outliers, missing data, or fault status data according to the relationship between the reconstruction error and the threshold and between the duration of abnormal data and the tolerance window. To verify the effectiveness of the proposed method, the SDAE model is used to process the data for the dissolved gas concentration in transformer oil and the temperature of the transmission line. The results show that the proposed method can effectively identify and repair outliers and missing information. The model can perform rapid anomaly detection when the equipment is running abnormally.

Photonic crystal fiber modal interferometer with Pd/WO3 coating for real-time monitoring of dissolved hydrogen concentration in transformer oil.

A highly-sensitive and temperature-robust photonic crystal fiber (PCF) modal interferometer coated with Pd/WO3 film was fabricated and studied, aiming for real-time monitoring of dissolved hydrogen concentration in transformer oil. The sensor probe was fabricated by splicing two segments of a single mode fiber (SMF) with both ends of the PCF. Since the collapse of air holes in the PCF in the interfaces between SMF and PCF, a SMF-PCF-SMF interferometer structure was formed. The Pd/WO3 film was fabricated by sol-gel method and coated on the surface of the PCF by dip-coating method. When the Pd/WO3 film is exposed to hydrogen, both the length and cladding refractive index of the PCF would be changed, resulting in the resonant wavelength shift of the interferometer. Experimental results showed that the hydrogen measurement sensitivity of the proposed sensor can reach 0.109 pm/(μl/l) in the transformer oil, with the measurement range of 0-10 000 μl/l and response time less than 33 min. Besides, the proposed sensor was temperature-insensitive without any compensation process, easy to fabricate without any tapering, polishing, or etching process, low cost and quickly response without any oil-gas separation device. All these performances satisfy the actual need of real-time monitoring of dissolved hydrogen concentration in the transformer oil.

PMMA Based Optical Fiber Bragg Grating for Measuring Moisture in Transformer Oil

Water contamination can cause serious problems that compromise in transformer's safe operation and reduce its lifetime. Online monitoring of moisture concentration in transformer oil would permit the control of moisture buildup. This letter presents a direct optical measurement of moisture concentration in transformer oil using a poly(methyl methacrylate) (PMMA)-based optical fiber Bragg grating (POFBG). The refractive index and volume of PMMA-based optical fiber vary with the moisture in the surrounding transformer oil, changing the reflecting wavelength of the grating. A sensitivity of POFBG wavelength change to moisture content of 29 pm/ppm is demonstrated in this letter, indicating detectable water content better than 0.05 ppm.

Research on High Sensitive D-Shaped FBG Hydrogen Sensors in Power Transformer Oil.

Dissolved hydrogen is a symbol gas decomposed by power transformer oil for electrical faults such as overheat or partial discharges. A novel D-shaped fiber Bragg grating (D-FBG) sensor is herein proposed and was fabricated with magnetron sputtering to measure the dissolved hydrogen concentration in power transformer oil in this paper. Different from the RI (refractive index)-based effect, D-FBG in this case is sensitive to curvature caused by stress from sensing coating, leading to Bragg wavelength shifts accordingly. The relationship between the D-FBG wavelength shift and dissolved hydrogen concentration in oil was measured experimentally in the laboratory. The detected sensitivity could be as high as 1.96 μL/L at every 1-pm wavelength shift. The results proved that a simple, polished FBG-based hydrogen sensor provides a linear measuring characteristic in the range of low hydrogen concentrations in transformer oil. Moreover, the stable hydrogen sensing performance was investigated by X-ray diffraction analysis.

Analysis of furfural dissolved in transformer oil based on confocal laser Raman spectroscopy

Furfural (Furan-2-carbaldehyde) dissolved in transformer oil is regarded as an important mark of the thermal and mechanical degradation in oil-paper insulation. Confocal laser Raman spectroscopy (CLRS) is an effective means of detecting trace liquid and has been widely applied in many fields. Using CLRS to detect furfural concentration can overcome many disadvantages of traditional detection methods and accelerate testing. Thus, the application of CLRS in the detection of furfural concentration was investigated in this research. A structure model of a furfural molecule was constructed with Gaussian 09W software. Then, the Raman spectrum of this molecule was analyzed theoretically. Meanwhile, a set of liquid test platforms was set up based on basic CLRS principles. Subsequently, standard transformer oil samples were tested by utilizing extraction technology together with the CLRS liquid test platform. As a result, the peak at 1677 cm−1 was selected as the Raman characteristic peak. The detection limit reached 0.10 mg/L. In addition, a quantitative analysis model was constructed between the concentrations and the Raman characteristic peak areas by applying external standard method and least square method. Finally, six types of oil samples with different furfural concentrations were detected; the test results were compared with those obtained through high-performance liquid chromatography. Comparison results showed that CLRS with extraction can quantitatively and effectively detect the furfural concentration in transformer oil (with a maximum error less than 11.7%). Therefore, a new detection tool is presented for assessing the degradation of insulating papers in power transformers.

Effect of Conducting Materials on UV-Vis Spectral Response Characteristics

Spectroscopic analysis is recently being employed to measure furan concentration in transformer oil. Light absorbance and spectral bandwidth are used as indicators to estimate furan concentration level in transformer oil. However, the possibility of dust and other conducting materials that might exist in transformer oil sample need to be considered as it may influence the spectroscopic performance. In this paper, series of tests are carried out to investigate the impact of dust on the transformer oil spectral response characteristics. Oil sample with various furan concentrations along with certain amount of copper powder are examined. Experimental results show that the spectral response characteristics only affected by dust at the beginning of mix process. However, the characteristic returns to the original one within a few minutes.

Correlation of furan concentration and spectral response of transformer oil-using expert systems

Furans are the major degradation of insulating study in transformer oil. Hence, the concentration of furans in oil can be used as a good indicator of study deterioration. Furan concentration in transformer oil is currently measured using high-performance liquid chromatography or gas chromatography–mass spectrometry. Both methods provide accurate and reliable results in detecting furan concentration. However, the two methods need very expensive equipment and take a long time to perform the test on oil samples that should be chemically treated before conducting the test. Moreover, both methods require an expert to perform the test and to interpret its results. This study introduces a novel technique for detecting furan concentration in transformer oil through measuring oil spectral response. The ultraviolet-to-visible spectral response of transformer oil can be measured instantly with relatively cheap equipment and does not need an expert person to conduct the test. Results show that there is a good correlation between oil spectral response and its furan contents. The study introduces two approaches using expert systems to estimate the relationship between furan concentration and spectral response of transformer oil; they are gene expression programming and fuzzy logic.

The determination of FFA concentration in transformer oil by fluorescence measurements

Condition monitoring of the paper winding of power transformers involves the measurement of the concentration of 2-furaldehyde, also called furfuraldehyde (FFA) in the transformer oil. Measuring the concentration of FFA in the oil gives a reliable indication of the condition of the paper insulation. This measurement has routinely been performed by using techniques such as high performance liquid chromatography. We report for the first time the use of fluorescence-based methods for the assessment of FFA in transformer oil.