Converting geothermal energy into electricity is a complex process that involves a lot of skills and disciplines, from geology to thermodynamics. Consequently, the optimal operation and maintenance of a power plant requires the collection and management of a considerable amount of information. To manage the information collected within the power plant, ABB and STEAM developed a solution that performs remote diagnostic and predictive maintenance of any geothermal power plant. This software is a platform able to offer utilities having a wide range of geothermal power plants, an instrument for analyzing the operating conditions that goes beyond pure control and automation logics, based on the principles of the Standardization of the measured data and the Formalization of the know-how. "Standardization" means that the database generated in real time in each system is collected in a storage system where similar engineered quantities from different sources have the same symbolic representation (TAG) within a "Library" of "Functional Units" which can describe each component. Additionally, the behavior of such components is predicted on the basis of multidisciplinary "rules", combined with trends and algorithms. This method also allows the comparison of similar events that occurred at different times on same or different plants, providing diagnostic tools and increasing the staff's ability to recognize future warning symptoms. "Formalization" means that every single “rule” is based both on physical laws, as on the construction characteristics and guaranteed performance of each component and, last but not least, on experience. The “knowledge” embedded in the system, represents the technical know-how coming from the human factor, historically matured on field during site operation. Formalization is the key to invest in capacity building of the O&M staff, to keep the worth of their experience, to transmit it to the future operators of the power plant.
Date
Date and Time
October 20, 2020 03:45 PM (PDT)–04:45 PM (PDT)
Sponsors
Abstract
Speakers
Session Code
TSTA10