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Academic literature on the topic 'Doernenburg ratio method'
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Journal articles on the topic "Doernenburg ratio method"
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Mohamed, Siti Hajar, Ab Halim Abu Bakar, and Mohd Syukri Ali. "Comparative Study of DGA for Transformer Service Life." International Journal of Renewable Energy Resources 11, no. 1 (2022): 13–26. http://dx.doi.org/10.22452/ijrer.vol11no1.2.
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This paper investigates the accuracy and reliability of each DGA method via dissolved gases. Key Gas Method, Doernenburg Ratio Method, Rogers Ratio Method, IEC Ratio Method, and Duval Triangle Method are used to test the 100 sample units. The results obtained are Doernenburg and IEC 94% accuracy each, Duval Triangle 80% accuracy, Rogers 79% accuracy, and Key Gas 75% accuracy. The said individual ratio methods are combined to proceed with newly developed hybrid testing methods that could probably improve the existing DGA methods. The first hybrid method is a combination between Doernenburg and Rogers which produced a slight improvement of 86% accuracy. Then a combination between Doernenburg and IEC delivered a stable prediction result of 94%. It seems that Doernenburg and IEC are the most accurate DGA methods. Although both methods are combined, they still produced a constant accuracy result. While the other methods, such as Key Gas, Rogers, and Duval Triangle, do not achieve satisfactory results.
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Karel, Octavianus Bachri, Khayam Umar, Anggoro Soedjarno Bambang, Datumaya Wahyudi Sumari Arwin, and Suwandi Ahmad Adang. "Cognitive artificial-intelligence for doernenburg dissolved gas analysis interpretation." TELKOMNIKA Telecommunication, Computing, Electronics and Control 17, no. 1 (2019): 268–74. https://doi.org/10.12928/TELKOMNIKA.v17i1.11612.
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This paper proposes Cognitive Artificial Intelligence (CAI) method for Dissolved Gas Analysis (DGA) interpretation adopting Doernenburg Ratio method. CAI works based on Knowledge Growing System (KGS) principle and is capable of growing its own knowledge. Data are collected from sensors, but they are not the information itself, and thus, data needs to be processed to extract information. Multiple information are then fused in order to obtain new information with Degree of Certainty (DoC). The new information is used to identify faults occurred at a single observation. The proposed method is tested using the previously published dataset and compared with Fuzzy Inference System (FIS) and Artificial Neural Network (ANN). Experiment shows CAI implementation on Doernenburg Ratio performs 115 out of 117 accurate identification, followed by Fuzzy Inference System 94.02% and ANN 78.6%. CAI works well even with small amount of data and does not require trainings.
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Khoirudin, Sukarman Sukarman, Dodi Mulyadi, et al. "Analysis of Transformer Oil Post-Flashover: DGA Testing and Diagnostic Approached." Jurnal Teknik Mesin Mechanical Xplore 4, no. 2 (2024): 74–85. http://dx.doi.org/10.36805/jtmmx.v4i2.6093.
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Transformer oil (TO) serves as a cooling fluid and insulation medium in transformers. One cause of the decline in the quality of TO is flashover, leading to overheating of the oil inside the transformer. Flashovers, which are sudden electrical discharges in transformers, can lead to the generation of gases within the insulating oil. Understanding the changes in gas content is crucial for assessing the health and condition of the transformer. Gas analysis was conducted using the Total Dissolved Combustible Gas (TDCG), Doernenburg and Roger’s ratio method, focusing on gases extracted from both transformer oil and the gas space. The results provide valuable insights into the effects of flashovers on gas production and aid in the diagnosis of potential issues within the transformer. The TDCG values for all cycles are higher than those for the original oil. This is due to the flashover simulation using BDV testing, causing a change in the gas values contained in the TO. Based on the TDCG results the transformer is in condition I. If this occurs during actual transformer operation, the transformer can continue normal operation with certain considerations, namely, exercising caution, analyzing for individual gases, and determining load dependence. Both analyses using the Doernenburg and Roger's ratio method indicate "No Fault." Therefore, if flashover simulation is conducted using the BDV test, it will cause a change in gas content in the oil but will not lead to anything fatal.
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Wajid, Abdul, Atiq Ur Rehman, Sheeraz Iqbal, et al. "Comparative Performance Study of Dissolved Gas Analysis (DGA) Methods for Identification of Faults in Power Transformer." International Journal of Energy Research 2023 (September 25, 2023): 1–14. http://dx.doi.org/10.1155/2023/9960743.
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The power transformer is an essential component of the electrical network that can be used to step up and step down voltage. Dissolved gas analysis (DGA) is the most reliable method for the identification of incipient faults in power transformers. Various DGA methods are used to observe the generated key gases after oil decomposition. The main gases included are hydrogen (H2), ethylene (C2H4), acetylene (C2H2), methane (CH4), and ethane (C2H6). There is a lack of research that can compare the performance of various DGA methods in identification of faults in power transformer. In addition, it is also not clear which DGA method is optimal for identification of faults in power transformer. In this paper, the comparative performance study of seven DGA methods such as Roger’s ratio, key gas, IEC ratio, the Doernenburg ratio, the Duval triangle, three-ratio method, and the relative percentage of four gases is carried out in order to identify the optimal technique for fault identification in transformer. The data of various power transformers installed in “RAWAT” NTDC grid station, Islamabad, and “UCH-II” power station, Balochistan, are considered for the comparative analysis. This analysis shows that the three-ratio method provides better performance than other DGA methods in accurately identifying the faults in power transformers. The three-ratio method has 90% accuracy in identifying the faults in power transformer.
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Norazhar, Abu Bakar, Sutan Chairul Imran, Ab Ghani Sharin, Shahril Ahmad Khiar Mohd, and Zamri Che Wanik Mohd. "Improvement of transformer dissolved gas analysis interpretation using J48 decision tree model." International Journal of Artificial Intelligence (IJ-AI) 12, no. 1 (2023): 48–56. https://doi.org/10.11591/ijai.v12.i1.pp48-56.
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Dissolved gas analysis (DGA) is widely accepted as an effective method to detect incipient faults within power transformers. Gases such as hydrogen, methane, acetylene, ethylene and ethane are normally utilized to identify the transformer fault conditions. Several techniques have been developed to interpret DGA results such as the key gas method, Doernenburg, Rogers, International Electro Technical Commission (IEC) ratio-based methods, Duval triangles, and the latest Duval pentagon methods. However, each of these approaches depends on the experts' shared knowledge and experience rather than quantitative scientific methods, therefore different diagnoses may be reported for the same oil sample. To overcome these shortcomings, this paper proposed the use of decision tree method to interpret the transformer health condition based on DGA results. The proposed decision tree model employed three main fault gases; methane, acetylene, ethylene as inputs, and classified the transformer into eight fault conditions. The J48 algorithm is used to train and developed the decision tree model. The performance of the proposed model is validated with the pre-known condition of transformers and compared with the Duval triangle method (DTM). Results show that the proposed model delivers better precision and accuracy in predicting transformer fault conditions compared to DTM with 81% and 69% respectively.
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Souvannalath, Phoumsavath, Suttichai Premrudeepreechacharn, and Kanchit Ngamsanroaj. "REVOLUTIONIZING POWER TRANSFORMER FAULT DIAGNOSIS THROUGH COGNITIVE ARTIFICIAL INTELLIGENCE AND DISSOLVED GAS ANALYSIS INTEGRATION." ASEAN Engineering Journal 14, no. 3 (2024): 1–14. http://dx.doi.org/10.11113/aej.v14.19506.
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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.
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Bakar, Norazhar Abu, Imran Sutan Chairul, Sharin Ab Ghani, Mohd Shahril Ahmad Khiar, and Mohd Zamri Che Wanik. "Improvement of transformer dissolved gas analysis interpretation using j48 decision tree model." IAES International Journal of Artificial Intelligence (IJ-AI) 12, no. 1 (2023): 48. http://dx.doi.org/10.11591/ijai.v12.i1.pp48-56.
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<span lang="EN-US">Dissolved gas analysis (DGA) is widely accepted as an effective method to detect incipient faults within power transformers. Gases such as hydrogen, methane, acetylene, ethylene and ethane are normally utilized to identify the transformer fault conditions. Several techniques have been developed to interpret DGA results such as the key gas method, Doernenburg, Rogers, IEC ratio-based methods, Duval Triangles, and the latest Duval Pentagon methods. However, each of these approaches depends on the experts' shared knowledge and experience rather than quantitative scientific methods, therefore different diagnoses may be reported for the same oil sample. To overcome these shortcomings, this paper proposed the use of decision tree method to interpret the transformer health condition based on DGA results. The proposed decision tree model employed three main fault gases; methane, acetylene, ethylene as inputs, and classified the transformer into eight fault conditions. The J48 algorithm is used to train and developed the decision tree model. The performance of the proposed model is validated with the pre-known condition of transformers and compared with the Duval Triangle method. Results show that the proposed model delivers better precision and accuracy in predicting transformer fault conditions compared to DTM with 81% and 69% respectively.</span>
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Al-Sakini, Sahar R., Ghassan A. Bilal, Ahmed T. Sadiq, and Wisam Abed Kattea Al-Maliki. "Dissolved Gas Analysis for Fault Prediction in Power Transformers Using Machine Learning Techniques." Applied Sciences 15, no. 1 (2024): 118. https://doi.org/10.3390/app15010118.
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Power transformers are one of the most important elements of electrical power systems. The fast diagnosis of transformer faults improves the efficiency of power systems. One of the most favored methodologies for transformer fault diagnostics is based on dissolved gas analysis (DGA) techniques, including the Duval triangle method (DTM), the Doernenburg ratio method (DRM), and the Rogers ratio method (RRM), which are suitable for on-line diagnosis of transformers. The imbalanced, insufficient, and overlapping state of gas-decomposed DGA datasets, however, remains a limitation to the deployment of a powerful and accurate diagnosis approach. This study presents a new approach for transformer fault diagnosis based on DGA, one which aims to improve the performance evaluation criteria to predict current faults and to lower the associated costs. We used six optimized machine learning methods (MLMs) for dataset transformation to organize the dataset. The MLMs used for transformer fault diagnosis were random forest (RF), backpropagation neural network (BPNN), K-nearest neighbors (KNN), support vector machine (SVM), decision tree (DT), and Naive Bayes (NB). The MLMs were implemented by using 628 dataset samples, which were obtained from laboratories, other studies, and electricity stations in Iraq. Accordingly, 502 dataset samples constituted the training set while the remaining 126 dataset samples served as the testing set. The results were examined according to six important measurements (accuracy ratio, precision, recall, specificity, F1 score, and Matthews correlation coefficient (MCC)). The best results were found for case A with RF (95.2%). In cases B and C, the best results were found for RF and DT (100% and 99.2%, respectively). With respect to the advanced machine learning method, the transformer fault diagnosis based on the MLMs was exceedingly more accurate in its predictions than the conventional and artificial intelligence-based methods.
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Ravi, Dayyala. "Condition Monitoring of High Voltage Transformer using Dissolved Gas Analysis Methods." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (2021): 1759–70. http://dx.doi.org/10.22214/ijraset.2021.35374.
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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.
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Okhlopkov, A. V., and V. D. Bitney. "Confirmation of necessity and applicability of using various methods of interpretation of results of gas chromatographic analysis of power transformers." Vestnik IGEU, no. 4 (August 31, 2023): 18–27. http://dx.doi.org/10.17588/2072-2672.2023.4.018-027.
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The method of analysis of gases dissolved in oil is one of the most informative methods of early detection of defects in power oil-filled transformers. Now, the decision on the state of the transformers is based on the method of interpretation of the results of the gas chromatographic (GC) according to the guideline document RD 153-34.0-46.302-00. At the same time, there are situations when this document does not provide accurate analysis results. Thus, it is proposed to use several methods of interpreting the results of the GC to obtain refined conclusions. The purpose of the study is to substantiate the need to use various methods of interpretation of the results of gas chromatographic analysis of the oil of power transformers. The following methods for dissolved gas analysis have been reviewed: Rogers Ratio Method, IEC 60599 Standard Method, Doernenburg Ratio Method, Duval Triangle Method, ETRA method, as well as the guideline document RD 153-34.0-46.302-00 method adopted in the Russian Federation. These methods are implemented in various power companies of the Russian Federation, such as PJSC “Rosseti MR”, PJSC “FGC UES” and PJSC “Mosenergo”. The article reveals the need to consider the totality of all available methods and techniques based on RD 153-34.0-46.302-00 and development of training samples. The scientific novelty and significance of the conducted research lies in the confirmation of the need to use a set of methods for interpreting the results of the GC. An algorithm for the complex application of the methods described in the article for interpreting the results of the GC and training samples has been formed. The obtained results allow us to consider the possibility to develop software for the complex application of the methods of interpretation of the results of the GC described in the article and the formation of training samples based on the developed algorithm.
Conference papers on the topic "Doernenburg ratio method"
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Yaqin, Elko Nurul, and Umar Khayam. "Improvement of Application Cognitive Artificial Intelligence based on Doernenburg Ratio Method for Dissolved Gas Analysis Interpretation." In 2021 International Conference on Electrical Engineering and Informatics (ICEEI). IEEE, 2021. http://dx.doi.org/10.1109/iceei52609.2021.9611122.
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