Schneider Electric delivers Avantis asset health and performance monitoring software at power plant

Schneider Electric Software, a global leader in industrial software for the process industries, recently completed the installation of its Avantis® PRiSM predictive asset analytics software at EDF Group’s Norte Fluminense power generation plant.

Expanding on its use of PRiSM to monitor a number of assets across its fossil-fuel, nuclear and hydropower generation fleets in Europe, EDF is now implementing the technology for real-time asset health and performance monitoring of a combined-cycle plant in Brazil. The Norte Fluminense plant, with 780 MW of generating capacity, is committed to ensuring system reliability in the entire metropolitan area of ​​Rio de Janeiro and generating energy for a population of more than two million.

“As a utility known for our commitment to social responsibility and our focus on sustainable development, we place great importance on safe and reliable power generation,” said Marcio Marques, engineer O &M at EDF Group. “PRiSM helps us achieve those goals by improving the availability and performance of our critical assets.”

PRiSM uses a proprietary algorithm, machine learning and advanced pattern recognition to identify and alert on subtle changes in equipment behavior for early warning notification and diagnosis of asset health and performance problems. The software has the proven ability to alert plant personnel of asset failures days, weeks or months before problems occur, leading to increased equipment reliability and reduced unplanned downtime. Customers state that the use of PRiSM has allowed them to reduce equipment downtime by up to 25 percent.

“EDF Group is a long-time customer that has continued to expand its PRiSM deployments after experiencing significant avoided costs,” said Rob McGreevy, Vice President of Information, Operations at Schneider Electric. “We take great pride in the fact that they trust our technology to identify potential issues and achieve the greatest return on every asset.”

"Combining machine learning, like PRiSM’s advanced pattern recognition technology, opens new opportunities for improving uptime and asset longevity - which are the leading metrics for asset management,” commented Ralph Rio, Research Director, ARC Advisory Group. Ralph continued, “This software enables users to move up the maintenance management maturity curve from corrective and preventive approaches to more effective predictive and proactive strategies.”

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