Geothermal application for TOMONI

12 August 2020



Mitsubishi Hitachi Power Systems, Ltd (MHPS) (now Mitsubishi Power) has concluded a contract to introduce MHPS-TOMONI – its “digital solutions service” – to the Domo de San Pedro geothermal power station in southern Nayarit, Mexico, through the group company in Mexico, Mitsubishi Hitachi Power Systems de Mexico SA de CV (MHPS de Mexico).


Above: Domo de San Pedro geothermal power station

 

This is the first use of MHPS-TOMONI at a geothermal power plant. It will allow real- time monitoring of the plant’s status and performance.

The goal is to improve the operation and performance of equipment and reduce maintenance requirements and O&M costs, while extending the intervals between regular inspections.

The Domo de San Pedro geothermal power station is located on the outskirts of Tepic, the capital of Nayarit in western Mexico. It is the first privately-run geothermal power plant in Mexico, operated by Geote´rmica para el Desarrollo SAPI de CV (GEODESA). The 27 MW power plant was built by MHPS under a full turnkey EPC (engineering, procurement, and construction) contract, and completed in 2016.

Geothermal power plants are prone to performance degradation, with the pattern of degradation differing at each geothermal plant, making standardised remedial measures difficult.

MHPS-TOMONI applications originally developed for fossil fired power plants will provide diagnostic information on the plant status and performance and operational data accumulated on a daily basis, giving the operator in-depth support based on the plant’s operating status. Oversight and support will come from the Remote Monitoring Center (RMC), located at Mitsubishi’s Nagasaki Works in Japan.

Eng. Jose Manuel Lage Castro, general manager of the Domo de San Pedro geothermal power station, commented on his expectations: “MHPS-TOMONI will help us maximise the efficiency of our geothermal power plant. In addition, it will give us with the capability to perform predictive maintenance by monitoring trend data for the plant, and providing recommendations if abnormal conditions are detected thus preventing problems from occurring.”



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