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Information entropy and fuzzy analytical theory were combined and applied on the state assessment of power plant steam turbine unit. This method can be combined with the measured data of power plant, and made a comprehensive evaluation of the different working conditions. Fuzzy analysis of the original matrix can be a evaluation matrix, and the information entropy was introduced to solve the problem of calculating the weight of multi attribute factor set. Once the attribute weight is determined, the comprehensive evaluation value corresponding to the different working conditions can be defined and calculated. The results obtained by this method were compared with those obtained by the principal component analysis method. And the results obtained by information entropy method were objective and unique. The superiority of the method was verified. Under all conditions, the comprehensive evaluation value can be used as one index of the thermal archives as a benchmark for fault diagnosis, which provided a new idea and evaluation method for steam turbine fault diagnosis, and had a strong basic research and practical application value.

Based on big data analysis, for the actual operating data, the method of dividing non-uniform working conditions was studied to obtain typical data of different working conditions. The traditional sliding pressure curve is only a shortcoming of the single value function of the load. Convolution neural network is applied to build a nonlinear model of load, main steam temperature, reheated steam temperature, ambient temperature and main steam pressure. And then, the real-time optimized value of the main steam pressure is obtained. After verification, the model is proved to be satisfactory in both precision and regularity. Finally, the main steam pressure optimization model is applied to the actual 300MW thermal power unit, and closed-loop control is performed. The results prove that the application of real-time main steam pressure optimization can effectively reduce the energy consumption of the unit, and have higher energy saving potential at different ambient temperatures.