More realistic degradation trend prediction for gas turbine based on factor analysis and multiple penalty mechanism loss function

Gas turbines play a crucial role in absorbing the volatility of new energy sources such as wind and photovoltaic. Continuous degradation trend prediction for gas turbines is vital for rationalizing maintenance schedules and improving power system stability. Current prediction techniques do not consider the practicality of the prediction results. To address this issue, a prediction framework based on factor analysis and Multiple Penalty Mechanisms (MPM) loss function is proposed. Firstly, factor analysis is used to assess the health index of gas turbines. Secondly, an innovative loss function that incorporates penalties for prediction errors, lag prediction, and fluctuation prediction is proposed to improve forecast usability. A range-adjustable and asymmetric Hyperbolic Cosine with Exponential (CoshE) function is first proposed to address the prediction lag problem. Finally, Long Short Term Memory network is chosen as the predictive model, and dynamic weights are used to optimize the loss function. Experiments on the combustion chamber degradation dataset and C-MAPSS dataset show that the framework proposed performs optimally than the conventional loss functions and the CoshE function is more efficient in the MPM framework. Meanwhile, MPM significantly improves gate recurrent unit and convolutional neural network performance. The method proposed is noteworthy for its superiority and applicability.