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All Journal JEEE-U (Journal of Electrical and Electronic Engineering-UMSIDA)
Asaad Saber Ameen
Salahaddin University, Engineering College
Electrical & Electronics Engineering

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Data-Driven Digital Twin for Gas Turbine Power Output Estimation and Performance Deviation Monitoring: Data-Driven Digital Twin for Gas Turbine Asaad Saber Ameen; Ibrahim Ismail Hamarash
JEEE-U (Journal of Electrical and Electronic Engineering-UMSIDA) Vol. 10 No. 2 (2026): Oktober
Publisher : Muhammadiyah University, Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/jeeeu.v10i1.1738

Abstract

Digital twin which creates virtual representation of physical systems using real-time data are increasingly employed for performance monitoring, operational optimization, and decision support in the energy sector in power generation , the electrical power output in gas turbines varies significantly with environmental and operational conditions. Accurate estimation of the expected power output (MW) is indispensable for benchmarking and degradation detection. This study proposes a data-driven digital twin framework for predicting power output and monitoring performance deviation for an operational GE Frame 9E gas turbine power plant. Real-time data is acquired from a Mark VIe control system and archived in the AVEVA PI historian (PI ProcessBook).Four machine learning algorithms, Support Vector Regression (SVR), Random Forest regression, Extreme Gradient Boosting, and a feed-forward Artificial Neural Network (ANN) were implemented and evaluated to find the most suitable model for the digital twin, The XGBoost-based digital twin achieved the best overall accuracy (RMSE = 1.4056 MW and R² = 0.9125) and generated the most stable deviation behavior, supporting performance benchmarking in industrial operation.. The digital twin calculates the deviation (DMW) between actual and expected power output, enabling continuous non-intrusive monitoring of performance shifts. This study provides a framewok for integrating real-time data with machine learning models to deploy the digital twins for condition monitoring in gas turbine operations.
Co-Authors Ibrahim Ismail Hamarash