Development of Real-time Monitoring System in Organic Rankine Cycle Operation of Lahendong Geothermal Field

Abstract

Abstract Geothermal resources in Indonesia are commonly located in spread mountain area and some scattered in small islands, such as in Nusa Tenggara and Maluku province. Small scale geothermal power plant with a wellhead type may be required for some geothermal field areas. In operation and maintenance of geothermal power plant usually need high skilled personnel. The real-time monitoring system (RTMS) becomes a promising option for performance analysis and optimization from a remote monitoring room. The performance engineer may execute some small scale power plant in different project sites. The monitoring needs the data variables related significantly to the production of power output. Therefore, finding an attractive-interface, and related variables data of power output, become an interesting issue in performance monitoring. Lahendong organic Rankine cycle (ORC) power plant in North Sulawesi Indonesia, with a capacity of 500 kW, has implemented the RTMS. RTMS data, starting from the sensor reading up to the programmable logic controller (PLC) as a local data collector, transferred to the server (which placed in Germany) via the connection hub. From the server, the data stored on the database and visualize on the human-machine interface (HMI). The variables of the RMTS have been analyzed. The study includes analyzing the scatter-plot of the variables, plotting of the time variables, and applied the Pearson coefficient correlation (PCC) to discover the most related variables to the power output. The variables include brine inlet temperature, brine inlet pressure, hot water inlet temperature, hot water inlet pressure, and power output during the three months. The result, the monitoring of the Lahendong ORC power plant gives positive feedback to the user, with interactive visualization data. The feedback shows that hot water temperature has the most robust correlation to the power output of the Lahendong ORC power plant with 0.91, close to 1 for PCC analysis. In the future, machine learning analysis will be applied to RTMS on Lahendong, so that it will increase user experience and sharpening data analysis. The relocation of the server from Germany to Indonesia will be done to add decision-making features. Expectedly, this paper can be a reference to other power plants and fields that apply RTMS as remote monitoring, taking into consideration the remote location of the geothermal field.