摘要
某些火电厂调速器执行机构调门呈非线性,基于调门整段全开全关曲线计算的执行机构开启和关闭时间常数结果不准确,改进了汽轮机调速器执行机构数学模型。该模型分别采用主、辅分段开启和关闭时间常数反映调门的非线性,其主开启和关闭时间常数通过全开全关曲线线性段计算得到,并结合调门小扰动实验辨识得到执行机构PID参数,再由全开全关实验确定调门分段点及辅开启和关闭时间常数。选用多个测试函数与其他基本智能算法比较,仿真验证了遗传粒子群优化算法(GA-PSO)的有效性;实际电厂算例验证了所建执行机构分段线性模型及参数的有效性。
Tone fully open and close tests in some thermal power plants are nonlinear, and opening and closing time constant calculation based on the whole opening and closing curves is inaccurate. An improved turbine governor actuator mathematical model is proposed, in which the main and auxiliary on/off time constants were used in segment to reflect the nonlinear characteristic of the turbine valve. The defined main on and off time constants were calculated through linear segments of the full opening and closing curves. Then actuator PID parameters were obtained through small disturbed position tests of the valve, and the valve segmentation point as well as auxiliary on and off time constants was determined by the fully opening and closing valve tests. Compared with other basic intelligence algorithms by selecting multiple test functions, the simulation results show the effectiveness of GA-PSO algorithm, Moreover, the actual power plant example verifies that the governor actuator piecewise linear model and its parameters are effective.
出处
《电工技术学报》
EI
CSCD
北大核心
2016年第12期204-210,共7页
Transactions of China Electrotechnical Society
基金
国家自然科学基金资助项目(51307123
51347006)
关键词
遗传粒子群优化
调速器
执行机构
数学模型
参数辨识
Genetic algorithm-particle swarm optimization
speed governor
servo and actuator
mathematical model
parameter identification