Lessons learned in fleet wide asset monitoring of gas turbines and supporting equipment in power generation applications

Abstract

The transition from route based measurements to a Fleet Wide Surveillance program touches many elements from sensors to networked data acquisition nodes to servers to historians and predictive technologies. At Duke Energy, installation costs, information technology strategies, and long term vision comes together to create higher machine reliability at lower operational cost and new automation in performance monitoring, diagnostics, and advisory generation. With automation, comes increased sensory data from pumps and turbines that require new tools for data management, mining, and transformation into actionable information. This case study reviews the open and extensible data architecture of the system deployed, the ongoing efforts, and current benefits delivered to Duke Energy. The nearly five year effort is a combined effort of vendors, the Electrical Power Research Institute (EPRI), and tireless effort from Duke Energy.