Performance Optimization of a Steam Generator Level Control System via a Revised Simplex Search-Based Data-Driven Optimization Methodology

• Published in: Processes Vol. 10; no. 2; p. 264
• Main Authors: Kong, Xiangsong, Shi, Changqing, Liu, Hang, Geng, Pengcheng, Liu, Jiabin, Fan, Yasen
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.02.2022
• Subjects: Approximation; Boilers; Control systems; Controllers; Data search; Design of experiments; Efficiency; Iterative methods; Methods; Nonlinear systems; Nuclear power plants; Nuclear reactors; Optimization; Optimization algorithms; Parameters; Quality control; Search methods; Steam electric power generation; Tuning
• ISSN: 2227-9717; 2227-9717
• DOI: 10.3390/pr10020264

A Steam generator is a crucial device of a nuclear power plant. Control performance of the steam generator level control system is key to its normal operation. To improve its performance, the control system parameters should be optimized by utilizing a proper optimization method. Furthermore, the method’s efficiency is critical for its operability in the actual plant. However, the steam generator level process is a complex process, with high nonlinearity and time-varying properties. Traditional parameters tuning methods are experience-based, cumbersome, and time-consuming. To address the challenge, a systemic data-driven optimization methodology based on the model-free optimization with a revised simplex search method was proposed. Rather than the traditional controller parameter tuning method, this method optimizes the control system directly by using control performance measurements. To strengthen its efficiency, two critical modifications were incorporated into the traditional simplex search method to form a knowledge-informed simplex search based on historical gradient approximations. Firstly, with the help of the historical gradient approximations, the revised method could sense the optimization direction more accurately and accomplish the iteration step size tuning adaptively, significantly reducing the optimization cost. Secondly, a revised iteration termination control strategy was developed and integrated to monitor the optimization progress, which can promptly terminate the progress to avoid unnecessary iteration costs. The effectiveness and the efficiency of the revised method were demonstrated through simulation experiments.