Data-Driven Hierarchical Neural Network Modeling for High-Pressure Feedwater Heater Group

Abstract

This paper proposes a data-driven hierarchical neural network modeling method for a high-pressure feedwater heater group (HPFHG) in power generation industry. An HPFHG is usually made up of several cascaded high-pressure feedwater heaters (HPFH). The challenge of modeling an HPFHG is to formulate not only the HPFHG as a whole but also its components at the same time. Physical modeling techniques based on dynamic thermal calculation can hardly be applied in practice because of lacking necessary parameters. Based on big operating data, modeling by neural networks is feasible. However, traditional artificial neural networks are black boxes, which are difficult to describe the subsystems or inner components of a system. The proposed modeling approach is inspired by the physical cascade structure of the HPFHG to tackle this problem. Experimental results show that our modeling approach is effective for the entire HPFHG as well as its every single component.