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Research on Genetic Algorithm and Its Application in Thermal Process Identification and Control

Author: ZhangShiHua
Tutor: ZuoGang
School: Southeast University
Course: Thermal Power Engineering
Keywords: Genetic Algorithms Thermal Process Identification PID parameter optimization
CLC: TK32
Type: Master's thesis
Year: 2004
Downloads: 246
Quote: 13
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Abstract


The increasing demand for energy to promote the development of the electric power industry, and a variety of high-capacity, high-parameter thermal power units continue to put into production, the increasingly high degree of automation, quality control of the process control system, also put forward higher requirements. Grasp the controlled object characteristics (mathematical models) to determine the optimal control parameters, design reasonable system. After modeling the complex control system using traditional empirical analysis to determine the reasonable and regulation quality is far from adequate. Therefore, the study of modern optimization techniques based model identification method and controller parameter tuning technology for improving the quality control of the thermal power units of great significance, but also the crew optimization run, an important measure to improve running economy. The thesis consists of three parts. The first part of the genetic algorithm. Precocity and the local convergence problems prevalent in traditional genetic algorithm to solve the problem, this paper proposes an improved real-coded adaptive genetic algorithm, and verify the effectiveness of the improved algorithm. The second part of the study a thermal process based on improved genetic algorithms identification method. First expounded the principle of the process of identification based on genetic algorithm. Strong as a common identification methods, analysis and simulation when the test signal to the effectiveness of the proposed algorithm, various signal or test signal containing noise. In response to the degree of curve fitting the criteria for work, in theory, a system to meet the corresponding system model fitting conditions, there are infinitely many system identification method based on the step response curve fitting based on genetic algorithm the possibility of such identification method and reliability analysis, and to draw firm conclusions, and provides a basis for the engineering application of such a system identification methods. According to the characteristics of the object of the thermal processes, the analysis of the differences between the parameters and parameter mismatch general transfer function type identification. Step response curve is characterized by thermal processes, the transfer function of the type classified and integrated thermal processes, so as to effectively overcome the problem using the general transfer function type identification, and simulation results show that the method is effective. The third part of the study based on improved genetic algorithm PID parameter optimization method. First, a more comprehensive overview of the PID parameter tuning technology. On this basis, the proposed tuning PID parameters based on genetic algorithm optimization technology. This method is the direct use of the global optimization ability of genetic algorithms to get adjusted amount and adjust the amount of linear quadratic global optimum, thus avoiding the routine based on the LQ optimal control design problem solving complicated Riccati equation, therefore more versatility. Simulation results show that the method is effective. Paper genetic algorithm theory and operator of a qualitative analysis to improve the strategy of the traditional genetic algorithm, to improve the ability of global optimization, and improved genetic algorithm is applied to the thermal process identification and PID parameter optimization, has good versatility and accuracy. Simulation results show that the algorithm is effective and has a very good engineering application value.

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CLC: > Industrial Technology > Energy and Power Engineering > Thermal measurement and thermal automatic control > Thermal Automatic Control
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