Operation Schedule And Load Dispatch Of Combined Cycle Generating Unit: Modeling, Solution By Heuristic Genetic Algorithm And Data Processing

This dissertation is mainly focused on the operation schedule and load dispatch of heavy-duty multi-shaft combined cycle generating unit. It involves key components ( including gas turbine, heat recovery steam generator and steam turbine ) modeling; general calculating models of thermodynamic properties for water, steam and gas; off-design performance calculating method for combined cycle generating unit; on-line optimum load dispatch for heavy-duty multi-shaft combined cycle generating unit according to the real-time dispatching load of automatic generation control; and heuristic genetic algorithm solving for operation schedule and load dispatch of combined cycle generating unit with multiple complex constraint condition. The most important contribution this dissertation presents is that multiple methods have been comprehensively applied so as to meet the needs of problem solving. The methods include modeling by mechanism analysis; modeling based on cerebellar model articulation controller ( CMAC ) neural networks; hybrid modeling combined mechanism analysis and CMAC neural networks; data mining technique and its applications in modeling; data processing algorithm used in modeling applied data mining technique; self-adaptive heuristic genetic algorithm; etc. The main work and innovations in this dissertation are as follows:1. The reverse engineering subject of combined cycle generating unit's key components modeling is discussed. The mathematic models for gas turbine, heat recovery steam generator and steam turbine combined mechanism analysis and CMAC neural networks are established. Firstly, the mechanism is analyzed and then mechanism models are established; secondly, on the basis of discussing the principles of CMAC, the author deduces the conceptual mapping algorithm, the physical mapping algorithm, the output mapping algorithm, and the learning algorithm of CMAC, then, introduces CMAC in the procedure of modeling, establishes the mathematic models for gas turbine, heat recovery steam generator and steam turbine based on CMAC; lastly, with the guidance of heuristic knowledges, the mechanism models andthe mathematic models based on CMAC are combined some hybrid models.2. The general calculating models of thermodynamic properties for water, steam and gas are systematically discussed in the dissertation. Then, the software is implemented and developed by adopting object oriented program design method, and the reliability, expandability, convenience are greatly improved.3. Based on the mathematic models for gas turbine, heat recovery steam generator and steam turbine combined mechanism analysis and CMAC neural networks, off-design performance calculation of combined cycle generating unit is performed, and some correlative conclusions are educed from the off-design performance calculation.4. On the basis of off-design performance calculation of combined cycle generating unit, on-line optimum load dispatch for heavy-duty multi-shaft combined cycle generating unit according to the ambient temperature, ambient pressure and real-time dispatching load of automatic generation control is studied.5. For the operation schedule and load dispatch of heavy-duty multi-shaft combined cycle generating unit, this research attempts to formulate a mathematical model for the operation schedule and load dispatch problem and propose a self-adaptive heuristic genetic algorithm to solve the problem. Multiple complex constraint conditions are considered in the mathematical model of the problem. The objective in this dissertation is to minimize the total cost among an operation period that consists of the start-up cost, the shutdown costs, and the normal operation cost etc. Based on the heuristic knowledge, some new and effective genetic operating operators are constructed, a modified algorithm control strategy is adopted, in order to overcome local minimum problem. Finally, the implemented software system applied self-adaptive heuristic genetic algorithm was tes...