基于遗传粒子群优化算法的调速器执行机构分段线性模型及参数辨识

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Abstract 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.

Key words： Genetic algorithm-particle swarm optimization speed governor servo and actuator mathematical model parameter identification

Received: 06 August 2014 Published: 12 July 2016

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TM743

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https://dgjsxb.ces-transaction.com/EN/ OR https://dgjsxb.ces-transaction.com/EN/Y2016/V31/I12/204

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