Their first digitization project was targeted at its Reliability Performance Monitoring (RPM) center that monitors its fleet of combined-cycle natural gas turbines totaling over 4 GW of capacity. The center was established 5 years earlier as a comprehensive asset management platform for 4 GW of operating capacity across their 8 sites. The existing RPM Center was focused in two areas: 1. Condition Based Monitoring (CBM) to improve turbine reliability, reduce operating & maintenance (O&M) costs, and increase Asset Utilization, and 2. Performance Monitoring to increase production while reducing fuel consumption. The center wanted to leverage OSIsoft PI Asset Frameworks (AFs), time-series, and transactional maintenance records from SAP to increase net capacity by maximizing turbine utilization. This required improving turbine reliability by using more effective maintenance strategies to reduce unscheduled forced outages.

The team chose OSIsoft PI System as the core of the new system, providing a standardized, single source of truth to store plant information, including DCS and historian data, predictive maintenance results, and day ahead market analytics.

Building the AF took considerable time and effort. Each site engineer needed to assemble maintenance data from SAP with over 10k tags/plant from OSIsoft PI (measuring pump vibration and temperature). Using spreadsheets proved ineffective as it not only introduced errors but was slow, impacting their ability to monitor turbine efficiency.

And because new data was manually assembled within spreadsheets, KPIs that tracked excess energy were built site-by-site leveraging those same spreadsheets to display scheduled and unscheduled availability. This made comparing KPIs for like-units between sites difficult - which in turn - slowed access to turbine performance data, and impacting contractual production vs potential capacity decision making. Maintenance also suffered as crews did not have enough time — nor insight — to perform corrective maintenance tasks efficiently. An investigation by the monitoring center’s data analyst also showed that data quality was an issue — especially with null and flat-lined values. This resulted in day ahead market analytics incorrectly forecasting demand leading to increased spot trading. Laborious data cleansing tasks by the PI admin and data managers to address the quality issue further impacted the team's ability to respond quickly.

This removes the burdensome hand-coding of individual site spreadsheets and provides a way for engineers to compare like-unit performance to resolve issues consistently across sites. Element Unify builds — and automatically updates — AFs to reflect changes in production data, allowing engineers to focus on improvement activities without additional resources, increasing their efficiency 4x, and reducing costs.

Higher production and lower risk

Element Unify lowered risks and unlocked higher production by increasing compliance with PPAs, reducing exposure to spot markets, and materializing excess energy sales. Data is now trusted as Data Quality reports identify issues before analysis, reducing the risk of inaccurate predictions and ineffective maintenance tasks, improving asset lifecycle management.
‍