Application of a novel dynamic recurrent fuzzy neural network with rule self-adaptation based on chaotic quantum pigeon-inspired optimization in modeling for gas turbine

With the rapid development of renewable energy, higher requirements are put forward for the control ability of thermal power units. And an accurate system model is the premise and guarantee of controller design. In this article, a novel dynamic recurrent fuzzy neural network with rule self-adaptation modeling approach based on chaotic quantum pigeon-inspired optimization (NDRFNN-CQPIO) is proposed for the modeling of gas turbine system. The introduction of recursive layer, automatic generation of fuzzy rules and fast parameter solving are the main features of the proposed algorithm. First, set the appropriate threshold, and process the system error and distance index through 4 preset cases to generate fuzzy rules. When a new rule is generated, its antecedent parameters are determined at the same time, and define a boundary to determine whether the antecedent parameters need to be adjusted, so as to avoid the generation of redundant Gaussian functions. Then, a pruning technique based on the contributions of the normalized neuron and learning error is raised to trim the redundant fuzzy rules to ensure the model structure more compact and efficient. Finally, using the chaotic quantum pigeon-inspired algorithm to solve the consequent parameters quickly and accurately. The actual operation data of a gas turbine system were selected to identify and verify the model. In the established model, the root mean square error of output power, exhaust gas temperature, rotor speed, and exhaust gas flow are 0.0023, 0.0015, 0.0011 and 0.0069, respectively. The simulation results show that the proposed approach is effective and feasible, and it can be used for the model identification of gas turbine system. Moreover, we conducted robustness analysis of the model and proved that the proposed model has strong robustness, which provides a basis for its further application.