The parameter identification of the steam turbine speed governing system needs to be realized by the fitting of the measured response curve of the steam turbine active power. The national standard puts forward strict requirements on the error of the identification result. At present, error calculation is often realized by manual punctuation, which is a complicated process and greatly influenced by human judgment. Hence it needs to be improved urgently. In this paper, polynomial fitting and improved sliding window method are used to optimize the error identification algorithm of the steam turbine active power response curve. The visualization of the program is realized based on the Python language. The algorithm improves the data processing efficiency and reduces the influence of human judgment. The calculation results meet the standard requirements.
At present, power system stability analysis model usually adopt the BPA model, which is provided by the PSD-BPA Transient Stability Program User Manual, and the model parameters are usually obtained from the measured data of 80% or more typical working conditions. With the expansion of load operating interval after retrofitting the flexibility of thermal power units, whether the model parameters can accurately simulate the response changes of the power system under the working conditions of deep peak regulation, that is, the model adaptability under the working conditions of deep peak regulation needs to be further studied. In this paper, the measured data of a 660MW subcritical unit under the working conditions of deep peak regulation was simulated and identified through the model. After the analysis of the simulation results, it was found that the model parameters under the working conditions of deep peak regulation greatly deviated compared with those under typical working conditions, so the model’s adaptability was poor.
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