Gas Analysis Method Research Articles

Application of Dissolved Gas Analysis Standards and Methods on Raw Gas Sample

Dissolved gas analysis (DGA) is a tool to diagnose and identify faults that causing abnormal thermal or electrical activity inside power transformers, which could give rise to the aging and degrading of oil and pressboard. Different DGA standards and methods will be reviewed, and discussed in the paper, followed by a demonstration of application of three DGA method on a dissolved gas dataset.

Development and implementation of UV absorption methods of gas analysis for environmental monitoring of the atmosphere

Development and implementation of UV absorption methods of gas analysis for environmental monitoring of the atmosphere

Depolarization Ratio of the ν1 Raman Band of Pure CH4 and Perturbed by N2 and CO2.

In this work, the effect of nitrogen and carbon dioxide on the depolarization ratio of the ν1 band of methane in the pressure range of 0.1–5 MPa is studied. A high-sensitivity single-pass Raman spectrometer was used to obtain accurate results. Moreover, we took into account the overlap of the ν1 band by the ν3 and ν2 + ν4 bands using the simulation of their spectra. The depolarization ratio of the ν1 band in pure methane is within 0–0.001, and the effect of nitrogen and carbon dioxide on this parameter is negligible in the indicated pressure range. The obtained results are useful for correct simulation of the Raman spectrum of methane at different pressures, which is necessary to improve the accuracy of gas analysis methods using Raman spectroscopy.

Analytical performance validation and clinical application of blood gas analyzer on the detection of neonatal bilirubin.

BackgroundIt is not yet clear whether the trace blood gas analyzer can be used for biochemical detection of newborns. This study aimed to evaluate the reliability of the method for the detection of bilirubin in infants.MethodsBased on the Clinical and Laboratory Standards Institute (CLSI) EP15-A2 document, the analytical performance of the blood gas analyzer method for bilirubin detection in neonates was validated. The resulting data of 363 simultaneous bilirubin detection with blood gas analyzer (optical method) and biochemical analyzer (enzymatic method) were reviewed. According to the CLSI EP9-A3 document, the relevance and consistency of the measurement results were evaluated by Pearson correlation analysis, Passing-Bablok regression, and Bland-Altman deviation analysis.ResultsThe precision and accuracy of the Werfen GEM 4000 blood gas analyzer for the detection of different levels of bilirubin samples adhered to the manufacturer's statement and industry quality standards. The bilirubin detection values of the 2 methods showed a good correlation, and both of them were significantly correlated (P<0.001). Passing-Bablok regression results showed that the regression equation of the bilirubin detection value of the 2 methods is y = −21.00 + 1.17x, with the slope as 1.17 [95% confidence interval (CI): 1.15 to 1.19], and the intercept was −21.00 (95% CI: −23.62 to −18.71), the data of the 2 sets were not consistent in each concentration range. The Bland-Altman plot demonstrated that the bilirubin detection value of 16/363 cases (4.4%) for the 2 methods exceeded the 95% limits of agreement (95% LoA); of which the maximum bias was −30.34 (95% CI: −38.48 to −22.26) and there were 5/76 cases (6.6%) outside the 95% LoA in the >300 μmol/L group.ConclusionsThe method for detecting neonatal total bilirubin by trace blood gas analyzer basically meets the clinical requirements and can be used for the preliminary screening of neonatal jaundice. However, for severe hyperbilirubinemia that requires close monitoring of dynamics, a precise enzymatic quantification is required.

In-situ Detection of Hydrogen Gas Evolved from Dissolving Mg by Gas-Chromatographic Analysis

The higher rates of hydrogen evolution are observed on Mg and its alloys surface when they are polarized anodically in the electrolyte solution. To clarify this phenomenon, which has been termed “anomalous HE” in recent years1, various real-time hydrogen gas analysis methods have been developed2-10. Our group5,10 have developed an in-situ hydrogen gas detection system during electrochemical measurement of Mg. This system is composed of an electrochemical cell and a gas chromatograph. When the hydrogen gas evolved from dissolving Mg surface during electrochemical measurement, the gases are delivered to the gas chromatograph with the argon carrier gas. Thus, it allows for the qualitative and quantitative analyses of gases evolved from dissolving Mg surface.In the present study, we applied the developed system to in-situ detection of hydrogen gas from magnesium surface under potentiostatic polarization and during anodic polarization curve measurement5,10. The hydrogen evolution rate could be determined successively by calculating from the volume of hydrogen gas estimated by gas-chromatographic analysis at arbitrary time under potentiostatic polarization. In the case of anodic polarization curve measurement of Mg, the cathodic current attributed to the hydrogen evolution reaction could be estimated from the volume of hydrogen gas obtained by gas-chromatographic analysis at arbitrary polarization potential. It indicated that the partial anodic current at each polarization potential could be estimated by the sum of the measured anodic current and absolute value of cathodic current related to the hydrogen evolution reaction by the developed system. On the basis of these results, the measured anodic polarization curve of Mg and partial anodic polarization curve, which could be obtained by the plots of partial anodic current at each polarization potential, are correlated discussed.References M. Esmaily, J. E. Svensson, S. Fajardo, N. Birbilis, G. S. Frankel, S. Virtanen, R. Arrabal, S. Thomas, and L. G. Johansson, Prog. Mater. Sci., 89, 92 (2017).M. Curioni, Electrochim. Acta, 120, 284 (2014).S. Lebouil, A. Duboin, F. Monti, P. Tabeling, P. Volovitch, and K. Ogle, Electrochim. Acta, 124, 176 (2014).S. Fajardo and G. S. Frankel, J. Electrochem. Soc., 162, C693 (2015).Y. Hoshi, R. Takemiya, I. Shitanda, and M. Itagaki, J. Electrochem. Soc., 163, C303 (2016).Y. Hoshi, K. Miyazawa, I. Shitanda, and M. Itagaki, J. Electrochem. Soc., 165, C243 (2018).M. Strebl and S. Virtanen, J. Electrochem. Soc., 166, C3001 (2019).M. Strebl, M. Bruns, and S. Virtanen, J. Electrochem. Soc., 167, 021510 (2020).M. G. Strebl, M. P. Bruns, G. Schulze, and S. Virtanen, J. Electrochem. Soc., 168, 011502 (2021).Y. Hoshi, Y. Hirayama, I. Shitanda, and M. Itagaki, J. Electrochem. Soc., 168, 031510 (2021).

A novel method for transformer fault diagnosis based on refined deep residual shrinkage network

This study proposes a novel method to improve the fault identification performance of transformers. First, to couple multiple factors, a high-dimensional feature map composed of the feature gas concentrations and some associated variables is constructed. Second, the deep residual shrinkage network is revised using the updated alternating direction multiplier, and the newly constructed variable soft thresholding is proposed to eliminate constant deviations. In addition, the fast iterative shrinkage-thresholding algorithm is adopted, as it can speed up the determination of the threshold. For the output end, the uniform manifold approximation and projection algorithm are adopted to ensure the integrity of the local optimal solution and the global solution. Compared with traditional dissolved gas analysis methods, the novel refined deep residual shrinkage network exhibits superior precision, which is justified through experiments. The results show that the recognition accuracy of the new model is more than 1.3% higher than that of the existing methods. The new method has good scalability in power applications and fault prevention.

Correlation of anthropometric index and cardiopulmonary exercise testing in children with pectus excavatum

BackgroundCardiopulmonary exercise testing (CPET) is a method used to evaluate functional impairment of patients with various diseases. ObjectiveThe objective was to use CPET to estimate the usability of anthropometric index (AI) in patients with pectus excavatum (PE) as a marker of functional impairment caused by chest deformity. MethodsThe study included 32 paediatric patients (28 males) with PE. Patients underwent CPET using a breath-by-breath exhaled gas analysis method and continuous monitoring of cardiac parameters. ResultsIn both groups, two (overall four) patients met criteria for cardiogenic limitation (low VO2 and low O2Pulse). Mean VO2/WR was below two standard deviations (2SD) in patients with less severe PE; other observed parameters were within normal limits (Z-score ± 2 SD). The AI had no observed correlation with peak ventilation, VO2peak and peak workload. ConclusionThe obtained CPET data do not correlate well with the severity of chest deformity expressed with AI. There were similar physical activity limitations in both examined groups of patients and they did not depend on the severity of the deformity.

Coulombic effects on resolution of ion mobility spectrometry and its application in online qualitative analysis

Ion mobility spectrometry is an important gas analysis method used in the rapid detection field. However, due to a lacking of explicit mathematical model of ion peak, it is difficult to extract characteristic analyte peaks from a spectrum containing overlapping peaks to achieve online qualitative analysis. Here, we present an asymmetric peak model for processing ion mobility peaks. For the asymmetric peak model, the key is to accurately estimate the standard deviation of the peak model and the fitting function of the tailing edge. We focused on the Coulombic effects on resolution of ion mobility spectrometry based on a new hypothesis of ion cloud shape and derived a formula for calculating the standard deviation taking the initial pulse width, diffusion and Coulomb repulsion factors into account. The proposed asymmetric peak model combines the advantages of optimal physical and chemical interpretation and explicit mathematical meaning. A fast decomposition method based on the peak model was developed to decompose overlapping peaks. Two overlapping simulated data sets and one real data set (a mixture of acetone and methyl salicylate) were used to test the method. The results indicated that our proposed method successfully decomposed the overlapping spectrum into individual peaks and performed markedly better than other three available methods in terms of the execution time. The proposed method meets the requirements for online qualitative analysis.

Evaluation and Interpretation of Dissolved Gas Analysis of Soybean-Based Natural Ester Insulating Liquid

The electrical industry needs a better understanding in the topic related to the fault diagnosis, since these fluids are chemically different in comparison with conventional mineral oils, therefore, its performance in field changes, for example the generated gases. This paper presents an investigation related to the gas generation of a new soybean-based natural ester liquid, which includes a data compilation of distribution and power transformers diagnosis during factory tests by dissolved gas analysis (DGA) method. This work covers an evaluation of the generated gases for three common types of transformer faults (overheating, partial discharge and high energy discharges) at lab scale testing and their interpretation by using different diagnostic methods to predict the fault type. It is found that overheating problems in natural esters can be associated with ethylene, while acetylene and hydrogen are linked for high energy fault and partial discharge faults, respectively. On the other hand, it is confirmed that Duval triangle is the most reliable method to diagnose natural esters liquids. The authors consider that the DGA data presented in this work represent valuable information for the transformer industry.

Interpreting dissolved gases in transformer oil: A new method based on the analysis of labelled fault data

In this contribution, a new dissolved gas analysis (DGA) method combining key gases and ratio approaches for power transformer fault diagnostic is presented. It is based on studying subsets and uses the five main hydrocarbon gases including hydrogen (H2), methane (CH4), ethane (C2H6), ethylene (C2H4), and acetylene (C2H2). The proposed method uses 475 samples from the dataset divided into subsets formed from the maximum and minimum(s) concentrations of the whole dataset. It has been tested on 117 DGA sample data and validated on the International Electrotechnical Commission (IEC) TC10 database. The performance of the proposed diagnostic method was evaluated and compared with the following diagnostic methods: IEC ratios method, Duval's triangle (DT), three ratios technique (TRT), Gouda's triangle (GT), and self-organizing map (SOM) clusters. The results found were analysed by computer simulations using MATLAB software. The proposed method has a diagnosis accuracy of 97.42% for fault types, as compared to 93.16% of TRT, 96.58% of GT method, 97.25% of SOM clusters method and 98.29% of DT method. However, in terms of fault severity, the proposed method has a diagnostic accuracy of 90.59% as compared to 78.90% of SOM clusters method, 83.76% of TRT, 88.03% of DT method, and 89.74% of GT method.

Condition Monitoring of High Voltage Transformer using Dissolved Gas Analysis Methods

Power transformer plays a significant role in the entire power transmission network; thus, transformer protection requires more attention for fault free electric supply. when the mineral oil and insulation inside the transformer is subjected to high thermal and electrical stresses, gases are created by the decay of mineral oil and cellulose. Different gases create different faults, Identification of faults inside the power transformer before they occur reduces its failure rate during its service period. For Knowing the fault condition of power transformer, Dissolved Gas Analysis (DGA) is proven to be as accurate method based on combination of concentration of gases like CO, CO2, H2, C2H6, C2H4, C2H2 etc., Dissolved gas analysis is the most important test in determining the fault condition of a transformer and it is the first indicator of a problem and can identify deteriorating insulation and oil, overheating hot spots, partial discharge and arcing. For developing this DGA Techniques, the MATLAB GUIDE interface can be used for making easy interaction between the user and software developed. This software is designed using some conditional statements and logical functions to get the type of faults in transformers based on the concentration of gases in transformer oil. The faults in transformer using dissolved gases analysis are detected using methods such as key gas, Roger’s methods, IEC ratio, Doernenburg ratio, Duval triangle and the Combined DGA methods. In this paper, these four methods of dissolved gas analysis (DGA) are presented and explained briefly.

Failure of 150 KV Power Transformer in Indication of Dissolved Gas Analysis Test in Total Dissolved Combustile Gas (TDCG) Method

A transformer is an electrical device to change the voltage from one level to a certain level with the same frequency that is generated from the generator. This transformer is used in a power plant to increase the voltage from the generator to be supplied to extra high voltage substations. By knowing the type and amount of gas in the transformer oil, it can be seen that the failure occurred. Preventive steps can be taken before the transformer is more severely damaged or a breakdown which will result in disrupted and detrimental electrical energy supply. The method used in this research is data collection methods used are observation, interviews and participation conducted at PT. Krakatau, Serang Banten. The method used to determine the failure of the transformer is the Dissolved Gas Analysis (DGA) method. The DGA test uses several methods to interpret the data, these methods are Total Dissolved Combustile Gas (TDCG), Key Gasses, Doernenburg Ratio, Roger's Ratio, IEC Ratio and Duval's Triangel. The method used in this research is direct observation of the process of taking transformer oil samples and testing transformer oil samples with the Transport-X tool. The result of this research is that the transformer condition is still in normal condition, because the appearance of CO2 gas has almost crossed the condition limit, so other supporting methods are needed to find the cause of the gas. Key gasses indicate the presence of overheating of paper insulation, IEC Ratio diagnoses transformer failure caused by heat failure at temperatures of 300o-700o C, Duval's Triangel diagnoses transformer failure caused by temperatures over 700o and this method supports the previous analysis. With this method, overheating occurs at certain points (hotspots) in the internal transformer. Preventive maintenance is needed to conform to the standard of the transformer. A transformer is an electrical device to change the voltage from one level to a certain level with the same frequency that is generated from the generator. This transformer is used in a power plant to increase the voltage from the generator to be supplied to extra high voltage substations. By knowing the type and amount of gas in the transformer oil, it can be seen that the failure occurred. Preventive steps can be taken before the transformer is more severely damaged or a breakdown which will result in disrupted and detrimental electrical energy supply. The method used in this research is data collection methods used are observation, interviews and participation conducted at PT. Krakatau, Serang Banten. The method used to determine the failure of the transformer is the Dissolved Gas Analysis (DGA) method. The DGA test uses several methods to interpret the data, these methods are Total Dissolved Combustile Gas (TDCG), Key Gasses, Doernenburg Ratio, Roger's Ratio, IEC Ratio and Duval's Triangel. The method used in this research is direct observation of the process of taking transformer oil samples and testing transformer oil samples with the Transport-X tool. The result of this research is that the transformer condition is still in normal condition, because the appearance of CO 2 gas has almost crossed the condition limit, so other supporting methods are needed to find the cause of the gas. Key gasses indicate the presence of overheating of paper insulation, IEC Ratio diagnoses transformer failure caused by heat failure at temperatures of 300 o -700 o C, Duval's Triangel diagnoses transformer failure caused by temperatures over 700 o and this method supports the previous analysis. With this method, overheating occurs at certain points (hotspots) in the internal transformer. Preventive maintenance is needed to conform to the standard of the transformer.

Convolutional Bi-directional Long Short Term Memory Network based Dynamic Fault Diagnosis for Transformer DGA

Dissolved gas analysis (DGA) method is one of the important methods to detect early internal faults in transformers. Aiming at the shortcomings of feature extraction and online modelling in the analysis model based on shallow layer, a transformer DGA online fault diagnosis method combining convolutional neural network and bidirectional long short term memory network is proposed in this paper. By using multi-time characteristic gas data as the input of the diagnostic model, combined with convolution and pooling, the fault sensitive features were extracted, and the time dimension features were extracted by using the recursive neural network with gated structure. Under various evaluation criteria, it is compared with convolutional neural network, bidirectional long short-term memory (Bi-LSTM) network and dynamic support vector machine. The experimental results show that the online transformer diagnosis model proposed in this paper, which considers both time and space characteristics, has higher prediction accuracy.

A Robust Multi-Layer Framework for Online Condition Assessment of Power Transformers

Online continuous monitoring of dissolved gasses in transformer oil has become an emerging trend in the recent years. This way, in addition to sending the warning signal in the case of gas concentration, a fault diagnosis procedure can be conducted using standard Dissolved Gas Analysis (DGA) methods. However, since the fault diagnosis outcome and result of these methods are sometimes different, an appropriate solution should be suggested to accurately describe the transformer internal condition. To do so, a robust multi-layer framework has been proposed in this paper. Handling the measurement uncertainties and fusing the results of independent DGA methods without losing their diagnosis resolutions are the interesting features of this framework. To improve the overall accuracy of fused diagnosis result, an artificial neural network-based approach is proposed. It intelligently assigns the weight of each independent method in the fusion procedure according to its fault type detection accuracy for the range of input gasses concentrations. To evaluate performance of the proposed framework, comprehensive test studies have been conducted and the promising obtained results prove its superior performance for use in online applications.

Fault Diagnosis Method of Power Transformer Based on Improved PNN

Power transformer is a core hub of power transmission in power grid, and its operation state will affect the effective operation of power grid. The existing power transformer fault diagnosis methods mainly use the traditional dissolved gas analysis (DGA) method combined with artificial neural network. Probabilistic neural network (PNN) has a wide range of applications in the field of power transformer fault diagnosis because of its good fault tolerance and high training efficiency. In PNN, the smoothing factor has a great influence on the output, but the parameter is lack of efficient selection method, which makes the classification efficiency of PNN not high. Aiming at this problem, a diagnosis method based on improved PNN is proposed for power transformer faults in this paper. Bat algorithm is used to optimize the smoothing factor of PNN to determine the optimal value. Based on the optimal results, the network model is trained to obtain the optimal power transformer fault diagnosis model. The experiment fully proves that the improved PNN has higher diagnosis accuracy than other methods.

Application of piezoelectric transducers in photoacoustic spectroscopy for trace gas analysis

AbstractPhotoacoustic spectroscopy (PAS) is a boomingly developed spectroscopic method for trace gas analysis in the past decades. Unlike traditional laser absorption spectroscopy, an acoustic transducer is employed to detect the acoustic waves instead of optical waves. Microphone is a commonly used transducer to convert the acoustic signal into electrical signal, which is related to the gas concentration. In recent years, piezoelectric materials are playing important roles in the acoustic‐to‐electrical conversion process of photoacoustic system. In this article, recently used transducers in PAS such as quartz tuning fork, polyvinylidene fluoride, lead zirconate titanate, electromechanical film and LiNbO3, BiB3O6, NbCOB crystals are summarized and discussed in detail. The different spectrophone configuration, schematic diagram, and experimental system are reported. Side by side comparison is carried out to show the gas sensing performance of these piezoelectric transducers.

Research on Real-time Application of Ubiquitous Power Internet of Things Information Based on Smart Sensor Technology

In view of the current situation that it is difficult for the power control centre to take into account the multi-source information to conduct effective risk assessment on the dynamic process of the power grid, the challenges and opportunities faced by the power grid risk assessment under the background of the ubiquitous power Internet of Things are pointed out. The framework of the dynamic risk assessment system for the grid of the Internet of Things. At the same time, in view of the monitoring and fault diagnosis functions of sensor technology, especially gas sensor in transformer oil chromatographic monitoring, the application of ubiquitous power Internet of Things sensor technology in online monitoring of power equipment is discussed. First, the application of online monitoring of power equipment is analysed. The inevitable trend will then discuss in detail the monitoring and fault diagnosis functions of gas sensor technology in transformer oil chromatographic monitoring from sensor technology, gas sensor, transformer oil chromatographic device, and gas analysis method.

Cooper’s 12 min run test: Its validity in Indian swimmers and volleyball players

Objectives: VO2max is globally considered as the gold standard to evaluate the cardiorespiratory fitness that is an essential component to judge one’s aerobic fitness level. The present study was aimed to enumerate the validity for application of Cooper’s 12 min run test (CRT) in predicting VO2max among Indian swimmers and volleyball players. Materials and Methods: Swimmers (male = 66, female = 70) and volleyball players (male = 88, female = 81) were recruited by simple random sampling from reputed clubs of Kolkata. They were segregated into study and confirmatory groups. VO2max was determined by graded incremental cycle ergometer test followed by expired gas analysis and indirect CRT method. Results: Predicted VO2max (PVO2max) showed significant difference with the directly measured VO2max (VO2max) in study groups of both sports. Limits of agreement between PVO2max and VO2max proved inapplicability of current CRT protocol in studied populations. Modified population specific equations were computed from significant correlation of VO2max with distance covered in Cooper run test. Application of these norms in confirmatory groups revealed insignificant difference between PVO2max and VO2max in both genders. Conclusion: Modified equations are validated for application of CRT in evaluating VO2max in swimmers and volleyball players of both genders of Kolkata, India.

Study of Thermooxidation of Oil Shale Samples and Basics of Processes for Utilization of Oil Shale Ashes

A circular economy becomes an object of actual discussions as a real alternative to the existing linear economy system. The problem is actually in Estonia also, first of all in the sector of heat and power production which based mainly on the combustion of local solid fossil fuel—Estonian oil shale (OS) resulting in the formation of ~5–6 million tons of OS ashes annually. The thermooxidative decomposition of OS samples from different deposits and estimation of the possibilities of utilization of OS ashes formed at both—pulverized firing (PF) and circulating fluidized bed combustion (CFBC) of Estonian OS were studied. The thermal analysis combined with evolved gas analysis (EGA) methods like Fourier transform infrared (FTIR) and mass-spectroscopy (MS) was exploited. It was established that the differences in the thermal behaviour of different OS samples are caused by the differences in the chemical matrix of organic matter, chemical and mineralogical composition of the inorganic part of OS, and morphology of samples. It was also found that moderate grinding of OS ashes with simultaneous moderate water treatment notably improved the SO2 binding efficiency of cyclone ash, and that the strength and leachability characteristics of granulated OS ashes strongly depend on the post-granulation treatment conditions allowing to increase the soil neutralizing ability of the granulated products. This overview was based on our investigations carried out during the last fifteen years.

Reveal of through defects by gas analysis method

Reveal of through defects by gas analysis method