Thermal power generating unit peak load regulation capacity prediction method, device and system

Abstract

The invention discloses a thermal power generating unit peak regulation capability prediction method, device and system. The method comprises the following steps of screening out a data set from historical data of thermal power generating unit operation; determining the optimal clustering number according to the BIC value; performing primary clustering on the data set based on a K-means algorithm;performing secondary clustering on the data set based on a GMM algorithm; and determining the class cluster with the highest similarity in all the class clusters according to the collected real-timedata, and taking the maximum value of the optimization parameter in the class cluster with the highest similarity and the minimum value of the optimization parameter in all the class clusters as prediction results. According to the method, the optimal clustering number of a sample set and the initial parameters of a GMM algorithm are determined through a BIC value and a K-means algorithm, the accuracy of solving the Gaussian distribution parameters by utilizing the EM algorithm is effectively improved, so that the accuracy of final prediction is improved; according to the thermal power generating unit peak load regulation capacity prediction method, device and system, online prediction can be carried out on the thermal power generating unit peak load regulation capacity, and a data basis can be provided for a power grid to compile a load scheduling strategy.

Description

technical field [0001] The invention relates to the technical field of power generation in thermal power plants, in particular to a method, device and system for predicting the peak-shaving capability of a thermal power unit. Background technique [0002] Thermal power generation is an extremely complex production process. There are significant differences in the load capacity of the units under different coal quality and seasons. It is difficult to achieve good results by using traditional methods to monitor and evaluate the peak-shaving capacity of thermal power units. Therefore, how to Mining a large amount of historical operation data, automatically classifying the unit operating conditions, and predicting the peak-shaving capacity of the unit under each condition is a requirement for safe and reliable dispatching and operation of the power grid and the construction of a smart grid, and it is also a problem that still needs to be studied. Contents of the invention [0...

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Problems solved by technology

[0002] Thermal power generation is an extremely complex production process. There are significant differences in the load capacity of the units under different coal quality and seasons. It is difficult to achieve good results by using traditional methods to monitor and evaluate the peak-shaving capacity of thermal power units. Therefore, how to Mining a large

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