New energy template machine addressing method and system based on chaotic genetic algorithm

Abstract

The invention provides a new energy template machine addressing method and system based on a chaotic genetic algorithm. According to the method, whether template machine replacement is needed is determined, blower fans are divided into multiple large groups according to characteristics, and template machines are further re-calculated through the chaotic genetic algorithm; accurate relevant meteorological prediction data can be acquired through the new template machines to acquire accurate active / reactive prediction data. The method is advantaged in that quantitative indexes are employed to analyze template machine performance for disadvantages of a traditional subjective template machine selection method, real-time template machine replacement is realized through the chaotic genetic algorithm, theoretical generating capacities and air abandoning volumes of other blower fans in a wind power field can be accurately estimated through the proper template machines, and the method has quite important actual application values.

Description

technical field [0001] The invention belongs to the field of wind power, and in particular relates to a new method and system for analyzing the performance of a model machine in a wind farm by using quantitative indicators, and replacing the model machine in real time through a chaotic genetic algorithm. Background technique [0002] With the increasing installed capacity of wind power, the share of wind power in the electricity market is gradually increasing. The country pays more and more attention to the management of wind power related statistics and real-time control of wind farms. Therefore, the concept of wind farm prototype machine is extended. Based on the wind farm model machine combined with the wind turbine processing characteristics combined with the electric field model, the approximate theoretical active power of each wind turbine and the wind power abandonment volume of the wind farm can be effectively estimated, which has very important practical significance...

AI Technical Summary

Problems solved by technology

In addition, no more effective selection criteria or methods are given

As a result, the current method of selecting benchmark wind turbines in wind farms mainly depends on the subjective judgment of wind farm field operators, or through idealized modeling analysis with relevant modeling software. Selection of mock-ups for objective modification

[0004] The existing wind turbine model selection method leads to the fact that after the model machine is selected, it is seldom to verify whether the model machine can represent the actual operation of the entire wind farm. The impact of the model machine is an inevitable change, which will cause the fixed fan model machine to not fully reflect the operating status of the wind farm under all wind conditions, resulting in incorrect estimation of the wind farm’s abandoned wind volume, wind farm active power / Reactive power control errors, etc., which in turn lead to inaccurate prediction of electric field power, and the impact of electric field active / reactive power fluctuations on the power grid, which will affect the overall control of wind power resources by the power grid and the safe operation of the power grid to a certain extent

[0005] Especially when the prototype machine is in the maintenance state, the function of the prototype machine is in an error state, which will seriously affect the estimation of some electric field data based on the data of the sample machine and the regulation of the electric field fan

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