Multilevel SVM and AI based Transformer Fault Diagnosis using the DGA Data

The Dissolved Gas Analysis (DGA) is utilized as a test for the detection of incipient prob-lems in transformers, and condition monitoring of transformers using software-based diagnosis tools has become crucial. This research uses dissolved gas analysis as an intel-ligent fault classification of a transformer. The Multilayer SVM technique is used to de-termine the classification of faults and the name of the gas. The learned classifier in the multilayer SVM is trained with the training samples and can classify the state as normal or fault state, which contains six fault categories. In this paper, polynomial and Gaussi-an functions are utilized to assess the effectiveness of SVM diagnosis. The results demonstrate that the combination ratios and graphical representation technique is more suitable as a gas signature, and that the SVM with the Gaussian function outperforms the other kernel functions in diagnosis accuracy

Development of a quantitative health index and diagnostic method for efficient asset management of power transformers

Power transformers play a very important role in electrical power networks and are frequently operated longer than their expected design life. Therefore, to ensure their best operating performance in a transmission network, the fault condition of each transformer must be assessed regularly. For an accurate fault diagnosis, it is important to have maximum information about an individual transformer based on unbiased measurements. This can best be achieved using artificial intelligence (AI) that can systematically analyse the complex features of diagnostic measurements. Clustering techniques are a form of AI that is particularly well suited to fault diagnosis. To provide an assessment of transformers, a hybrid k-means algorithm, and probabilistic Parzen window estimation are used in this research. The clusters they form are representative of a single or multiple fault categories. The proposed technique computes the maximum probability of transformers in each cluster to determine their fault categories. The main focus of this research is to determine a quantitative health index (HI) to characterize the operating condition of transformers. Condition assessment tries to detect incipient faults before they become too serious, which requires a sensitive and quantified approach. Therefore, the HI needs to come from a proportionate system that can estimate health condition of transformers over time. To quantify this condition, the General Regression Neural Network (GRNN), a type of AI, has been chosen in this research. The GRNN works well with small sets of training data and avoids the needs to estimate large sets of model parameters, following a largely non-parametric approach. The methodology used here regards transformers as a collection of subsystems and summarizes their individual condition into a quantified HI based on the existing agreed benchmarks drawn from IEEE and CIGRE standards. To better calibrate the HI, it may be mapped to a failure probability estimate for each transformer over the coming year. Experimental results of the research show that the proposed methods are more effective than previously published approaches when diagnosing critical faults. Moreover, this novel HI approach can provide a comprehensive assessment of transformers based on the actual condition of their individual subsystems

Development of a quantitative health index and diagnostic method for efficient asset management of power transformers

Power transformers play a very important role in electrical power networks and are frequently operated longer than their expected design life. Therefore, to ensure their best operating performance in a transmission network, the fault condition of each transformer must be assessed regularly. For an accurate fault diagnosis, it is important to have maximum information about an individual transformer based on unbiased measurements. This can best be achieved using artificial intelligence (AI) that can systematically analyse the complex features of diagnostic measurements. Clustering techniques are a form of AI that is particularly well suited to fault diagnosis. To provide an assessment of transformers, a hybrid k-means algorithm, and probabilistic Parzen window estimation are used in this research. The clusters they form are representative of a single or multiple fault categories. The proposed technique computes the maximum probability of transformers in each cluster to determine their fault categories. The main focus of this research is to determine a quantitative health index (HI) to characterize the operating condition of transformers. Condition assessment tries to detect incipient faults before they become too serious, which requires a sensitive and quantified approach. Therefore, the HI needs to come from a proportionate system that can estimate health condition of transformers over time. To quantify this condition, the General Regression Neural Network (GRNN), a type of AI, has been chosen in this research. The GRNN works well with small sets of training data and avoids the needs to estimate large sets of model parameters, following a largely non-parametric approach. The methodology used here regards transformers as a collection of subsystems and summarizes their individual condition into a quantified HI based on the existing agreed benchmarks drawn from IEEE and CIGRE standards. To better calibrate the HI, it may be mapped to a failure probability estimate for each transformer over the coming year. Experimental results of the research show that the proposed methods are more effective than previously published approaches when diagnosing critical faults. Moreover, this novel HI approach can provide a comprehensive assessment of transformers based on the actual condition of their individual subsystems

Predicting the technical condition of the power transformer using fuzzy logic and dissolved gas analysis method

Power transformers are one of the most important and complex parts of an electric power system. Maintenance is performed for this responsible part based on the technical condition of the transformer using a predictive approach. The technical condition of the power transformer can be diagnosed using a range of different diagnostic methods, for example, analysis of dissolved gases (DGA), partial discharge monitoring, vibration monitoring, and moisture monitoring. In this paper, the authors present a digital model for predicting the technical condition of a power transformer and determining the type of defect and its cause in the event of defect detection. The predictive digital model is developed using the programming environment in LabVIEW and is based on the fuzzy logic approach to the DGA method, interpreted by the key gas method and the Dornenburg ratio method. The developed digital model is verified on a set of 110 kV and 220 kV transformers of one of the sections of the distribution network and thermal power plant in the Russian Federation. The results obtained showed its high efficiency in predicting faults and the possibility of using it as an effective computing tool to facilitate the work of the operating personnel of power enterprises

РОЗПІЗНАВАННЯ РОЗРЯДІВ, ЯКІ СУПРОВОДЖУЮТЬСЯ НИЗЬКОТЕМПЕРАТУРНИМИ ПЕРЕГРІВАМИ ЗА РЕЗУЛЬТАТАМИ АНАЛІЗУ РОЗЧИНЕНИХ У МАСЛІ ГАЗІВ ВИСОКОВОЛЬТНИХ ТРАНСФОРМАТОРІВ

Based on the analysis of test results for 135 high-voltage transformers, ranges of gas percentage, gas ratio values were obtained and nomograms for 10 types of combined defects were made, representing discharges with different intensity which are accompanied by overheating with temperature of 150-300°C. It has been established that in transformers with discharges accompanied by low-temperature overheating the values of CH4/H2, C2H2/CH4, C2H2/C2H6 and C2H2/C2H4 ratios determine the discharge energy, in accordance with the norms regulated by the most known standards, the C2H4/C2H6 ratio varies slightly depending on the hot spot temperature and the C2H6/CH4>1 ratio value. Dynamics of defects nomograms changing in the process of their development is analyzed. It is stated by the analysis results that in majority of cases the primary defect is discharges with different intensity, which are accompanied by low-temperature overheating. Overheating occurs in the process of discharge development. The analysis of recognition reliability of discharges with different intensity which are accompanied by 150-300°C overheating was made, using norms and criteria regulated by the most known standards and methods. The results of the analysis show that the most reliable recognition of the defects analyzed is provided to a large extent by the graphical methods, namely the ETRA square and the Duval triangle. The results obtained will significantly increase the recognition reliability of combined defects based on the results of the dissolved gas analysis in the oil.На основании анализа результатов испытаний по 135 высоковольтным трансформаторам получены диапазоны значений процентного содержания газов, значений отношений газов и построены номограмм для 10 типов комбинированных дефектов, представляющих собой разряды с разной степенью интенсивности которые сопровождаются нагревом с температурой 150-300°С. Установлено, что в трансформаторах с разрядами, которые сопровождаются низкотемпературными перегревами значения отношений: СН4/Н2, C2H2/CH4, C2H2/C2H6 и C2H2/C2H4 определяют энергию разрядов, в соответствии с нормами регламентируемыми в большинстве известных стандартов, значение отношения C2H4/C2H6 незначительно варьируется в зависимости от температуры горячей точки, а значение отношения C2H6/CH4>1. Проанализирована динамика изменения номограмм дефектов в процессе их развития. По результатам анализа установлено, что при развитии разрядов с разной степенью интенсивности, которые сопровождаются перегревами в диапазоне низких температур, в большинстве случаев первичным дефектом являются именно разряды. Перегревы возникают уже в процессе развития разрядов. Выполнен анализ достоверности распознавания разрядов с разной степенью интенсивности, которые сопровождаются нагревом с температурой 150-300°С, с использованием норм и критериев, регламентируемых наиболее известными стандартами и методиками. По результатам анализа установлено, что наибольшую достоверность распознавания, применительно к анализируемым дефектам обеспечивают в большей степени графические методы, а именно квадрат ЕТРА и треугольник Дюваля. Полученные результаты позволят существенно повысить достоверность распознавания комбинированных дефектов по результатам анализа растворенных в масле газов.На підставі аналізу результатів випробувань по 135 високовольтним трансформаторам отримані діапазони значень відсоткового вмісту газів, значення відношень газів і побудовані номограми для 10 типів комбінованих дефектів, що представляють собою розряди з різним ступенем інтенсивності, які супроводжуються перегрівами з температурою 150-300°С. Встановлено, що в трансформаторах з розрядами, які супроводжуються низькотемпературними перегрівами, значення відношень: СН4/Н2, C2H2/CH4, C2H2/C2H6 і C2H2/C2H4 визначають енергію розрядів, відповідно до норм, що регламентуються у більшості відомих стандартів, значення відношення C2H4/C2H6 незначно варіюється залежно від температури «гарячої точки», а значення відношення C2H6/CH4>1. Проаналізовано динаміку зміни номограм дефектів у процесі їх розвитку. За результатами аналізу встановлено, що при розвитку розрядів з різним ступенем інтенсивності, які супроводжуються перегрівами в діапазоні низьких температур, у більшості випадків первинним дефектом є саме розряди. Перегріви виникають вже в процесі розвитку розрядів. Виконано аналіз достовірності розпізнавання розрядів з різним ступенем інтенсивності, які супроводжуються перегрівами з температурою 150-300°С, з використанням норм і критеріїв, регламентованих найбільш відомими стандартами і методиками. За результатами аналізу встановлено, що найбільшу достовірність розпізнавання, стосовно аналізованих дефектів забезпечують більшою мірою графічні методи, а саме квадрат ЕТРА і трикутник Дюваля. Отримані результати дозволять істотно підвищити достовірність розпізнавання комбінованих дефектів за результатами аналізу розчинених у маслі газів

DITRANS - a multi-agent system for integrated diagnosis of power transformers

This paper describes the development and the implementation of a multi-agent system for integrated diagnosis of power transformers. The system is divided in layers which contain a number of agents performing different functions. The social ability and cooperation between the agents lead to the final diagnosis and to other relevant conclusions through integrating various monitoring technologies, diagnostic methods and data sources, such as the dissolved gas analysis

Expert system for the assessment of power transformer insulation condition based on type-2 fuzzy logic systems

An efficient expert system for the power transformer condition assessment is presented in this paper. Through the application of Duval's triangle and the method of the gas ratios a first assessment of the transformer condition is obtained in the form of a dissolved gas analysis (DGA) diagnosis according IEC 60599. As a second step, a knowledge mining procedure is performed, by conducting surveys whose results are fed into a first Type-2 Fuzzy Logic System (T2-FLS), in order to initially evaluate the condition of the equipment taking only the results of dissolved gas analysis into account. The output of this first T2-FLS is used as the input of a second T2-FLS, which additionally weighs up the condition of the paper-oil system. The output of this last T2-FLS is given in terms of words easily understandable by the maintenance personnel. The proposed assessing methodology has been validated for several cases of transformers in service. © 2011 Elsevier Ltd. All rights reserved.Fil: Flores, Wilfredo C.. Universidad Nacional Autónoma de Honduras; Honduras. Universidad Nacional de San Juan; ArgentinaFil: Mombello, Enrique Esteban. Universidad Nacional de San Juan; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; ArgentinaFil: Jardini, José. A.. Universidade de Sao Paulo; BrasilFil: Rattá Gutiérrez, Giuseppe Aníbal. Universidad Nacional de San Juan; ArgentinaFil: Corvo, Antonio M.. Companhia de Transmissão de Energía Elétrica Paulista; Brasi

Development of nominal rules on the Fuzzy Sugeno method to determine the quality of power transformer insulation oil using Dissolved Gas Analysis data

This paper aims to develop the nominal rules on the Fuzzy Logic Method using the Sugeno-Fuzzy Inference System (FIS) for Dissolved Gas Analysis (DGA) and determine the quality of the power Transformer 1 and Transformer 6 insulating oil at the Buduran 150 kV substation. The nominal number of proposed fuzzy rules is 1920 rules. Implementing the Fuzzy-Sugeno method on Transformers 1 and 6 shows that the six input variables from the DGA test can produce a Total Dissolved Combustible Gas (TDCG) output value of 32.67 and 26.19 ppm, respectively. Both values indicate that the insulating oil of Transformers 1 and 6 are in condition one and, at the same time, indicates that the dissolved gas composition is in Normal status. Furthermore, the TDCG value, condition, and quality status of the insulating oil have the same or 100 % accuracy compared to the DGA test by PLN (UPT Surabaya). Thus, the nominal development of fuzzy rules using the Fuzzy-Sugeno method can perform DGA analysis more accurately to determine the quality of power transformer insulation oil compared to previous studies