Data-efficient digital twin for turbine heat rate of industrial thermal power plant

Developing a robust and efficient data-driven digital twin system for industrial thermal power systems remains challenging due to data drift, change in operating behaviour of the system and ineffective data-sampling issues for data-driven model development. We present data-efficient model training framework that incorporates effective data sampling from large volumes of asymmetric and controlled data regimes of industrial power systems. Artificial Neural Network (ANN) model is trained on the sampled and representative dataset to predict turbine heat rate (THR) of 660-megawatt (MW) capacity thermal power plant. Later, THR is minimized by a constrained non-linear optimisation technique at 50%, 75%, and 100% capacity discharge of power plant, and the optimisation-based results are validated in the operation of the power plant with the mean absolute percentage errors of 0.79%, 2.98% and 0.33% respectively. The analysis on cost of operation and carbon dioxide (CO 2 ) reduction reveals that optimising THR through the data-efficient model training and optimisation framework can save around 13 million USD with a reduction of 28 kilotonnes (kt) of CO 2 per year. Finally, the data-efficient trained ANN model is deployed as a digital twin system for monitoring the THR and is found to be more than 90% accurate on 19000 minutes of real-time monitoring window. This research paves the way for data-efficient sampling from the controlled datasets of thermal power plants that leads to improved generalisation capacity of the trained machine learning models for their integration in the digital twin systems for monitoring the performance of industrial power systems. • Data-efficient sampling from asymmetric industrial data-distributions is presented. • Model trained on representative data sample accurately predicts turbine heat rate. • ≈USD 13 million cut in operating cost and ≈28 kt CO 2 per year reduction are achieved. • Data-driven digital twin system monitors the turbine heat rate with > 90% accuracy. • Data-efficient model training enhances the adaptability of next-generation digital tools in industrial operations.