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由于水轮机调节系统的大惯性、“水锤”效应等特点及其结构复杂等问题,采用传统的常规PID控制已很难满足控制要求,控制品质也难以改善,控制过程中易发生超调量大、震荡频次多、收敛时间过长等问题。对此,在常规PID控制基础上设计了基于BP神经网络自适应PID控制,并在Matlab软件中完成相关程序的编写及仿真试验。仿真结果表明,基于BP神经网络自适应PID控制是一种有效的水轮机调速器参数整定方法,相较于常规PID控制能获得更好的动态性能。
Due to the large inertia, “water hammer” effect and other structural features of the hydraulic turbine governing system and its complicated structure, it is difficult to meet the control requirements by conventional PID control and the control quality is hard to be improved. Large amount of vibration frequency, convergence time is too long and other issues. In this regard, based on conventional PID control design based on BP neural network adaptive PID control, and in the Matlab software to complete the preparation of the relevant programs and simulation tests. The simulation results show that the adaptive PID control based on BP neural network is an effective parameter tuning method for turbine governor. Compared with conventional PID control, better dynamic performance can be obtained.