| Intelligent optimization algorithm has global, parallel and efficient optimize performance, robustness, versatility, etc., it has been widely used in multi-objective optimization, optimization scheduling problems, transportation problems, combinatorial optimization problems, engineering design optimization in many areas and caused the attention of domestic and foreign scholars to study and set off a boom. Intelligent optimization algorithm based on typical particle swarm optimization algorithm and clonal selection algorithms have attracted more and more attention of people, particularly focus on improvements of these algorithms and the research of algorithm in many areas, Researchers requirements of algorithm performance are increasing. Therefore, how to design a robust, fast, high precision, stable performance intelligent optimization algorithm is still the focus of research.THSI paper mainly research on the intelligent optimization algorithms and it's applications, such as thermal optimization. The main study of intelligent optimization algorithm is Biogeography-Based Optimization(BBO) algorithm and improved BBO algorithm combined with the particle swarm optimization algorithm, and optimize the controller parameters of typical thermal power thermal system, the algorithm and improved BBO algorithm performance and performance proved effective by simulation study. Details are as follows:(1)Describes in detail the develop of intelligent optimization algorithms and classification, and introduces a new optimization algorithm- Biogeography-Based Optimization and it's development, algorithm characteristics and application status algorithm.(2)Details the basic knowledge of biological geography and biogeography migration model, and further elaborated on the princip les of biogeography-based optimization algorithms, including BBO algorithm migration and mutat ion operation content. Finally, gives the overall operating process of BBO algorithm.(3)Research on the optimize method of PID parameters in thermal power thermal system based on BBO algorithm. THSI chapter based on the characteristics of BBO algorithm, such as global convergence speed, and combined with the characteristics of thermal power control thermal system, applied BBO algorithm to parameter optimization of thermal system controller. The article gives thermal BBO PID algorithm that based on hybrid optimization method combined with thermal power reheat steam temperature system, and simulation.(4)In order to improve BBO algorithm to optimize the performance in thermal systems, make it have superior convergence, the article proposed an improved BBO optimization algorithm. Introduced convergence mechanism of particle swarm optimization algorithm based on the original BBO migration strategy, make the entire migration process with a certain direction, and use the phase-out strategy to reject parameter which mutation worse after the migration. Thus, on the one hand directional migration and elimination mechanism to ensure its rapid convergence, on the other hand mutational mechanisms to ensure global characteristics of wide-area search, to avoid falling into local minima. Simulation results show that, improved BBO algorithm has effectively improved the convergence speed and accuracy in the optimization process.(5)In order to verify the effectiveness of the proposed method in engineering applications, and for the further discussion of its engineering application in thermal systems of power plant, the article given a improved BBO algorithm to conduct system identification and parameter optimization in engineering applications of thermal system. As an example of burner tilt reheat steam temperature system in actual power plant, given improved BBO algorithms to make model identification and controller optimization. The article contains data collection, data filtering, system identification, parameter optimization. Finally, verify the results of optimization. |
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