LSTM networks for the trend prediction of gases dissolved in power transformer insulation oil

2018 ◽  
Author(s):  
Jiejie Dai ◽  
Hui Song ◽  
Gehao Sheng ◽  
Xiuchen Jiang
Keyword(s):  
Power Transformer ◽  
Trend Prediction ◽  
Transformer Insulation ◽  
Insulation Oil

scholarly journals DGA Method-Based ANFIS Expert System for Diagnosing Faults and Assessing Quality of Power Transformer Insulation Oil

2015 ◽  
Vol 10 (1) ◽  
pp. 13 ◽  
Author(s):  
Mohd Muhridza Bin Yaacob ◽  
Ahmed Raisan Hussein ◽  
Mohd Fauzi Bin Othman
Keyword(s):  
Expert System ◽  
Power Transformer ◽  
Oil Quality ◽  
Gas Analysis ◽  
Ratio Method ◽  
Economic Losses ◽  
Intelligent Software ◽  
Transformer Insulation ◽  
Insulation Oil

<p class="zhengwen"><span lang="EN-GB">Accurate fault diagnostics and assessment of electrical power transformer insulation oil for lifelong endurance are the key issues addressed in this research. The durability of a transformer is significantly determined by the quality of its insulation oil, which deteriorates over time due to temperature fluctuations and moisture content. Protecting transformers from potential failure through early and precise diagnosis of faults and through efficient assessment of oil quality during the actual conduct of the operation can avoid sizeable economic losses. The ANFIS Expert System that uses intelligent software plays an important role in this regard. The dissolved gas analysis (DGA) in oil is a reliable method for diagnosing faults and assessing insulation oil quality in transformers. The safeguarding teams of transformer power stations often suffer from the occurrence of sudden faults, which result in severe damages and heavy monetary loss. The oil in transformers must be appropriately treated to circumvent such failures. In this research, an ANFIS Expert System was used to diagnose faults and to assess the status and quality of insulation oil in power transformers. A suitable treatment was identified using the Rogers ratio method depending on the DGA in oil. The graphical user interface from the MATLAB environment was used and proven effective for fault diagnosis and oil quality evaluation. The training algorithm is capable of assessing oil quality according to the specifications of the IEEE standard C57-104 and the IEC standard 60599.</span></p>

Enhanced optimal trained hybrid classifiers for aging assessment of power transformer insulation oil

2020 ◽  
Vol 17 (3) ◽  
pp. 407-426
Author(s):  
Harkamal Deep Singh ◽  
Jashandeep Singh
Keyword(s):  
Prediction Models ◽  
Power Transformer ◽  
Electrical Equipment ◽  
Diagnostic Techniques ◽  
Assessment Model ◽  
Content Type ◽  
Lion Algorithm ◽  
Hybrid Classifiers ◽  
Transformer Insulation ◽  
Insulation Oil

Purpose As a result of the deregulations in the power system networks, diverse beneficial operations have been competing to optimize their operational costs and improve the consistency of their electrical infrastructure. Having certain and comprehensive state assessment of the electrical equipment helps the assortment of the suitable maintenance plan. Hence, the insulation condition monitoring and diagnostic techniques for the reliable and economic transformers are necessary to accomplish a comprehensive and proficient transformer condition assessment. Design/methodology/approach The main intent of this paper is to develop a new prediction model for the aging assessment of power transformer insulation oil. The data pertaining to power transformer insulation oil have been already collected using 20 working power transformers of 16-20 MVA operated at various substations in Punjab, India. It includes various parameters associated with the transformer such as breakdown voltage, moisture, resistivity, tan δ, interfacial tension and flashpoint. These data are given as input for predicting the age of the insulation oil. The proposed aging assessment model deploys a hybrid classifier model by merging the neural network (NN) and deep belief network (DBN). As the main contribution of this paper, the training algorithm of both NN and DBN is replaced by the modified lion algorithm (LA) named as a randomly modified lion algorithm (RM-LA) to reduce the error difference between the predicted and actual outcomes. Finally, the comparative analysis of different prediction models with respect to error measures proves the efficiency of the proposed model. Findings For the Transformer 2, root mean square error (RMSE) of the developed RM-LA-NN + DBN was 83.2, 92.5, 40.4, 57.4, 93.9 and 72 per cent improved than NN + DBN, PSO, FF, CSA, PS-CSA and LA-NN + DBN, respectively. Moreover, the RMSE of the suggested RM-LA-NN + DBN was 97.4 per cent superior to DBN + NN, 96.9 per cent superior to PSO, 81.4 per cent superior to FF, 93.2 per cent superior to CSA, 49.6 per cent superior to PS-CSA and 36.6 per cent superior to LA-based NN + DBN, respectively, for the Transformer 13. Originality/value This paper presents a new model for the aging assessment of transformer insulation oil using RM-LA-based DBN + NN. This is the first work uses RM-LA-based optimization for aging assessment in power transformation insulation oil.

scholarly journals Aging Assessment of Power Transformer Insulation Oil using Hybrid Meta-Heuristic Trained Artificial Neural Network

2019 ◽  
Vol 9 (1) ◽  
pp. 7115-7128
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Search Algorithm ◽  
Power Transformer ◽  
Particle Swarm ◽  
Transformer Oil ◽  
Training Algorithm ◽  
Artificial Neural ◽  
Transformer Insulation ◽  
Insulation Oil

In today's economic state, power transformer remains as the most expensive equipment in electrical system, in which insulation oil has been taken a significant role for performing a prominent operation. Since the insulation oil happens to degrade soon due to aging, high temperature and chemical reactions such as the oxidation, the periodic checking of oil followed by its replacement is necessary to stop the unexpected failure of the transformer. Moreover, it will be very advantageous if it happens to implement an automated model for predicting the age of transformer oil from time to time. The main intent of this paper is to develop an age assessment framework of transformer insulation oil using intelligent approaches. Here, diverse parameters associated with the transformer such as Breakdown Voltage (BDV), moisture, resistivity, tan delta, interfacial tension, and flash point is given as input for predicting the age of the insulation oil. These data have been already collected using 20 working power transformers operated at various substations in Punjab, India. In the proposed model, the collected parameters are subjected to a well-performing machine learning algorithm termed as Artificial Neural Network (ANN) in order to predict the age of the insulation oil. As a main contribution, the existing training algorithm in ANN so called as Levenberg–Marquardt (LM) is replaced by a hybrid metaheuritics algorithm. The newly developed hybrid algorithm merges the idea of Crow Search Algorithm (CSA), and Particle Swarm Optimization (PSO), and the new algorithm is termed as Particle Swarm-based Crow Search Algorithm (PS-CSA). The new training algorithm optimizes the weight of ANN using the hybrid CS-PSO updating procedure, in such a way that the difference between the predicted and actual outcome is minimum. Hence, this age prediction of transformer insulation oil will be beneficial for the environs to avoid the drastic condition.

scholarly journals Alternative liquid dielectrics in power transformer insulation: a review

2021 ◽  
Vol 23 (3) ◽  
pp. 1761
Author(s):  
Bokang Agripa Tlhabologo ◽  
Ravi Samikannu ◽  
Modisa Mosalaosi
Keyword(s):  
Power Transformer ◽  
Chemical Properties ◽  
Fire Risk ◽  
Mineral Oil ◽  
Acid Number ◽  
Gas Analysis ◽  
International Standards ◽  
Liquid Dielectrics ◽  
Transformer Insulation ◽  
Natural Ester

Transformer liquid dielectrics evolved where mineral oil has been the dominant choice until emergence of synthetic esters and natural esters. Natural ester-based oils have been under extensive investigations to enhance their properties for replacing petroleum-based mineral oil, which is non-biodegradable and has poor dielectric properties. This paper focuses on exposition of natural ester oil application in mixed transformer liquid dielectrics. Physical, chemical, electrical, and ageing characteristics of these dielectrics and the dissolved gas analysis (DGA) were reviewed. Physical properties include viscosity, pour point, flash and fire point which are vital indicators of heat insulation and fire risk. Chemical properties considered are water content, acid number, DGA, corrosive sulphur, and sludge content to limit and detect degradation and corrosion due to oil ageing. Electrical properties including breakdown voltage were considered for consistent insulation during overload and fault conditions. These properties of evolving alternative dielectrics were reviewed based on ASTM International standards and International Electro technical Commission standards for acceptable transformer liquid dielectrics. This review paper was compiled to avail modern methodologies for both the industry and scholars, also providing the significance of using mixed dielectrics for power transformers as they are concluded to show superiority over non-mixed dielectrics.

scholarly journals Impact of Accelerated Stresses on Power Transformer Insulation

2017 ◽  
Vol 09 (04) ◽  
pp. 217-231 ◽  
Author(s):  
Jashandeep Singh ◽  
Yog Raj Sood ◽  
Piush Verma
Keyword(s):  
Power Transformer ◽  
Transformer Insulation
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