A Classified Intelligent Extraction Method for Hydropower Station Reservoir Scheduling Rules

Abstract

The invention discloses a classification intelligent extraction method for hydropower station reservoir dispatching operation rules, comprising: using the power station output as an output variable, using a correlation analysis method to determine the input variable; obtaining the normalized input variable and output variable, using a clustering method The input variables of all samples are divided into K categories; for the input variables and output variables under each category, the corresponding ELM models are respectively constructed for simulation and approximation, and the improved particle swarm optimization algorithm is used to optimize the parameters of the ELM model, thereby obtaining K different ELM model: Determine the category of the newly acquired input variable, and input it into the corresponding model to obtain the corresponding output value. Perform denormalization processing to obtain the output value of the power station for dispatching decisions. The invention adopts the classified evolutionary extreme learning machine model to extract the scheduling rules of the hydropower station reservoirs, which can significantly improve the long-term operation benefits of the hydropower station reservoirs, and is beneficial to the efficient utilization of water energy resources of cascade hydropower station groups in the river basin.

Description

technical field [0001] The invention belongs to the field of high-efficiency utilization of water resources and optimal dispatching of hydropower systems, and more specifically relates to a method for classifying and intelligently extracting dispatching rules of hydropower station reservoirs. Background technique [0002] Compared with other fossil energy sources, hydropower has unique advantages such as low pollutant discharge, renewable and rapid start-stop capabilities, so it is very necessary to mine the dispatching and operation rules of hydropower reservoirs from long-sequence actual data to improve the actual dispatching level and economic efficiency. Operating efficiency is a research subject with important theoretical significance and practical value. In the field of dispatching rules for hydropower stations and reservoirs, scholars at home and abroad have successively proposed various methods such as linear regression, nonlinear regression, dispatching graphs, and ...

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Problems solved by technology

[0004] In view of the above defects or improvement needs of the prior art, the present invention provides a classification intelligent extraction method for hydropower station reservoir dispatching rules, thereby solving the problems of low learning efficiency and easy falling into local optimum existing in the existing derivation of reservoir dispatching operation rules. technical problem

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