Machine Learning Applications in Estimating Transformer Loss of Life

Transformer life assessment and failure diagnostics have always been important problems for electric utility companies. Ambient temperature and load profile are the main factors which affect aging of the transformer insulation, and consequently, the transformer lifetime. The IEEE Std. C57.911995 provides a model for calculating the transformer loss of life based on ambient temperature and transformer's loading. In this paper, this standard is used to develop a data-driven static model for hourly estimation of the transformer loss of life. Among various machine learning methods for developing this static model, the Adaptive Network-Based Fuzzy Inference System (ANFIS) is selected. Numerical simulations demonstrate the effectiveness and the accuracy of the proposed ANFIS method compared with other relevant machine learning based methods to solve this problem.Comment: IEEE Power and Energy Society General Meeting, 201

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

Power transformer dissolved gas analysis through Bayesian networks and hypothesis testing

Accurate diagnosis of power transformers is critical for the reliable and cost-effective operation of the power grid. Presently there are a range of methods and analytical models for transformer fault diagnosis based on dissolved gas analysis. However, these methods give conflicting results and they are not able to generate uncertainty information associated with the diagnostics outcome. In this situation it is not always clear which model is the most accurate. This paper presents a novel multiclass probabilistic diagnosis framework for dissolved gas analysis based on Bayesian networks and hypothesis testing. Bayesian network models embed expert knowledge, learn patterns from data and infer the uncertainty associated with the diagnostics outcome, and hypothesis testing aids in the data selection process. The effectiveness of the proposed framework is validated using the IEC TC 10 dataset and is shown to have a maximum diagnosis accuracy of 88.9%

Diagnosing faults in power transformers with autoassociative neural networks and mean shift

Tese de Mestrado Integrado. Engenharia Electrotécnica e de Computadores (Área de Especialização de Energia). Faculdade de Engenharia. Universidade do Porto. 201

Fuzzy neural networks' application for substation integral state assessment

This paper addresses the problems connected with fuzzy neural networks' application in equipment technical state assessment problems at electrical substations. This paper discusses the main principles of fuzzy neural network formation and its construction algorithm. Also, the case study for the determination of fuzzy neural network synaptic weights for the unit "disconnector" on the basis of technical diagnostic statistical data and tests is presented. © 2014 WIT Press.International Journal of Safety and Security Engineering;International Journal of Sustainable Development and Planning;WIT Transactions on Ecology and the Environmen

An AI-Layered with Multi-Agent Systems Architecture for Prognostics Health Management of Smart Transformers:A Novel Approach for Smart Grid-Ready Energy Management Systems

After the massive integration of distributed energy resources, energy storage systems and the charging stations of electric vehicles, it has become very difficult to implement an efficient grid energy management system regarding the unmanageable behavior of the power flow within the grid, which can cause many critical problems in different grid stages, typically in the substations, such as failures, blackouts, and power transformer explosions. However, the current digital transition toward Energy 4.0 in Smart Grids allows the integration of smart solutions to substations by integrating smart sensors and implementing new control and monitoring techniques. This paper is proposing a hybrid artificial intelligence multilayer for power transformers, integrating different diagnostic algorithms, Health Index, and life-loss estimation approaches. After gathering different datasets, this paper presents an exhaustive algorithm comparative study to select the best fit models. This developed architecture for prognostic (PHM) health management is a hybrid interaction between evolutionary support vector machine, random forest, k-nearest neighbor, and linear regression-based models connected to an online monitoring system of the power transformer; these interactions are calculating the important key performance indicators which are related to alarms and a smart energy management system that gives decisions on the load management, the power factor control, and the maintenance schedule planning

Determination of Optimal Transformation Ratios of Power System Transformers in Conditions of Incomplete Information Regarding the Values of Diagnostic Parameters

On the base of damage rate analysis of power transformers and methods of electrical energy system (EES) modes control the necessity of using the results of on-line diagnostics of LTC transformers not only for determinations of the expending of further operation or equipment repair but also for calculation of optimal transformation coefficients (with account of the suggested RRCT) for their application in the process of modes control has been proved. Improved method of determination of control action, realized by the LTC transformers by means of comparative analysis of the results calculation of EES modes with quasi resistances of the circuit branches. Such peculiarity of the suggested method of determination of control actions by LTC transformers, as the account of RRCT, in the process of EES mode control provides such advantages as reduction of the damage rate of the equipment, reduction of active power losses in EES. Due to the peculiarities of the method of determination of control actions by LTC transformers, with the account of their technical state, perspectives of the development and introduction in EES modern microprocessor-based systems of automatic control of transformers LTC open

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 an integrated low-power RF partial discharge detector

This paper presents the results from integrating a low-power partial discharge detector with a wireless sensor node designed for operating as part of an IEEE 802.15.4 sensor network, and applying an on-line classifier capable of classifying partial discharges in real-time. Such a system is of benefit to monitoring engineers as it provides a means to exploit the RF technique using a low-cost device while circumventing the need for any additional cabling associated with new condition monitoring systems. The detector uses a frequency-based technique to differentiate between multiple defects, and has been integrated with a SunSPOT wireless sensor node hosting an agent-based monitoring platform, which includes a data capture agent and rule induction agent trained using experimental data. The results of laboratory system verification are discussed, and the requirements for a fully robust and flexible system are outlined