Method for fault diagnosis of wind turbines on basis of genetic neural network

Abstract

The invention discloses a method for the fault diagnosis of wind turbines on the basis of a genetic neural network, in particular to a method for modeling the fault diagnosis of wind turbines on the basis of the genetic neural network capable of learning the operating data of the wind turbines in the history, more particularly a method for judging the probability that faults occur to a gearbox, a generator and a yaw system by reading the real-time operating data of the wind turbines online and calling the diagnosis model of the genetic neural network to carry out the analysis on the real-time data, thus judging the fault state of the wind turbines. Based on the method combining the genetic algorithm with the neural network, the invention can achieve the algorithm complementation, improve the model convergence and diagnostic capacity and ensure higher robustness; and the method capable of carrying out the online monitoring and fault diagnosis on the operating state of the wind turbines on a real-time basis to take maintenance measures as soon as possible can improve the reliability of the wind turbines and reduce the maintenance cost.

Description

technical field [0001] The invention relates to a fault diagnosis method, in particular to a fault diagnosis method for a wind turbine based on a genetic neural network. Background technique [0002] With the aggravation of the energy crisis, countries all over the world are actively developing new energy sources. As a clean, pollution-free and renewable new energy source, wind energy has been valued by countries all over the world. Due to its mature technology and the highest commercial development value, wind power technology has achieved great development in recent years. However, along with the expansion of the wind power industry, failures of wind turbines also continue to appear. Due to the remote location of the wind turbine and the difficulty in maintenance, the failure of the unit often causes huge economic losses. Therefore, the development of wind turbine fault diagnosis and online real-time monitoring technology has become an urgent problem to be solved. [00...

AI Technical Summary

Problems solved by technology

When using these methods, the overall requirements for the measurement equipment are relatively high, and the investment in equipment is large. In addition, various signal analysis methods are often a reflection of the status of certain components of the unit, lacking the integrity of status monitoring, and practical reference experience is also required. more

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