Statistical Machine Learning and Dissolved Gas Analysis: A Review (original) (raw)
Related papers
International Journal of Electrical and Computer Engineering, 2010
The gases generated in oil filled transformers can be used for qualitative determination of incipient faults. The Dissolved Gas Analysis has been widely used by utilities throughout the world as the primarily diagnostic tool for transformer maintenance. In this paper, various Artificial Intelligence Techniques that have been used by the researchers in the past have been reviewed, some conclusions have been drawn and a sequential hybrid system has been proposed. The synergy of ANN and FIS can be a good solution for reliable results for predicting faults because one should not rely on a single technology when dealing with real–life applications. Keywords—Dissolved Gas Analysis, Artificial Intelligence Techniques, Incipient Faults, Transformer Fault Diagnosis, and Hybrid Systems.
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.
A Multinomial DGA Classifier for Incipient Fault Detection in Oil-Impregnated Power Transformers
Algorithms
This study investigates the use of machine-learning approaches to interpret Dissolved Gas Analysis (DGA) data to find incipient faults early in oil-impregnated transformers. Transformers are critical pieces of equipment in transmitting and distributing electrical energy. The failure of a single unit disturbs a huge number of consumers and suppresses economic activities in the vicinity. Because of this, it is important that power utility companies accord high priority to condition monitoring of critical assets. The analysis of dissolved gases is a technique popularly used for monitoring the condition of transformers dipped in oil. The interpretation of DGA data is however inconclusive as far as the determination of incipient faults is concerned and depends largely on the expertise of technical personnel. To have a coherent, accurate, and clear interpretation of DGA, this study proposes a novel multinomial classification model christened KosaNet that is based on decision trees. Actual...
Machine learning techniques for power transformer insulation diagnosis
2011
Power transformers are one of the most critical equipments in electricity network. A number of techniques such as dissolved gas analysis (DGA), polarization and depolarization currents (PDC) measurement and frequency domain spectroscopy (FDS) have been adopted across utilities for transformer insulation diagnosis. However, there are still considerable challenges remaining in interpreting measured data of these techniques. This paper develops machine learning algorithms, which utilise archived data for making insulation diagnosis on the transformer of interest. Analysis and interpretation of field test data are presented in the paper.
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.
Electronics
Dissolved gas analysis (DGA) is one of the regular routine tests accepted by worldwide utilities to detect power transformer incipient faults. While the DGA measurement has fully matured since the development of offline and online sensors, interpretation of the DGA results still calls for advanced approaches to automate and standardize the process. Current industry practice relies on various interpretation techniques that are reported to be inconsistent and, in some cases, unreliable. This paper presents a new application for the advanced logistic regression algorithm to improve the reliability of the DGA interpretation process. In this regard, regularized logistic regression is used to improve the accuracy of the DGA interpretation process. Results reveal the superior features of the proposed logistic regression approach over the conventional and artificial intelligence techniques presented in the literature.
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 ...
2009
In this paper, Artificial Neural Networks are used to solve a complex problem concerning to power transformers and characterized by non-linearity and hard dynamic modeling. The operation conditions and integrity of a power transformer can be detected by analysis of physical-chemical and chromatographic isolating oil, allowing establish procedures for operating and maintaining the equipment. However, while the costs of physicalchemical tests are smaller, the chromatographic analysis is more informative. This work presents an estimation study of the information that would be obtained in the chromatographic test from the physical-chemical analysis through Artificial Neural Networks. Thus, the power utilities can achieve greater reliability in the prediction of incipient failures at a lower cost. The results show this strategy to be a promising, with accuracy of 100% in best cases. The application in the thermal fault diagnosis presents more than 91% accuracy in best cases.
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.
The search for determining accurate faults and assessing the oil quality of high voltage electrical power transformers for life-long maintenance is ever-demanding. The durability of transformers function is significantly decided by the excellence of its insulation which deteriorates over time due to temperature fluctuations and moisture contents. The accurate diagnoses of faults in early stages and the efficient assessment of oil quality using an intelligent program is the key challenges in protecting transformers from potential failures occur during operation to avoid economic losses. The dissolved gases analysis in oil is a reliable method in the diagnosis of faults and assessing the quality of insulating oil in transformers. Recently, application of artificial intelligence (AI) has included fuzzy logic, expert system (EPS), and artificial neural network (ANN), Expert system and fuzzy logic can take DGA standards. This paper represents the review most of the methods used to diagnose faults and assessment of insulating oil for transformers through the dissolved gases analysis DGA.