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The next generation of power substations, known as digital substations, are influencing contemporary digital technologies to change how energy is delivered and transported. The objective of the paper is to clearly define how the Industrial Internet of Things (IIoT) can improve the performance and value of assets without requiring replacement of the fleet, reshaping the future of electrical power substations. The paper introduces a case study that analyses smart digital substations, connecting wind farms to the grid, where both primary and secondary assets and wind turbines data are made available to a SCADA system. Following a methodological approach and considering the off-line (historical data, operators’ inspections, off-line reports) and on-line (interconnected sensors, protection & control IEDs and SCADA parameters) data being captured from assets and are ingested into the platform, breaking the information silos, contributing to assess the risk level for each asset, in a user friendly and easily accessible cloud SaaS (Software as a Service) environment. The methodological approach deployed in this paper helped to define how to use data to its fullest potential and enable renewable energy sources to provide more dependable power supplies while utilizing the same grid infrastructure. The approach defines a clear path starting by applying predictive maintenance fundamentals through condition monitoring, moving to turning data into information, evolving to turning information into decisions as outcomes from the Asset Performance Management system (APM), and finally Video Analytics technology. As a result of applying this approach on a step-up power transformer and protection relays, APM application is proved to be able to produce more insights, of higher quality and level of detail, that helps in having effective decision-making process.
The next generation of power substations, known as digital substations, are influencing contemporary digital technologies to change how energy is delivered and transported. The objective of the paper is to clearly define how the Industrial Internet of Things (IIoT) can improve the performance and value of assets without requiring replacement of the fleet, reshaping the future of electrical power substations. The paper introduces a case study that analyses smart digital substations, connecting wind farms to the grid, where both primary and secondary assets and wind turbines data are made available to a SCADA system. Following a methodological approach and considering the off-line (historical data, operators’ inspections, off-line reports) and on-line (interconnected sensors, protection & control IEDs and SCADA parameters) data being captured from assets and are ingested into the platform, breaking the information silos, contributing to assess the risk level for each asset, in a user friendly and easily accessible cloud SaaS (Software as a Service) environment. The methodological approach deployed in this paper helped to define how to use data to its fullest potential and enable renewable energy sources to provide more dependable power supplies while utilizing the same grid infrastructure. The approach defines a clear path starting by applying predictive maintenance fundamentals through condition monitoring, moving to turning data into information, evolving to turning information into decisions as outcomes from the Asset Performance Management system (APM), and finally Video Analytics technology. As a result of applying this approach on a step-up power transformer and protection relays, APM application is proved to be able to produce more insights, of higher quality and level of detail, that helps in having effective decision-making process.
This study emphasizes the urgent need for systems that monitor the operational states of primary electrical equipment, particularly power transformers. The rapid digitalization of and increasing data volumes from substations, coupled with the inability to retrofit outdated equipment with modern sensors, underscore the necessity for algorithms that analyze the operational parameters of digital substations based on key power system metrics such as current and voltage. This research focuses on digital substations with Architecture III and aims to develop an algorithm for processing digital substation data through an appropriate mathematical tool for time-series analysis. For this purpose, the fast discrete wavelet transform was chosen as the most suitable method. Within the framework of the research, possible transformer faults were divided into two categories by the nature of their manifestation. A mathematical model for two internal transformer fault categories was built. The most effective parameters from the point of view of the possibility of identifying an internal fault were selected. The proposed algorithm shows its effectiveness in the compact representation of the signal and compression of the time series of the parameter to be monitored.
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