Research on Modeling and Control of Turbine-Driven Coaxial Boiler Feed Pump Speed Regulation System Based on an Improved BP-PID Algorithm

The turbine-driven coaxial boiler feed pump (TD-BFP) speed regulation system is a core auxiliary machine in thermal power generating units. Its complex physical characteristics, including strong square-law nonlinearity, multivariable coupling, and large inertia, pose significant challenges for conventional fixed-parameter PID controllers, which often suffer from severe regulation lag, integral windup, and high-frequency oscillation during wide-range operating condition transitions. To address these issues, an improved adaptive PID control strategy based on a Back Propagation (BP) neural network is proposed in this paper. Specifically, to overcome the negative control gradient loss caused by the square-law resistance in the physical model, a sign-preserving mapping logic (u∣u∣) is innovatively designed. Furthermore, a dynamic anti-integral windup mechanism with physical boundary constraints and a first-order inertial filtering algorithm is introduced. Comprehensive simulation experiments on the Matlab/Simulink platform under high-load step operating conditions (3683 r/min and 1104 t/h) reveal that the proposed algorithm achieves millisecond-level, zero-overshoot tracking. Quantitative evaluations demonstrate that, compared with the traditional PID controller, the proposed method reduces the Root Mean Square Error (RMSE) by 88.29% and the Integral of Absolute Error (IAE) by 93.75%, achieving a near-perfect goodness of fit (R2) of 0.9998. Additionally, the Total Variation (TV) of the control command is substantially decreased. These results convincingly demonstrate that the proposed controller perfectly balances extremely high dynamic fitting accuracy with reduced mechanical wear, presenting exceptional engineering application value for the localization transformation of power plant control systems.