Predicting Transformers Oil Parameters (original) (raw)
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A cascade of artificial neural networks to predict transformers oil parameters
IEEE Transactions on Dielectrics and Electrical Insulation, 2009
In this paper artificial neural networks have been constructed to predict different transformers oil parameters. The prediction is performed through modeling the relationship between the insulation resistance measured between distribution transformers high voltage winding, low voltage winding and the ground and the breakdown strength, interfacial tension acidity and the water content of the transformers oil. The process of predicting these oil parameters statuses is carried out using various configurations of neural networks. First, a multilayer feed forward neural network with a back-propagation learning algorithm was implemented. Subsequently, a cascade of these neural networks was deemed to be more promising, and four variations of a three stage cascade were tested. The first configuration takes four inputs and outputs four parameter values, while the other configurations have four neural networks, each with two or three inputs and a single output; the output from some networks are pipelined to some others to produce the final values. Both configurations are evaluated using real-world training and testing data and the accuracy is calculated across a variety of hidden layer and hidden neuron combinations. The results indicate that even with a lack of sufficient data to train the network, accuracy levels of 84% for breakdown voltage, 95% for interfacial tension, 56% for water content, and 75% for oil acidity predictions were obtained by the cascade of neural networks.
Energies
In this work, the application of a feed-forward artificial neural network (FFANN) in predicting the degree of polymerization (DP) and loss of life (LOL) in oil-submerged transformers by using the solid insulation evaluation method is presented. The solid insulation evaluation method is a reliable technique to assess and predict the DP and LOL as it furnishes bountiful information in examining the transformer condition. Herein, two FFANN models are proposed. The first model is based on predicting the DP when only the 2-Furaldehyde (2FAL) concentration measured from oil samples is available for new and existing transformers. The second FFANN model proposed is based on predicting the transformer LOL when the 2FAL and DP are available to the utility owner, typically for the transformer operating at a site where un-tanking the unit is a daunting and unfeasible task. The development encompasses constructing numerous FFANN designs and picking networks with superlative performance. The trai...
Artificial neural network-based prediction technique for transformer oil breakdown voltage
Electric Power Systems Research, 2004
This paper presents an artificial neural network (ANN)-based modeling technique for prediction of transformer oil breakdown voltage. This model comprises transformer oil service period, total acidity and water content while preserving the nonlinear relationship between their combinations for predicting transformer oil breakdown voltage. The model results are compared with those obtained by various modeling techniques such as ANN-based model for transformer oil breakdown voltage as a function of its service period, a polynomial regression model for transformer oil breakdown voltage as a function of its service period and a multiple linear regression model for transformer oil breakdown voltage as a function of its total acidity, water content and service period. A quantitative analysis of various modeling techniques has been carried out using different evaluation indices; namely, mean absolute percentage error and actual percentage error at each service period. The results showed the effectiveness and capability of the proposed ANN-based modeling technique to predict transformer oil breakdown voltage and justified its accuracy.
Electrical Engineering, 2018
The insulating oil performance could be enhanced in high-voltage apparatus using barriers. The importance of the barrier in increasing the dielectric strength of the insulating oils in order to reduce the oil failure stresses had not been sufficiently studied. In this paper, the effects of the barrier variables on the insulation performance of the transformer oil for point-plate and plate-plate gaps were demonstrated. These variables are: gap space (d), the barrier location relative to the high-voltage electrode (a/d) %, barrier diameter (D), barrier thickness (e), electrode configurations (EC), the presence of contaminating particles, the weight of the contaminating particles (W) and the temperature of the insulating oil (T). The statistical t test was used to explain whether the effect of these parameters was significant or not. Furthermore, the above-mentioned variables were used as training variables to construct the prediction model of oil breakdown voltage considering barrier effect based on the artificial neural networks (ANN). The ANN model was developed based on the results from experimental works. Therefore, 784 samples were used as training data set and other 25 samples were taken as testing and validating samples. The results explained that the prediction ANN model had a high ability to expect the breakdown voltage for other different experiment cases. The average errors of the training and testing samples were 1.6%, and 2.66%, respectively. Therefore, the prediction accuracy could be considered as 98.4% for training and 97.34% for testing. Keywords Barriers • Breakdown voltage • Oil dielectric insulation • Artificial neural networks (ANN)
Estimating the Vital Parameters in Transformer Oil Using Soft Computing Technique
Power transformers are the costliest equipment in power system. Transformer may get failed by the failure of insulation system. Monitoring the transformer is essential to keeping continuity in power distribution. Goal of presented work is to predict the transformer oil critical parameters with low cost for monitoring purpose of transformer. In this project one of the soft computing technique, artificial neural network have been constructed to predict different critical transformer oil parameters. The prediction is performed through modeling the relationship between the predictable parameters and critical parameters. The process of predicting these oil parameters statuses is carried out using various configurations of neural networks. First, a multilayer feed forward neural network with a back-propagation learning Algorithm was implemented. Subsequently, a cascade of these neural networks was deemed to be more promising according to the correlation between the parameters.
2011
This paper presents two modeling techniques for the prediction and monitoring of the characteristics of transformer oil. The first employs artificial neural network (ANN) and the second employs non-linear modeling (nlm). The proposed techniques are implemented for predicting the transformer oil residual operating time (t rot) which is defined as the service period after which the breakdown voltage (BDV) violates the limits given in the standard specifications. The selection of the most influential characteristics on residual operating time (t rot) in the proposed techniques is obtained by statistical analysis. The non-linear model depends on linear combination of non-linear functions for each characteristic. The ANN technique for modeling these characteristics preserves the non-linear relationship between these characteristics and (t rot). The results are compared with previously published modeling techniques namely multiple linear regression and polynomial regression models. Different evaluation indices have been used to justify the superiority of the proposed modeling techniques for predicting (t rot).
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.
Development of ANN and AFIS Models for Age Prediction of in-Service Transformer Oil samples
Power transformer is one of the most important and expensive equipment in electrical network. The transformer oil is a very important component of power transformers. It has twin functions of cooling as well as insulation. The oil properties like viscosity, specific gravity, flash point, oxidation stability, total acid number, breakdown voltage, dissipation factor, volume resistivity and dielectric constant suffer a change with respect to time. Hence it is necessary that the oil condition be monitored regularly to predict, if possible, the remaining lifetime of the transformer oil, from time to time. Six properties such as moisture content, resistivity, tan delta, interfacial tension and flash point have been considered. The data for the six properties with respect to age, in days, has been taken from literature, whereby samples of ten working power transformers of 16 to 20 MVA installed at different substations in Punjab, India have been considered. This paper aims at developing ANN and ANFIS models for predicting the age of in-service transformer oil samples. Both the the models use the six properties as inputs and age as target. ANN (Artificial Neural Network) model uses a multi-layer feedforward network employing back propagation algorithm, and ANFIS (Adaptive Neuro Fuzzy Inference System) model is based on Sugeno model. The two models have been simulated for estimating the age of unknown transformer oil samples taken from generator transformers of Anpara Thermal Power Project in state of U.P. India. A comparative analysis of the two models has been made whereby ANFIS model has been found to yield better results than ANN model.
Energies
The life expectancy of power transformers is primarily determined by the integrity of the insulating oil and cellulose paper between the conductor turns, phases and phase to earth. During the course of their in-service lifetime, the solid insulating system of windings is contingent on long-standing ageing and decomposition. The decomposition of the cellulose paper insulation is strikingly grievous, as it reduces the tensile strength of the cellulose paper and can trigger premature failure. The latter can trigger premature failure, and to realize at which point during the operational life this may occur is a daunting task. Various methods of estimating the DP have been proposed in the literature; however, these methods yield different results, making it difficult to accurately estimate a reliable DP. In this work, a novel approach based on the Feedforward Backpropagation Artificial Neural Network has been proposed to predict the amount of DP in transformer cellulose insulation. Prese...
Transformer Oil Degradation Prediction Using Machine Learning
International journal of engineering research and technology, 2021
Transformer oil is a special type of insulating oil used to maintain and increase the life time of electrical transformers. It gets degraded over the years due to several reasons. Typically, testing of transformer oils happens physically in specialized labs, which can be expensive and time consuming. The aim of our work is to predict the degradation of transformer oil using mathematical approaches, to expedite the process and save the expenses of physical testing. In this research, we analyze the various parameters of transformer oil and correlate them to the extent of degradation using Machine Learning and Deep Learning algorithms. I.