Prognostic Approaches Using Transient Monitoring Methods

The utilization of steady state monitoring techniques has become an established means of providing diagnostic and prognostic information regarding both systems and equipment. However, steady state data is not the only, or in some cases, even the best source of information regarding the health and state of a system. Transient data has largely been overlooked as a source of system information due to the additional complexity in analyzing these types of signals. The development for algorithms and techniques to quickly, and intuitively develop generic quantification of deviations a transient signal towards the goal of prognostic predictions has until now, largely been overlooked. By quantifying and trending these shifts, an accurate measure of system heath can be established and utilized by prognostic algorithms. In fact, for some systems the elevated stress levels during transients can provide better, more clear indications of system health than those derived from steady state monitoring. This research is based on the hypothesis that equipment health signals for some failure modes are stronger during transient conditions than during steady-state because transient conditions (e.g. start-up) place greater stress on the equipment for these failure modes. From this it follows that these signals related to the system or equipment health would display more prominent indications of abnormality if one were to know the proper means to identify them. This project seeks to develop methods and conceptual models to monitor transient signals for equipment health. The purpose of this research is to assess if monitoring of transient signals could provide alternate or better indicators of incipient equipment failure prior to steady state signals. The project is focused on identifying methods, both traditional and novel, suitable to implement and test transient model monitoring in both an useful and intuitive way. By means of these techniques, it is shown that the addition information gathered during transient portions of life can be used to either to augment existing steady-state information, or in cases where such information is unavailable, be used as a primary means of developing prognostic models

A Review of Prognostics and Health Management Applications in Nuclear Power Plants

The US operating fleet of light water reactors (LWRs) is currently undergoing life extensions from the original 40-year license to 60 years of operation. In the US, 74 reactors have been approved for the first round license extension, and 19 additional applications are currently under review. Safe and economic operation of these plants beyond 60 years is now being considered in anticipation of a second round of license extensions to 80 years of operation.Greater situational awareness of key systems, structures, and components (SSCs) can provide the technical basis for extending the life of SSCs beyond the original design life and supports improvements in both safety and economics by supporting optimized maintenance planning and power uprates. These issues are not specific to the aging LWRs; future reactors (including Generation III+ LWRs, advanced reactors, small modular reactors, and fast reactors) can benefit from the same situational awareness. In fact, many SMR and advanced reactor designs have increased operating cycles (typically four years up to forty years), which reduce the opportunities for inspection and maintenance at frequent, scheduled outages. Understanding of the current condition of key equipment and the expected evolution of degradation during the next operating cycle allows for targeted inspection and maintenance activities. This article reviews the state of the art and the state of practice of prognostics and health management (PHM) for nuclear power systems. Key research needs and technical gaps are highlighted that must be addressed in order to fully realize the benefits of PHM in nuclear facilities

Failure analysis informing intelligent asset management

With increasing demands on the UK’s power grid it has become increasingly important to reform the methods of asset management used to maintain it. The science of Prognostics and Health Management (PHM) presents interesting possibilities by allowing the online diagnosis of faults in a component and the dynamic trending of its remaining useful life (RUL). Before a PHM system can be developed an extensive failure analysis must be conducted on the asset in question to determine the mechanisms of failure and their associated data precursors that precede them. In order to gain experience in the development of prognostic systems we have conducted a study of commercial power relays, using a data capture regime that revealed precursors to relay failure. We were able to determine important failure precursors for both stuck open failures caused by contact erosion and stuck closed failures caused by material transfer and are in a position to develop a more detailed prognostic system from this base. This research when expanded and applied to a system such as the power grid, presents an opportunity for more efficient asset management when compared to maintenance based upon time to replacement or purely on condition

Adjustment of model parameters to estimate distribution transformers remaining lifespan

Currently, the electrical system in Argentina is working at its maximum capacity, decreasing the margin between the installed power and demanded consumption, and drastically reducing the service life of transformer substations due to overload (since the margin for summer peaks is small). The advent of the Smart Grids allows electricity distribution companies to apply data analysis techniques to manage resources more efficiently at different levels (avoiding damages, better contingency management, maintenance planning, etc.). The Smart Grids in Argentina progresses slowly due to the high costs involved. In this context, the estimation of the lifespan reduction of distribution transformers is a key tool to efficiently manage human and material resources, maximizing the lifetime of this equipment. Despite the current state of the smart grids, the electricity distribution companies can implement it using the available data. Thermal models provide guidelines for lifespan estimation, but the adjustment to particular conditions, brands, or material quality is done by adjusting parameters. In this work we propose a method to adjust the parameters of a thermal model using Genetic Algorithms, comparing the estimation values of top-oil temperature with measurements from 315 kVA distribution transformers, located in the province of Tucumán, Argentina. The results show that, despite limited data availability, the adjusted model is suitable to implement a transformer monitoring system.Fil: Jimenez, Victor Adrian. Universidad Tecnológica Nacional. Facultad Regional Tucumán. Centro de Investigación en Tecnologías Avanzadas de Tucumán; ArgentinaFil: Will, Adrian L. E.. Universidad Tecnológica Nacional. Facultad Regional Tucumán. Centro de Investigación en Tecnologías Avanzadas de Tucumán; ArgentinaFil: Gotay Sardiñas, Jorge. Universidad Tecnológica Nacional. Facultad Regional Tucumán. Centro de Investigación en Tecnologías Avanzadas de Tucumán; ArgentinaFil: Rodriguez, Sebastian Alberto. Universidad Tecnológica Nacional. Facultad Regional Tucumán. Centro de Investigación en Tecnologías Avanzadas de Tucumán; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán; Argentin

Machine Learning Methods for Anomaly Detection in Nuclear Power Plant Power Transformers

Power transformers are an important component of a nuclear power plant (NPP). Currently, the NPP operates a lot of power transformers with extended service life, which exceeds the designated 25 years. Due to the extension of the service life, the task of monitoring the technical condition of power transformers becomes urgent. An important method for monitoring power transformers is Chromatographic Analysis of Dissolved Gas. It is based on the principle of controlling the concentration of gases dissolved in transformer oil. The appearance of almost any type of defect in equipment is accompanied by the formation of gases that dissolve in oil, and specific types of defects generate their gases in different quantities. At present, at NPPs, the monitoring systems for transformer equipment use predefined control limits for the concentration of dissolved gases in the oil. This study describes the stages of developing an algorithm to detect defects and faults in transformers automatically using machine learning and data analysis methods. Among machine learning models, we trained Logistic Regression, Decision Trees, Random Forest, Gradient Boosting, Neural Networks. The best of them were then combined into an ensemble (StackingClassifier) showing F1-score of 0.974 on a test sample. To develop mathematical models, we used data on the state of transformers, containing time series with values of gas concentrations (H2, CO, C2H4, C2H2). The datasets were labeled and contained four operating modes: normal mode, partial discharge, low energy discharge, low-temperature overheating.Comment: 9 pages, 5 figures, 4 tables, 33 reference

Editorial messages

Dear readers, The goal of this special edition was to shed light on the application of machine learning and artificial intelligence in the transformer industry, contributing to a better understanding of the requirements and available solutions. Expectations from these technologies are high in terms of what they can provide in various fields: monitoring, diagnostics, control, maintenance, perhaps even design, etc. A particular advantage of AI and ML technologies is the ability to predict future conditions, which opens up space for completely new paradigms, especially in maintenance. AI and ML technologies are also highly related to digitalization as a dominant global trend, which facilitates agile business models that respond to challenges within emerging markets. Digitalization also leads to a surge in data generation and accumulation, and with proper analysis, these data are expected to significantly secure and improve the grid performance and resolve various customer demands. This is also why we need solutions for understanding the data and learning from it. In addition, the speed and reliability of obtained information become essential, so all these trends are greatly supporting each other. Therefore, significant growth in investments and businesses related to this field is expected in near future. However, there are also challenges such as testing, deployability, scalability, transparency, affordability, and cyber security. I’m glad that a group of great authors together with our respectable Guest Editorial team have prepared high-quality articles this issue brings, addressing the above-mentioned key aspects. I hope you will enjoy your reading

Online condition monitoring of MV cable feeders using Rogowski coil sensors for PD measurements

Condition monitoring is a highly effective prognostic tool for incipient insulation degradation to avoid sudden failures of electrical components and to keep the power network in operation. Improved operational performance of the sensors and effective measurement techniques could enable the development of a robust monitoring system. This paper addresses two main aspects of condition monitoring: an enhanced design of an induction sensor that has the capability of measuring partial discharge (PD) signals emerging simultaneously from medium voltage cables and transformers, and an integrated monitoring system that enables the monitoring of a wider part of the cable feeder. Having described the conventional practices along with the authors’ own experiences and research on non-intrusive solutions, this paper proposes an optimum design of a Rogowski coil that can measure the PD signals from medium voltage cables, its accessories, and the distribution transformers. The proposed PD monitoring scheme is implemented using the directional sensitivity capability of Rogowski coils and a suitable sensor installation scheme that leads to the development of an integrated monitoring model for the components of a MV cable feeder. Furthermore, the paper presents forethought regarding huge amount of PD data from various sensors using a simplified and practical approach. In the perspective of today’s changing grid, the presented idea of integrated monitoring practices provide a concept towards automated condition monitoring.fi=vertaisarvioitu|en=peerReviewed

Study of Useful Life of Dry-Type WTSU Transformers

Dry-type transformers are rapidly becoming popular as wind turbine step-up (WTSU) transformers, especially in offshore wind farms. Cast resin transformers are not flammable and are also resistant to moisture. However, their thermalelectrical degradation must be carefully analysed given the special conditions of wind farm installations. The present paper studies the remaining useful life (RUL) calculation of dry-type WTSU transformers based on the most thermally stressed location i.e. the winding hot-spot. The estimation of the loss of life of the transformer can be used for diagnostic and prognostic monitoring purposes in the framework of digital twins. The methodology is then applied to a typical WTSU load profile and the impact of several transformer characteristics and operating conditions are compared to the reference case.The authors gratefully acknowledge the support of the Basque Government (project ELKARTEK KK2018/00096 and GISEL research group IT1191-19), as well as of the University of the Basque Country UPV/EHU (research group funding GIU18/181)

Editorial messages

Dear readers, The goal of this special edition was to shed light on the application of machine learning and artificial intelligence in the transformer industry, contributing to a better understanding of the requirements and available solutions. Expectations from these technologies are high in terms of what they can provide in various fields: monitoring, diagnostics, control, maintenance, perhaps even design, etc. A particular advantage of AI and ML technologies is the ability to predict future conditions, which opens up space for completely new paradigms, especially in maintenance. AI and ML technologies are also highly related to digitalization as a dominant global trend, which facilitates agile business models that respond to challenges within emerging markets. Digitalization also leads to a surge in data generation and accumulation, and with proper analysis, these data are expected to significantly secure and improve the grid performance and resolve various customer demands. This is also why we need solutions for understanding the data and learning from it. In addition, the speed and reliability of obtained information become essential, so all these trends are greatly supporting each other. Therefore, significant growth in investments and businesses related to this field is expected in near future. However, there are also challenges such as testing, deployability, scalability, transparency, affordability, and cyber security. I’m glad that a group of great authors together with our respectable Guest Editorial team have prepared high-quality articles this issue brings, addressing the above-mentioned key aspects. I hope you will enjoy your reading