Real-time power prediction approach for turbine using deep learning techniques

• Published in: Energy (Oxford) 2021-10, Vol.233, p.121130, Article 121130
• Main Authors: Sun, Lei, Liu, Tianyuan, Xie, Yonghui, Zhang, Di, Xia, Xinlei
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Convolutional neural network; Deep learning; Frequency dependence; Frequency modulation; Learning algorithms; Machine learning; Mathematical models; Neural networks; Power plant; Power plants; Power prediction; Predictions; Predictive control; Predictive maintenance; Real time; Recurrent neural network; Recurrent neural networks; Statistical analysis; Statistical methods; Statistical models; Thermal power; Thermal power plants; Turbines
• ISSN: 0360-5442; 1873-6785
• DOI: 10.1016/j.energy.2021.121130

Accurate power forecasting is of great importance to the turbine control and predictive maintenance. However, traditional physics models and statistical models can no longer meet the needs of precision and flexibility when thermal power plants frequently undertake more and more peak and frequency modulation tasks. In this study, the recurrent neural network (RNN) and convolutional neural network (CNN) for power prediction are proposed, and are applied to predict real-time power of turbine based on DCS data (recorded for 719 days) from a power plant. In addition, the performances of two deep learning models and five typical machine learning models are compared, including prediction deviation, variance and time cost. It is found that deep learning models outperform other shallow models and RNN model performs best in balancing the accuracy-efficient trade-off for power prediction (the relative prediction error of 99.76% samples is less than 1% in all load range for test 216 days). Moreover, the influence of training size and input time-steps on the performance of RNN model is also explored. The model can achieve remarkable performance by learning only 30% samples (about 216 days) with 3 input time-steps (about 60 s). Those results of the proposed models based on deep-learning methods indicated that deep learning is of great help to improve the accuracy of turbine power prediction. It is therefore convinced that those models have a high potential for turbine control and predictable maintenance in actual industrial scenarios. [Display omitted] •Real-time power prediction for steam turbine was performed via deep learning.•The predictive models were validated by actual data from the real power plant.•Results indicated deep learning models outperform shallow machine learning models.•Impacts of different variables on performance of the model was evaluated.•This study can improve accuracy, stability and efficiency of power prediction.