Dynamic Monitoring & Analysis of Plant Equipment using Near-Real-Time Data

Through the introduction of online monitoring technologies in the field, including substation monitors capable of providing near-real-time data and readings on power transformers, switchgear and circuit breakers, along with line monitors for overhead conductor and monitors for underground cables, organizations now possess novel opportunities to integrate this data into their asset management frameworks such that decision-making can be enhanced. This presentation will examine recent efforts to develop Asset Performance Management (APM) frameworks, leveraging available near-real-time data that is being retrieved from online monitoring devices. This presentation will touch upon the emerging field of analytics that can be applied to this data, including machine learning techniques, in order to detect specific signatures within the data that can be correlated to specific failure modes. Robust APM frameworks are able to leverage this near-real-time data for the purposes of delivering dynamic health index results based upon the newest readings, as well as operational alerts that can detect possible failures that will occur in the next few weeks or months, providing organizations with enough time to take action. For complex substation assets, an APM solution can allow plant managers to manage and mitigate significant risks within their system. This presentation will also present the results from specific case studies, where near-real-time data from field sensors was successfully leveraged and integrated into an APM such that the organization was able to enhance their risk management and asset management decision-making approaches.