Soft computing approach for modeling power plant with a once-through boiler
In this paper, a soft computing approach is presented for modeling electrical power generating plants in order to characterize the essential dynamic behavior of the plant subsystems. The structure of the soft computing method consists of fuzzy logic, neural networks and genetic algorithms. The measured data from a complete set of field experiments is the basis for training the models including the extraction of linguistic rules and membership functions as well as adjusting the other parameters of the fuzzy model. The genetic algorithm is applied to the modeling approach in order to optimize the procedure of the training. Comparison between the responses of the proposed models with the responses of the plants validates the accuracy and performance of the modeling approach. A similar comparison between the responses of these models with the models obtained based on the thermodynamical and physical relations of the plant shows the effectiveness and feasibility of the developed model in terms of more accurate and less deviation between the responses of the models and the corresponding subsystems.
soft computing power plant once-through boiler neural network genetic algorithm fuzzy modeling experimental data