Establishing an accurate model for power plant under working conditions with wide range is the most fundamental and critical part of smart power generation operation control. In order to improve the rapidity of the modeling process and the accuracy and versatility of the identified model

a new T-S fuzzy modeling method based on quantum bee colony clustering is proposed in this paper. Firstly

the quantum bee colony is presented as clustering algorithm for the premise part identification. Not only the clustering speed is improved through the introduction of quantum concepts

but also the subjectivity caused by artificially set clustering number is eliminated through the decision constants and data space constraint. Then

the exponential weighted least squares method is used in consequent part identification to ensure the simplicity and accuracy of the local model. Finally

taking the modeling of the coordinated control system in ultra-supercritical power plant as an example

the application of the proposed algorithm in the modeling of power plant in the intelligent power generation operation control system is discussed. The simulation results illustrate that

this proposed algorithm not only ensures the rapidity of the modeling process

but also makes the identified model maintain high precision and show well superior versatility and adaptability.