Method for foresting demand of wind generating set for spare parts based on fault tree analysis

Abstract

The invention discloses a method for forecasting the demand of a wind generating set for spare parts based on fault tree analysis. The method comprises the steps of firstly building an abstract model of a wind generating set fault tree; then acquiring the daily annual fault probability of components corresponding to each bottom event in the fault tree; building a fault tree simulation model by using a Simulink platform, and simplifying a Boolean function expression of a top event; solving a minimum cutest of the fault tree; compiling a numerical operation sub-function for calculating the probability importance degree and key importance indexes of each bottom event; inputting the daily annual fault probability of each bottom event by a user, and calling the sub-function to output the probability importance degree and key importance indexes; and calculating the number of spare parts of each component in a future stage. The method disclosed by the invention is based on the wind generating set fault tree, adjustable in model parameter, capable of adapting to product differentiation of different manufacturers and different working conditions, fault reasons of the wind generating set are detailed, the utilization efficiency of the spare parts is improved, the inventory cost is reduced, and so that the objective of reducing the operation and maintenance cost of wind power generation enterprises is achieved.

Description

technical field [0001] The invention relates to the field of fault diagnosis of wind power generators, in particular to the problem of spare parts for wind power generators with variable speed and variable pitch. Background technique [0002] As a green, pollution-free and renewable new energy, wind energy is of great significance for solving environmental pollution and energy crisis. In recent years, wind power conversion technology has developed rapidly around the world, and the field of wind power generation has sprung up. By the end of 2016, the total installed capacity of wind turbines reached 486,749 megawatts, of which China accounted for 34.66%, ranking first in the world. [0003] As a new type of equipment in an emerging industry, wind turbines also show new characteristics in the demand for spare parts: there are many types of spare parts, large quantities of demand, poor versatility of spare parts, long procurement cycle, and loss of spare parts due to shortage ...

AI Technical Summary

Problems solved by technology

[0005] In order to overcome the problem of inaccurate results of the existing demand forecasting and analysis methods for spare parts of wind power generating sets, the present invention provides a demand forecasting method for spare parts of wind power generating sets based on fault tree analysis

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