Dissolved Gas Analysis Research Articles

Conceptual Examination of Pt Atom-Adorned WTe2 for Improved Adsorption and Identification of CO and C2H4 in Dissolved Gas Analysis

The online monitoring of transformer insulation is crucial for ensuring power system stability and safety. Dissolved gas analysis (DGA), employing highly sensitive gas sensors to detect dissolved gas in transformer oil, offers a promising means to assess equipment insulation performance. Based on density functional theory (DFT), platinum modification of a WTe2 monolayer was studied and the adsorption behavior of CO and C2H4 on the Pt-WTe2 monolayer was simulated. The results showed that the Pt atom could be firmly anchored to the W atoms in the WTe2 monolayer, with a binding energy of −3.12 eV. The Pt-WTe2 monolayer showed a trend toward chemical adsorption to CO and C2H4 with adsorption energies of −2.46 and −1.88 eV, respectively, highlighting a stronger ability of Pt-WTe2 to adsorb CO compared with C2H4. Analyses of the band structure (BS) and density of states (DOS) revealed altered electronic properties in the Pt-WTe2 monolayer after gas adsorption. The bandgap decreased to 1.082 eV in the CO system and 1.084 eV in the C2H4 system, indicating a stronger interaction of Pt-WTe2 with CO, corroborated by the analysis of DOS. Moreover, the observed change in work function (WF) was more significant in CO systems, suggesting the potential of Pt-WTe2 as a WF-based gas sensor for CO detection. This study unveils the gas-sensing potential of the Pt-WTe2 monolayer for transformer status evaluation, paving the way for the development of gas sensor preparation for DGA.

Enhancing power transformer health assessment through dimensional reduction and ensemble approaches in Dissolved Gas Analysis

AbstractTransformer health analysis using Dissolved Gas Analysis is crucial for diagnosing power transformer faults. This paper proposes an innovative approach to diagnose power transformer faults by integrating machine learning algorithms with Ensemble techniques. The method involves fusing reduced dimensional input features through Principal Component Analysis with Ensemble techniques such as Bagging, Decorate, and Boosting. Various machine learning algorithms, including Decision Tree (DT), K‐Nearest Neighbours, Radial Basis Function Network, and Support Vector Machine, are employed in conjunction with Ensemble techniques. The long short‐term memory algorithm was used to create synthetic data to solve the issue of data imbalance. A dataset of 683 samples is used in the study for training, testing, validation, and comparison with current techniques. The results highlight the effectiveness of Ensemble techniques, particularly Boosting, which demonstrates superior performance across all classification algorithms. The Boosting with DT algorithm achieves an impressive accuracy of 98.32%, surpassing alternative methods. In validation, the proposed Boosting Ensemble technique outperforms various approaches, showcasing its diagnostic accuracy and superiority over alternative methods. The research emphasises the model's effectiveness in smoothing input vectors, enhancing harmony with ensemble techniques, and overcoming limitations in prior methods.

Enhancing Predictive Maintenance of Power Transformers Through Machine Learning Approaches

This paper focuses on the use of machine learning algorithms to assist in predictive maintenance, aiming to reduce downtime and associated costs for electrical equipment. It introduces two machine learning predictive indicators: the Chromatographic Assay Indicator (CAI) and the Electrical Failure Risk Indicator (EFRI), which leverage chromatographic and sensor data, respectively. The CAI indicator was trained on external data, and its generalization capability was assessed using company-specific chromatographic data with expert assistance. The evaluation involved two heuristics, both focusing on keyword identification in free-text diagnoses. Results showed over 90% accuracy in predicting failures in reactors and power transformers, and an AUC of nearly 85% for reactors. Additionally, CAI outperformed classical Dissolved Gas Analysis (DGA) methods. On the other hand, the EFRI indicator was trained using company’s internal data from a monitoring system of operational sensors and maintaining power transformer data. The classification model achieved over 95% accuracy and an AUC of almost 90% on the test set. These results indicate that both indicators can be integrated into a solution to support maintenance specialists in their decision-making processes.

Implementation of Fuzzy Logic Scheme for Assessment of Power Transformer Oil Deterioration Using Imprecise Information

This research aims to analyze the implementation of a fuzzy logic-based approach in improving the diagnosis of power transformer oil deterioration, which is critical for maintaining the efficient performance and operational life of transformers. Traditional diagnoses are based on strict measurements that do not account for the factors of variability and uncertainty of the actual data. In this article, we perform six different types of tests in this regard, and data have been collected during the period of 2021 to 2022 of 188 power transformer failures in the New KotLakhpat Lahore unit, whose voltage range is 132/66 kv and rating capacity is 40/50 MVA. In this case, a fuzzy logic-based scheme is developed based upon the membership function, a rule-based and defuzzification method that works with imprecision and the implementation of uncertainty in assessing the condition of transformer oils. Moisture, acidity, and a dissolved gas analysis indicator, along with other indication approaches such as interfacial tension, viscosity, and tangent delta measurement, are used to analyze the deterioration process in transformer oils. In the visual representation, oil samples with the following properties were first fuzzified: 19.9 mm2/s of viscosity, 0.453 mgKOH/g of acidity, 695 ppm of DGA, 20.8 mg/kg of moisture, 19.98 of IFT, and 4.35 × 100.14 of tangent delta. The output that was generated by software using the values entered into the parameters (HI and Age) after defuzzification is 45. Fuzzy logic serves as a concrete framework for transforming the diagnostics system and deterring the threats to the entire transformer’s health and reliability in the future. By using this technique, various faults were hypothetically and practically analyzed in a transformer to implement early detection technologies with the possibility to reduce maintenance costs and extend operational life up to 45 years. Various case studies indicate the effectiveness of fuzzy logic in comparison to traditional diagnostics.

Power Transformer Fault Diagnosis Based on Random Forest and Improved Particle Swarm Optimization–Backpropagation–AdaBoost

This paper proposes a novel fault diagnosis methodology for oil-immersed transformers to improve the diagnostic accuracy influenced by gas components in power transformer oil. Firstly, the Random Forest (RF) algorithm is utilized to evaluate and filter the raw data features, solving the problem of determining significant features in the dataset. Secondly, a multi-strategy Improved Particle Swarm Optimization (IPSO) is applied to optimize a double-hidden layer backpropagation neural network (BPNN), which overcomes the challenge of determining hyperparameters in the model. Four enhancement strategies, including SPM chaos mapping based on opposition-based learning, adaptive weight, spiral flight search, and crisscross strategies, are introduced based on traditional Particle Swarm Optimization (PSO) to enhance the model’s optimization capabilities. Lastly, AdaBoost is integrated to fortify the resilience of the IPSO-BP network. Ablation experiments demonstrate an enhanced convergence rate and model accuracy of IPSO. Case analysis using Dissolved Gas Analysis (DGA) samples compares the proposed IPSO–BP–AdaBoost model with other swarm intelligence optimization algorithms integrated with BPNN. The experimental findings highlight the superior diagnostic accuracy and classification performance of the IPSO–BP–AdaBoost model.

Pd-Adsorbing CrS2 monolayer as sensing material for detecting CO, C2H2, and C2H4 in dissolved gases: A first-principles study

This study employs density functional theory to initially examine the adsorption characteristics of Pd atom on CrS2 monolayer (termed Pd@CrS2). Subsequently, the adsorption capabilities of Pd@CrS2 monolayer for CO, C2H2, and C2H4 are simulated to assess its prospects for dissolved gas detection. Stable adsorption of Pd atoms onto the CrS2 monolayer atop Cr atoms is observed with a binding energy of −2.69 eV. Pd@CrS2 exhibits chemisorption towards CO, C2H2, and C2H4 with respective adsorption energies of −1.49, −1.13, and −1.22 eV. The band structure and density of states analysis indicate significant alterations in the electronic properties of CrS2 upon Pd adsorption and upon exposure to the gases. The bandgap of Pd@CrS2 is modified by −29.6 %, −24.7 %, and −27.1 % after CO, C2H2, and C2H4 adsorption, corresponding to highly sensitive detection responses of −99.6 %, −98.9 %, and −99.3 %. The research highlights the gas sensing capabilities of Pd@CrS2 monolayers for monitoring the operational status in dissolved gas analysis, showcasing the promising application potential of CrS2.

Theoretical Study of the Adsorption and Sensing Properties of Cr-Doped SnP3 Monolayer for Dissolved Characteristic Gases in Oil

Dissolved gas analysis (DGA) is a vital method for the online detection of transformer operation state. The adsorption performance of a SnP3 monolayer modified by transition metal Cr regarding six characteristic gases (CO, C2H4, C2H2, CH4, H2, C2H6) dissolved in oil was studied. The study reveals the relevant adsorption and gas-sensing response mechanisms through calculations of the adsorption energy, density of states, differential charge density, energy gap, and recovery time. The results display a considerable increase in the adsorption effect of the Cr-SnP3 monolayer on six gases. The CO, C2H2, and C2H4 gases lead to chemical adsorption, and the CH4, H2, and C2H6 gases lead to physical adsorption. Combined with the recovery time, the Cr-SnP3 monolayer has a strong adsorption effect on CO and C2H2 gases at normal temperatures and even high temperatures, and the adsorption is stable. C2H4 gas can be rapidly desorbed from the Cr-SnP3 monolayer at 398 K. Therefore, the Cr-SnP3 monolayer can be expected to serve as a CO and C2H2 gas adsorbent and a resistive gas sensor for C2H4 gas. This research offers a theoretical foundation for the development of the Cr-SnP3 monolayer in gas-sensitive materials.

Low-Power Chemiresistive Gas Sensors for Transformer Fault Diagnosis.

Dissolved gas analysis (DGA) is considered to be the most convenient and effective approach for transformer fault diagnosis. Due to their excellent performance and development potential, chemiresistive gas sensors are anticipated to supersede the traditional gas chromatography analysis in the dissolved gas analysis of transformers. However, their high operating temperature and high power consumption restrict their deployment in battery-powered devices. This review examines the underlying principles of chemiresistive gas sensors. It comprehensively summarizes recent advances in low-power gas sensors for the detection of dissolved fault characteristic gases (H2, C2H2, CH4, C2H6, C2H4, CO, and CO2). Emphasis is placed on the synthesis methods of sensitive materials and their properties. The investigations have yielded substantial experimental data, indicating that adjusting the particle size and morphology structure of the sensitive materials and combining them with noble metal doping are the principal methods for enhancing the sensitivity performance and reducing the power consumption of chemiresistive gas sensors. Additionally, strategies to overcome the significant challenge of cross-sensitivity encountered in applications are provided. Finally, the future development direction of chemiresistive gas sensors for DGA is envisioned, offering guidance for developing and applying novel gas-sensitive sensors in transformer fault diagnosis.

Analysis of Transformer Defect using Different Dissolved Gas Analysis Techniques: A Comparative Study

Analysis of Transformer Defect using Different Dissolved Gas Analysis Techniques: A Comparative Study

REVOLUTIONIZING POWER TRANSFORMER FAULT DIAGNOSIS THROUGH COGNITIVE ARTIFICIAL INTELLIGENCE AND DISSOLVED GAS ANALYSIS INTEGRATION

The research introduces a cognitive artificial intelligence (CAI) model that leverages dissolved gas analysis (DGA) to investigate power transformer faults. Conventional fault interpretation methods using DGA are limited in accuracy and uncertainty. In response, the proposed CAI model utilizes cognitive learning and direct interaction to achieve remarkably accurate fault identification without the need for supervised training. By extracting fault features through key gas ratio limitations. However, the CAI model also has a gap in data perception due to the information sensory challenges. Using gas ratios based on the conventional fault interpretation methods in the latest study still limited data perception of the CAI model to only three or four gas ratios. Thus, this study aims to increase data perception by extracting fault features through ten gas ratio limitations. The proposed CAI model's performance is validated, outperforming traditional methods like the Duval triangle method, Duval pentagon method, Doernenburg ratio method, and Roger ratio method, as well as common AI approaches including artificial neuron network, long short-term memory, nearest neighbor classifiers, support vector machine, ensemble classifiers, and decision trees. Notably, the CAI model's success rate in fault type identification stands at an impressive 98.04%. A distinctive trait of the CAI model is its autonomous knowledge accumulation and enhancement, enabled by inferring-fusion information and sensor-based knowledge integration. This intrinsic learning ability further contributes to its exceptional fault diagnosis accuracy. The proposed CAI model showcases promising potential for revolutionizing power transformer fault investigation and diagnosis, mitigating unplanned outages, and ultimately bolstering power system reliability.

Modified Dissolved Gas Analysis Scoring Approach for Transformer Health Evaluation Considering Delta and Rate Values of Dissolved Gases in Mineral Oil

Modified Dissolved Gas Analysis Scoring Approach for Transformer Health Evaluation Considering Delta and Rate Values of Dissolved Gases in Mineral Oil

Use of a Decision Tree to Define Transformer Oil Contamination from On-Load Tap-Changer Gases to Ensure Power Quality in the Grid

Power transformers are one of the most important and critical assets in the electricity distribution and transmission network. Power quality (PQ) can be disturbed when a power transformer is brought into or out of service, so it is very important to be sure of the reason for this action. Dissolved gas analysis (DGA) in oil can be used to diagnose the condition of transformer insulation. There may be situations where the DGA results indicate the presence of a serious fault which would lead to the transformer being taken out of service, when in fact the high gas concentrations are due to the leakage into the main oil tank of gases generated in the on-load tap-changer (OLTC) during normal operation. In previous work, using machine learning techniques and a distribution system operator's DGA database, a decision tree (DT) was developed to identify oil contamination from OLTC gases. In this work, the developed DT is applied to a new DGA database to identify contaminated transformers and test its accuracy. A total of 1161 DGA results from 95 transformers with OLTC were used, giving an initial DT accuracy of 83.13% when all samples were analysed and 85.26% when the last DGA result from each transformer was used.

Testing and approval standard development of online dissolved gas analysis monitors for oil‐filled transformers—Part I hydrogen monitors

Testing and approval standard development of online dissolved gas analysis monitors for oil‐filled transformers—Part I hydrogen monitors

Dissolved gas analysis comparison of electrically stressed methyl ester and mineral oil

Methyl ester is considered one of the alternative substitutes to mineral oil as an insulating liquid. This study investigates the dissolved gas analysis (DGA) of the methyl ester derived from palm oil, under low energy discharge faults. The aims are to understand the gas composition and evaluate the applicability of the well-established fault interpretation methods for mineral oil to the methyl ester. Experimental procedures were conducted based on the International Electrotechnical Commission (IEC) standards. It involved simulating electrical breakdowns in laboratory conditions as per IEC-156 standard and analyzing gas samples using gas chromatography based on IEC-567. Results show that methyl ester oils produce similar types of gases as mineral oils but at higher concentrations. The interpretation of DGA results using fault identification methods such as Duval Triangle, Duval Pentagon, and IEC ratio indicates an overestimation of fault severity in methyl ester oils, and categorizing the faults as high energy discharge. However, the key gas method correctly identifies the discharge in both methyl ester and mineral oils. These findings suggest the need for adjustments in existing DGA methods to account for the higher gas concentrations in methyl ester oils, for effective condition monitoring and maintenance of transformers if it was filled with methyl ester oil.

Dissolved gas analysis based fuzzy logic feamework for power transformer asset management

Dissolved gas analysis based fuzzy logic feamework for power transformer asset management

Advancement in transformer fault diagnosis technology

The transformer plays a critical role in maintaining the stability and smooth operation of the entire power system, particularly in power transmission and distribution. The paper begins by providing an overview of traditional fault diagnosis methods for transformers, including dissolved gas analysis and vibration analysis techniques, elucidating their developmental trajectory. Building upon these traditional methods, numerous researchers have aimed to enhance and optimize them through intelligent technologies such as neural networks, machine learning, and support vector machines. These researchers have addressed common issues in traditional fault diagnosis methods, such as the low correlation between characteristic parameters and faults, ambiguous fault descriptions, and the complexity of feature analysis. However, due to the complexity of transformer structures and the uncertainties in operating environments, the collection and analysis of characteristic parameters becomes highly intricate. Researchers have further refined algorithms and feature values based on intelligent diagnostic algorithms for transformers. The goal is to improve diagnostic speed, mitigate the impact of measurement noise, and further advance the adaptability of artificial intelligence technology in the field of transformers. On the other hand, the excellent multi-parameter analysis capability of artificial intelligence technology is more suitable for transformer diagnostic techniques that involve the fusion of multiple information sources. Through the powerful data acquisition, processing, and decision-making capabilities provided by intelligent algorithms, it can comprehensively analyze non-electrical parameters such as oil and gas characteristics, vibration signals, temperature, along with electrical parameters like short-circuit reactance and load ratio. Moreover, it can automatically analyze the inherent relationship between faults and characteristic quantities and provide decision-making suggestions. This technique plays a pivotal role in ensuring transformer safety and power network security, emerging as a prominent direction in transformer fault diagnosis research.

Intelligent Diagnosis Method of Transformer Based on Oil Chromatographic Data

Abstract As one of the most important equipment in the operation of power system, ensuring the safe and stable operation of power transformer is a prerequisite for ensuring the normal supply of power grid. The failure of the power transformer will lead to the interruption of the power supply of the power grid and cause huge losses to the national economy. With the rapid development of China’s power grid scale and the continuous improvement of the intelligent construction of modern power system, the number of power equipment such as transformers is increasing. Therefore, it is necessary to make timely and accurate judgment on the status of key power transformation and distribution equipment in the power system, and grasp the operation of electrical equipment in real time, so as to ensure the reliability of power supply in the power system. This paper takes 220kV oil-immersed transformer as the research object. This paper considers the problems and characteristics of the current transformer fault diagnosis methods. By making full use of dissolved gas analysis (DGA) information in oil, a method for diagnosing faults in electrical transformers based on particle swarm optimization and generalized regression neural networks has been proposed. The simulation comparison experiment is carried out to select the most suitable transformer intelligent diagnosis method based on oil chromatographic data.

Influence of water vapor concentration on photoacoustic spectroscopy‐based characteristic gas analysis of transformer faults

AbstractThe water vapor in the ambient air affects the accuracy of the photoacoustic (PA) dissolved gas analysis system for transformer health monitoring. A laser PA system was evaluated by dry and humidified standard gases to study the influence of water vapor concentration on PA gas detection. Theoretical analysis was conducted on the effect of gas molecule relaxation on PA signal detection. A high‐frequency resonant PA cell and a low‐frequency nonresonant PA cell were used to detect acetylene (C2H2) and carbon monoxide (CO), respectively. The experimental results show that the PA signal of humidified CO is about 12 times higher than PA signal of dry gas for the resonant PA detection system, respectively. In addition, as the frequency is increased from 30 to 980 Hz, the PA signals of humidified and dry CO attenuate by 1.5 and 6.9 times, respectively.

Adsorption behavior of Rh-VSe2 monolayer upon dissolved gases in transformer oil and the effect of applied electric field

Dissolved gas analysis (DGA) in transformer oil is an effective method to monitor the operating status of transformers. Based on fundamental principles, the adsorption behavior of decomposed gases from transformer oil (CO, CH4, C2H2, C2H4, and C2H6) on intrinsic and Rh-doped VSe2 monolayers is examined. The adsorption structure, adsorption energy, charge transfer, density of state, electron density difference, work function, and desorption properties are discussed to evaluate the potential applications of VSe2 monolayers as scavengers and gas-sensing materials for transformer oil decomposed gases. The results show that Rh dopant can be stably adsorbed on the surface of VSe2 monolayer, and the minimum binding energy is −4.957 eV. The adsorption behavior of oil-dissolved gases on the intrinsic VSe2 monolayer is weak. The sensing performance of VSe2 monolayer for oil-dissolved gas molecules is significantly enhanced after the introduction of Rh dopant. The sensing performance of Rh-VSe2 monolayer for CO, C2H2, C2H4 and C2H6 gases is stronger than that of CH4. Furthermore, in order to improve the applicability of the Rh doped VSe2 monolayer for the detection of oil-dissolved gases molecules. The effect of electric field on the sensing properties of gas molecules on Rh doped VSe2 monolayers is also investigated. Finally, the desorption performance of the system is evaluated based on the transition state theory and Van’t-Hoff-Arrhenius expression. The findings of the study not only disclose the method by which the Rh doped VSe2 monolayer detects the breakdown gasses in transformer oil, but they also offer theoretical recommendations for the advancement of VSe2-based sensors and scavengers.

Transformer fault diagnosis based on the improved QPSO and random forest

Abstract Although dissolved gas analysis (DGA) is an effective method for transformer fault diagnosis, problems with the quality and accuracy of DGA characterization datasets often arise in practical industrial applications and face difficulties in adjusting the parameters of fault diagnosis models. To address the above problems, this paper proposes a fault diagnosis model (MD-IQPSO-RF) based on Mahalanobis distance (MD) data cleaning and improved quantum particle swarm (IQPSO) optimization of random forest (RF) parameters. Specifically, the abnormal samples of the DGA dataset are first processed by MD to improve the quality and accuracy of the dataset. Then, the RF parameters were optimized using the IQPSO algorithm to adjust the model parameters in order to improve the diagnostic performance of the RF. Finally, the optimal parameters of RF are output, and the training data are used to train the RF algorithm to construct the MD-IQPSO-RF transformer fault diagnosis model. The experimental results show that the model achieves an average accuracy of 93.631% for fault diagnosis, which is 6.92% higher than the unoptimized RF model. Comparison with other similar methods also achieved good results, which further validated the effectiveness of the fault diagnosis model.