Gas turbine performance prediction via machine learning

Zuming Liu and Iftekhar A. Karimi

Energy, 2020, vol. 192, issue C

Abstract: This paper develops a machine learning-based method to predict gas turbine performance for power generation. Two surrogate models based on high dimensional model representation (HDMR) and artificial neural network (ANN) are developed from real operational data to predict the operating characteristics of air compressor and turbine. Both models capture the operating characteristics well with average errors of less than 1.0%. Moreover, four more holistic models are developed to capture gas turbine part-load and full-load performance. The models for air compressor and turbine are then embedded into a gas turbine simulation program, and all surrogate models are validated using a separate data set. It is shown that the power output, pressure ratio, fuel flow, and turbine exhaust temperature from these models match their measured values well with average and maximum errors of less than 2.0% and 4.3%, respectively. Since holistic ANN models have lower complexity and higher accuracy, the ANN model for predicting full-load performance is used to construct gas turbine performance correction curves. The correction curves along with the ANN model for predicting part-load performance offer an excellent basis for continuous health monitoring and fault diagnosis. The proposed methodology is applicable to any gas turbines and can help power plants to study and quantify performance degradation over time.

Keywords: Gas turbine; Surrogate models; Machine learning; Performance prediction; Correction curves; Simulation (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (32)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544219323229
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:192:y:2020:i:c:s0360544219323229

DOI: 10.1016/j.energy.2019.116627

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().