Work

A novel model-based diagnostics for identifying component degradations in gas turbines for power generation

Year: 2024

Type: article

Abstract: Improving the accuracy of gas turbine performance diagnosis is important for reducing maintenance costs. Conventional model-based diagnostics use either compressor map adaptation based on correction curves or compressor map scaling based on an assumed ratio of fouling factors. However, they usually do not reflect the characteristics of the actual gas turbine. In this study, adaptation of both the compressor and turbine maps was carried out. The ratio of fouling factors was determined by quantifying the actual fouling factors so that the novel diagnostics could be applied to any gas turbine. The novelty of the proposed diagnostics is that the component degradations are identified individually, which is not available in conventional methods. To demonstrate the capabilities of the proposed diagnostics, the method was applied to a 180-MW-class gas turbine operated over a long period with off-line washing. The diagnosis results after off-line washing showed that the air flow rate and efficiency in the compressor were fully recovered, but the turbine efficiency was still degraded. The results showed that the component degradations could be identified individually. The novel diagnostics is expected to be used in a variety of strategies to reduce maintenance costs.

Source: Case Studies in Thermal Engineering

DOI: 10.1016/j.csite.2024.105528

Authors: Young Kwang Park, Do Won Kang, Ji Hun Jeong, Tong Seop Kim

Institutions: Inha University, Korea Institute of Machinery & Materials

Language: English

Topic: Turbomachinery Performance and Optimization

Subfield: Aerospace Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Affordable and clean energy

Open Access status: gold

Estimated APC paid: $1,210