Domain-Informed Operation Excellence of Gas Turbine System with Machine Learning

TL;DR

This paper introduces MAD-OPT, a Mahalanobis distance-based framework that integrates domain knowledge into machine learning analytics to optimize gas turbine operations, improving efficiency and robustness under varying conditions.

Contribution

The paper presents a novel MAD-OPT framework that incorporates domain knowledge via Mahalanobis distance constraints into data-centric optimization for gas turbines.

Findings

MAD-OPT estimates robust optimal conditions under different ambient scenarios.

The framework achieves comparable results to actual plant data beyond design limits.

Incorporating domain constraints improves the practicality of AI solutions in power systems.

Abstract

The domain-consistent adoption of artificial intelligence (AI) remains low in thermal power plants due to the black-box nature of AI algorithms and low representation of domain knowledge in conventional data-centric analytics. In this paper, we develop a MAhalanobis Distance-based OPTimization (MAD-OPT) framework that incorporates the Mahalanobis distance-based constraint to introduce domain knowledge into data-centric analytics. The developed MAD-OPT framework is applied to maximize thermal efficiency and minimize turbine heat rate for a 395 MW capacity gas turbine system. We demonstrate that the MAD-OPT framework can estimate domain-informed optimal process conditions under different ambient conditions, and the optimal solutions are found to be robust as evaluated by Monte Carlo simulations. We also apply the MAD-OPT framework to estimate optimal process conditions beyond the design power generation limit of the gas turbine system, and have found comparable results with the actual data of the power plant. We demonstrate that implementing data-centric optimization analytics without incorporating domain-informed constraints may provide ineffective solutions that may not be implementable in the real operation of the gas turbine system. This research advances the integration of the data-driven domain knowledge into machine learning-powered analytics that enhances the domain-informed operation excellence and paves the way for safe AI adoption in thermal power systems.