Against the backdrop of achieving the “dual carbon” reduction goals, the proportion of renewable energy consumption in China is gradually increasing, and more and more thermal power units are gradually being operated at the deep peak shaving states, which puts forward higher requirements for stable operation of the power grid system. In view of the current situation, this paper designs a new type of Python-based parameter modeling software for turbine control systems, including the load selection module, data pre-processing module, parameter identification module, and result output module. To solve the problem that the primary frequency regulation parameters of the units operated at the deep peak shaving states change with the load, the software is designed to set key model parameters of different load segments of the units. The actual measurement results of a unit operated at the deep peak shaving state show that this developed software is convenient and friendly to operate, creates accurate parameter identification results, and has a wider range of applicability.
A typical operating set of equipment can be obtained through cluster analysis of historical data. Two state monitoring models for HP medium speed coal mill are established based on Gaussian process regression and the similarity index calculated by this model can be used for measuring the operating status of HP mills. Finally a method for fault diagnosis of HP mill based on Gaussian regression modelling is proposed combined with fault diagnosis knowledge base of this HP mill. Taking the HP medium speed mill of a 660MW thermal power unit as an example, the real operating data is collected and used for modelling and analysis. Results shows that the equipment parameter estimation calculated by Gaussian process regression is accurate. It can be used for early-warning and diagnosed of equipment fault and also for practical engineering application.
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