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Dynamic simulation of gas turbines via feature similarity-based transfer learning
Frontiers in Energy ( IF 2.9 ) Pub Date : 2020-12-10 , DOI: 10.1007/s11708-020-0709-9
Dengji Zhou , Jiarui Hao , Dawen Huang , Xingyun Jia , Huisheng Zhang

Since gas turbine plays a key role in electricity power generating, the requirements on the safety and reliability of this classical thermal system are becoming gradually strict. With a large amount of renewable energy being integrated into the power grid, the request of deep peak load regulation for satisfying the varying demand of users and maintaining the stability of the whole power grid leads to more unstable working conditions of gas turbines. The startup, shutdown, and load fluctuation are dominating the operating condition of gas turbines. Hence simulating and analyzing the dynamic behavior of the engines under such instable working conditions are important in improving their design, operation, and maintenance. However, conventional dynamic simulation methods based on the physic differential equations is unable to tackle the uncertainty and noise when faced with variant real-world operations. Although data-driven simulating methods, to some extent, can mitigate the problem, it is impossible to perform simulations with insufficient data. To tackle the issue, a novel transfer learning framework is proposed to transfer the knowledge from the physics equation domain to the real-world application domain to compensate for the lack of data. A strong dynamic operating data set with steep slope signals is created based on physics equations and then a feature similarity-based learning model with an encoder and a decoder is built and trained to achieve feature adaptive knowledge transferring. The simulation accuracy is significantly increased by 24.6% and the predicting error reduced by 63.6% compared with the baseline model. Moreover, compared with the other classical transfer learning modes, the method proposed has the best simulating performance on field testing data set. Furthermore, the effect study on the hyper parameters indicates that the method proposed is able to adaptively balance the weight of learning knowledge from the physical theory domain or from the real-world operation domain.



中文翻译:

通过基于特征相似性的传递学习进行燃气轮机动态仿真

由于燃气轮机在发电中起着关键作用,因此对这种经典热力系统的安全性和可靠性的要求日益严格。随着大量可再生能源被集成到电网中,为了满足用户不断变化的需求并维持整个电网的稳定性而对深峰值负荷进行调节的要求导致燃气轮机的工作条件更加不稳定。启动,关闭和负载波动是燃气轮机运行状态的主要控制因素。因此,在这种不稳定的工作条件下模拟和分析发动机的动态行为对于改善其设计,操作和维护非常重要。然而,传统的基于物理微分方程的动态仿真方法在面对变化的现实世界操作时无法解决不确定性和噪声。尽管数据驱动的仿真方法在一定程度上可以缓解该问题,但是不可能在数据不足的情况下执行仿真。为了解决这个问题,提出了一种新颖的转移学习框架,可以将知识从物理方程域转移到现实世界的应用域,以弥补数据的不足。根据物理方程式创建具有陡峭斜率信号的强大动态操作数据集,然后构建并训练具有编码器和解码器的基于特征相似性的学习模型,以实现特征自适应知识的传递。模拟精度显着提高了24。与基线模型相比,预测误差降低了6%,预测误差降低了63.6%。此外,与其他经典的转移学习模式相比,该方法在现场测试数据集上具有最佳的模拟性能。此外,对超参数的效果研究表明,所提出的方法能够自适应地平衡来自物理理论领域或来自实际操作领域的学习知识的权重。

更新日期:2020-12-22
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