Research on Parameter Identification for Primary Frequency Regulation of Steam Turbine Based on Improved Bayesian Optimization-Whale Optimization Algorithm

Wei Li, Weizhen Hou, Siyuan Wen, Yang Jiang, Jiaming Sun and Chengbing He ()
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Wei Li: Shandong Electric Power Engineering Consulting Institute Co., Ltd., Jinan 250011, China
Weizhen Hou: Shandong Electric Power Engineering Consulting Institute Co., Ltd., Jinan 250011, China
Siyuan Wen: School of Energy, Power and Mechanical Engineering, North China Electric Power University, Beijing 102206, China
Yang Jiang: School of Energy, Power and Mechanical Engineering, North China Electric Power University, Beijing 102206, China
Jiaming Sun: School of Energy, Power and Mechanical Engineering, North China Electric Power University, Beijing 102206, China
Chengbing He: School of Energy, Power and Mechanical Engineering, North China Electric Power University, Beijing 102206, China

Energies, 2025, vol. 18, issue 21, 1-30

Abstract: To address the problems of local optima and insufficient convergence accuracy in parameter identification of primary frequency regulation (PFR) for steam turbines, this paper proposed a hybrid identification method that integrated an Improved Bayesian Optimization (IBO) algorithm and an Improved Whale Optimization Algorithm (IWOA). By initializing the Bayesian parameter population using Tent chaotic mapping and the reverse learning strategy, employing a radial basis kernel function hyperparameter training mechanism based on the Adam optimizer and optimizing the Expected Improvement (EI) function using the Limited-memory Broyden–Fletcher– Goldfarb–Shanno with Bounds (L-BFGS-B) method, IBO was proposed to obtain the optimal candidate set with the smallest objective function value. By introducing a nonlinear convergence factor and the adaptive Levy flight perturbation strategy, IWOA was proposed to obtain locally optimized optimal solutions. By using the reverse-guided optimization mechanism and employing a fitness-oriented selection strategy, the optimal solution was chosen to complete the closed-loop process of reverse learning feedback. Nine standard test functions and the Proportional Integral Derivative (PID) parameter identification of the electro-hydraulic servo system in a 330 MW steam turbine were presented as examples. Compared with Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), Bayesian Optimization (BO) and Particle Swarm Optimization-Grey Wolf Optimizer (PSO-GWO), the Improved Bayesian Optimization-Whale Optimization Algorithm (IBO-WOA) proposed in this paper has been validated to effectively avoid the problem of getting stuck in local optima during complex optimization and has high parameter recognition accuracy. Meanwhile, an Out-Of-Distribution (OOD) Test based on noise injection had demonstrated that IBO-WOA had good robustness. The time constant identification of the steam turbine were carried out using IBO-WOA under two experimental conditions, and the identification results were input into the PFR model. The simulated power curve can track the experimental measured curve well, proving that the parameter identification results obtained by IBO-WOA have high accuracy and can be used for the modeling and response characteristic analysis of the steam turbine PFR.

Keywords: steam turbine; primary frequency regulation (PFR); Improved Bayesian Optimization (IBO); Improved Whale Optimization Algorithm (IWOA); parameter identification (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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