Real-Time Ageing and Diagnostic Prediction for Various Hybrid Solar-TEG Power Units by Machine Learning
Authors: Kasempong Surawich , Kanokwareerat Niyom , Tangjitkongpittaya Boonyalit , Setakornnukul Sarunyoo , Laipanich Apinan , Pourwichittham Weerawat 
Published: March 2021

In PTTEP's offshore fields, more than 100 units of hybrid Solar-TEG power with individual VRLA battery banks are installed on wellhead platforms for powering the process. With large number of various equipment in remote locations, traditional maintenance approach highly consuming resources is, thus, not cost effective especially in oil price crisis. In 2019, "Hybrid Power Solar-TEG Predictive Maintenance" project is established to develop a predictive model and transform maintenance process to total predictive maintenance.

The project was begun with three platforms as a pilot project. The operating model was built by machine learning using various historical data recorded in PI system, records of maintenance data and other relevant information such as manufacturer manual, international standard and related white papers. The modelled algorithm was embedded in an application which was developed by Python to predict the ageing and performance of battery banks on pilot wellhead platforms. In 2020, the project continues to build the model of Thermo-Electric Generator (TEG) and extend the coverage location for additional thirty-seven (37) platforms.

Lower Depth of Discharge (DoD), higher ambient temperature and lower charging performance are signs of battery’s deterioration while lower supplied current from power source is sign of their underperformance. All parameters were ingested to conduct pattern recognition to make algorithm be able to predict the remaining life of the key equipment.

The Eyeball method is conducted to train algorithm the various charging patterns by the developers with aim to evaluate the DoD of battery bank. Apart from battery life prediction, DoD is employed to determine the energy left in battery from night operation to indicate the remaining run time duration.

By leveraging machine learning, all failure patterns are recognized. The application is operating real?time and provide early alarm to all person-in-charge when failure potential is realized. The results are visualized on PowerBI to provide the latest status of power units of each platforms.

From above, the maintenance approach is thus completely converted from Run-to-Failure to Predictive Maintenance. The long lead spare parts e.g. battery cells could be procured in advance. Spare inventory can be optimized per actual demand. In addition, the offshore supervisor could accurately identify the defective battery banks and proactively recover them in time to minimize unplanned shutdown.

The modelled algorithm was in-house developed based on technical information and maintenance records. Although the system goes live, the preventive maintenance according to IEEE1188 is still retained for further collecting more field data to improve accuracy of the model.

In addition, the model’s analyzed information, such as battery run time and DoD, has revealed the hidden actual design margin of power system. The platform CAPEX can be thus deducted by removing such excess margin.