Journal Article

Power Prediction for Marine Gas Turbine Plants Using a Condition-Adaptive Physics-Informed LSTM Model

Zhang, Huisheng, Hu, Zhenchao, Chen, Jinwei

2026

Overview

The accurate prediction of gas turbine output power is critical for flexible scheduling and shipboard microgrid resilience. However, purely data-driven models suffer from poor generalization and physical inconsistency in complex marine environments, especially under unseen operation conditions. This paper proposes a condition-adaptive physics-informed long short-term memory (CAPI-LSTM) framework to ensure physical consistency across the full operation envelope. In the proposed framework, an MLP-based condition-adaptive regulator is developed to dynamically adjust the compressor air flow rate within the embedded physics-informed loss function. The proposed CAPI-LSTM model is verified using the operation data from an LM2500+ gas turbine. The comparison results demonstrate the superiority of the proposed method over traditional architectures. The CAPI-LSTM model achieves the lowest root mean square error of 0.177 MW, and its error distribution is the most concentrated near zero among all compared models. The robustness of the CAPI-LSTM model is further verified under the unseen operation conditions. The CAPI-LSTM still maintains excellent generalization capability compared to both purely data-driven models and standard physics-informed models, with an average error of only 0.218 MW and a narrow interquartile range of [0.058, 0.363]. The paired t-test results confirm that the improvement of the CAPI-LSTM model is statistically significant. The CAPI-LSTM model achieves competitive computational efficiency despite the integration of the physics-informed loss function with a condition-adaptive regulator. Furthermore, the CAPI-LSTM model achieves superior performance in noise immunity and transferability to other types of gas turbines. In summary, the proposed CAPI-LSTM model provides an effective and practical solution for marine gas turbine output power prediction.

• Publisher: MDPI AG
• Subject: Accuracy / Air flow / Algorithms / Analysis / compressor flow rate / Compressors / condition-adaptive / Crack propagation / Deep learning / Design / Distributed generation / Energy consumption / Errors / Flexible work hours / Flow rates / Gas turbines / Long short-term memory / LSTM / Machine learning / Marine environment / Neural networks / Physics / physics-informed neural network / power prediction / Predictions / Statistical analysis / Turbine engines / Turbines