Hybrid Model for on-line Detection of Gas Turbine Lean Blowout Events

Abstract

Modern dry low NOx combustors can target very low emissions levels by operating at a lean air/gas ratio. However, ultra-lean combustion is extremely susceptible to thermoacoustic combustion instabilities and Lean Blowout (LBO), which can lead to large pressure oscillations in the combustor and decreased durability of components. Conventional on-board diagnostics embedded in the Unit Control Panel (UCP) of a Gas Turbine, continuously check the health status of the combustion section at a high scan rate and raise alarms when abnormal conditions occur. While ensuring protection and control, UCP control logics may not provide precise indications on the nature of the issue and further troubleshooting, also using specific tools, is typically required. In a changing environment where Industrial Internet of Things (IIoT) is offering a chance to drive productivity and growth, online Monitoring and Diagnostic (M&D) software and services on connected units are becoming strategic to increase asset availability and reliability, as well as reducing maintenance costs. In this paper, we present a hybrid analytic, which combines physics-based and data-driven models, for the detection of Lean Blowout conditions on Gas Turbines equipped with Dry Low NOx multi-can combustion system. Regarding the data-driven model, we face a problem of classification and exploit dimensionality reduction to reduce the number of variables under consideration. During the development, different techniques are tested and benchmarked. The analytic is trained on real LBO events and finally is deployed in a production environment to process incoming on-line data acquired from monitored units. Obtained results are presented.

Dry-Low Emission Gas Turbine Technology: Recent Trends and Challenges

“…According to [90], the increase in pilot fuel percentage decreases the equivalence ratio of LBO. Furthermore, the swirl strength and physical mixing of fuel and air will also influence the LBO limit [82,88,93]. The LBO limit increases with the rise of swirl intensity, as reported in [94].…”

“…This phenomenon is widely known as the LBO fault, which will be thoroughly discussed in the next section. In many cases, as reported in [79][80][81][82][83][84], LBO fault is considered the most common problem in DLE systems, which leads the gas turbine to trip. Other problems are observed before the blowout, such as auto-ignition, flashback, and combustion instability.…”

“…Controlling the air and fuel ratio to keep the gas turbine in its operating region [79][80][81][82][83][84] Auto-ignition…”

“…Further, the computation time is also faster than both other methods. The following study implemented data-driven was performed by Iannitelli as documented in [82]. Iannitelli used a classification approach to detect LBO from the exhaust gas temperature profile.…”