Application of Artificial Intelligence Techniques for Dissolved Gas Analysis of Transformers-A Review (original) (raw)
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International Journal of Computer and Electrical Engineering, 2011
Transformers are a critical part of an electrical utility's asset base. On-line monitoring and diagnostics is a useful tool to help operators to manage their assets and make decisions on continuing operation, maintenance or replacement. Dissolved Gas Analysis (DGA) is the heart of on-line monitoring as it is a well-established method of transformer diagnosis. DGA techniques are simple, inexpensive, and widely used to interpret gases dissolved due to the deterioration of the insulating oil of power transformers and hence to diagnosis, possibility of various type of faults in power transformer. Various diagnostic criteria based on gas analysis have been developed. In this paper, the application of many AI techniques have been presented such as Artificial Neural Network (AAN), Fuzzy Interface System (FIS), Genetic Algorithm (GA), Extended Relation Function (ERF), Bayesian Network (BN), Self Organizing Map (SOM) and Discrete Wavelet Network (WNs) Transforms, which can be used to increase the efficient and accurate diagnosis for off line and on line monitoring of power transformers.
IEEE Transactions on Dielectrics and Electrical Insulation, 2018
Transformers are vital components of power systems as they are situated between energy generation and consumers and their failure disrupts the use of electrical energy. Therefore, diagnosing an incipient fault is essential in avoiding hazardous operating conditions and minimizes downtime cost. In transformers, faults take place due to electrical or thermal stresses that cause insulation decomposition in transformers. In oil-filled transformers, insulations are cellulose and oil, and the products of the insulation decomposition are gases which can be dissolved in the oil. Therefore, dissolved gas analysis (DGA) can be used for fault diagnosis in oil filled transformers. In this paper, DGA interpretation methods, conventional and intelligence, are investigated and compared. For evaluating consistency and accuracy of the methods, "No Result" cases are not considered. It can help the newcomers to this field to have access to a comprehensive comparison about the application of computational intelligence and conventional methods in transformer fault detection using DGA.
Energies
In South Africa, the growing power demand, challenges of having idle infrastructure, and power delivery issues have become crucial problems. Reliability enhancement necessitates a life-cycle performance analysis of the electrical power transformers. To attain reliable operation and continuous electric power supply, methodical condition monitoring of the electrical power transformer is compulsory. Abrupt breakdown of the power transformer instigates grievous economic detriment in the context of the cost of the transformer and disturbance in the electrical energy supply. On the condition that the state of the transformer is appraised in advance, it can be superseded to reduced loading conditions as an alternative to unexpected failure. Dissolved gas analysis (DGA) nowadays has become a customary method for diagnosing transformer faults. DGA provides the concentration level of various gases dissolved, and consequently, the nature of faults can be predicted subject to the concentration ...
Dissolved Gas Analysis in Power Transformer using Artificial Neural Network
Power transformers plays most crucial in power system. It transfers of power from one voltage level to another. Power transformer breakdown or damage may interrupt power transmission & distribution operation Under continuous operation confront to the electrical and thermal stresses. Fault in the power transformer may lead to the power supply interruption The Dissolved Gas Analysis to detect incipient faults has been improved using artificial neural networks and is compared with Rogers ratio method with available samples of field information. Dissolved gas analysis (DGA) is a reliable technique for detecting the presence of incipient fault conditions in oil immersed transformers. In this method the presence of certain key gases is monitored. The various analysis methods are : Rogers ratio, IEC ratio, Doernenburg, Duval triangle, key gas, artificial neural network (ANN) method. In this paper the various DGA methods are evaluated and compared. The key gases considered are hydrogen, methane, ethane, ethylene, acetylene.
Power transformer fault diagnosis based on dissolved gas analysis by artificial neural network
2012 First International Conference on Renewable Energies and Vehicular Technology, 2012
This paper present a methodology for power transformers diagnosis using a fuzzy neural network approach. In the development of the system and neural network training was used a real data base of gases concentration in power transformers supplied by Energetic Company of Minas Gerais (CEMIG).
COMPUTATIONAL RESEARCH PROGRESS IN APPLIED SCIENCE & ENGINEERING, 2020
This paper presents the development of a potential fault diagnosis system of power transformers by an artificial neural network (ANN) based on the gas components of dissolved gas-in-oil analysis (DGA) data. The input of the ANN is five components H2, C2H4, CH4, C2H2, C2H6. The outputs are 3 major conclusions about the condition of the transformer including “normal”, “overheating” and “discharging”. Using Multi-Layer Perception network (MLP) with a selected network structure of 5-16-3. Through testing with actual DGA data, the results show that the diagnostic system makes conclusions that are reliable.
2004
Power transformer is a vital component of power system, which has no substitute for its major role. They are quite expensive also. It is therefore, very important to closely monitor it's in -service behavior to avoid costly outages and loss of production. Many devices have evolved to monitor the serviceability of power transformers. These devices such as Buchholz relay or differential relay respond only to a severe power failure requiring immediate removal of transformer from service, in which case, outages are inevitable. Thus, preventive techniques for early detection of faults to avoid outages would be valuable.
ANN Application in DGA Methods for the Detection of Incipient Faults in Oil-Filled Power Transformer
Power transformer is one of the fundamental equipments in the power system. Transformer breakdown or damage may interrupt power distribution and transmission operation, as well as incur high repair cost. Thus, detection of incipient faults in power transformer is essential and it has become an interesting topic to study. This paper presents the application of artificial neural network (ANN) in detecting incipient faults in power transformers by using dissolved gas analysis (DGA) technique. DGA is a reliable technique to detect incipient faults as it provides wealth of information in analyzing transformer condition. For this project, ANN was developed to classify seven types of transformer condition based on three combustible gas ratios. The development involves constructing several ANN designs and selecting network with the best performance. The gas ratio are based on IEC 60599 (2007) standard while historical data were used in the training and testing processes. The selected ANN design yields a very satisfactory result where it can make a reliable classification of transformer condition with respect to combustible gas generated.