Advancing Data Analytics in Power Plant Operations

Since its establishment in 2020, TNB Genco has been actively leveraging data analytics to enhance power plant operations and address operational challenges.

Previously known as the Plant Performance and Analytics [PPA] unit, the Advanced Data Analytics [ADA] team was formed as part of Genco’s recent restructuring. While PPA focused primarily on operational issues within power plants, ADA now plays a broader role in driving the adoption of advanced analytics and machine learning across Genco.

Among the many successful initiatives led by ADA, one project stands out for its significant impact—the TNBJ M4 Coal Fineness Prediction using Machine Learning Tools For Mill Operation. This project, developed in house by ADA has earned the "Most Impactful" Project Award at the recent TNB ICT Idea Xcelerator Program [as supported in Appendix 4].

Importance of Coal Fineness Optimization

Optimizing coal fineness is crucial for coal-fired power plants, as it:

1.    Improves efficiency, leading to lower fuel costs.

2.    Enhances reliability by ensuring better and more uniform combustion.

3.    Reduces the risk of boiler tube leaks, contributing to the avoidance of capacity payment loss.

Through the use of machine learning, this project has successfully:

1.    Developed a model to accurately predict coal fineness based on specific mill settings.

2.    Enabled predictive recommendations for mill settings to achieve desired coal fineness.

3.    Reduced the duration of fineness lab tests and supported data-driven decision-making for new coal types.