Wind Turbine Fault Diagnoses Based On Cloud Model And Data Mining

Today the use of wind power has become a hot spot in various fields. But thewind turbine are often in a poor working environment, once a failure occurs, it willcause a huge loss. Therefore, the operational status of the wind turbine are monitoredand analyzed, and the failure to predict the trend has been processed, we can achieveefficient maintenance of wind turbines, so that generator maintenance efficiency andreduce the maintenance cost.In my thesis, we using cloud model and the method of data mining, have a faultevaluation and diagnosis of wind turbines. First, build a suitable evaluation system,choose the correct evaluation indicators; we apply cloud model randomness, andobtain membership of each index by the X condition cloud generator. This methodreduces the membership function artificially determined subjectivity. Finally, theexperiment proves that assessment calculation method based on cloud model moreaccurately determine the fault state, it is a very practical way.Taking into account the need to extractt in the wind turbine vibration faultdiagnosis time domain feature parameters, and too many parameters easily lead to adecision table attributes redundancy, this thesis uses rough set reduction algorithm.Due to disjunctive expression of the attribute reduction algorithm based on differencefunction calculation is overmuch, logic conversion cost is very big, and calculationprocess complicate, therefore, we will be converted to Boolean difference matrix, thematrix elements make position or calculation, remove redundant elements, thencalculate relative core to obtain a final extraction expressions. Examples show that thealgorithm is fast and easy. The advantages of rapid classification based on decisiontree, establish a rough decision tree to create a new decision tree model. To discretesample parameters, followed by an improved attribute reduction algorithm reduceproperty, we get the best reduction set, final generate the diagnosis rules for faultdiagnosis by means of a decision tree. The simulation results show the feasibility ofthis model in the fault diagnosis of wind turbine; with a single decision tree modelresults compare the accuracy and convenience.This thesis develops wind turbine fault diagnosis system based rough decisiontree，it not only can management database system, but also can monitor anddiagnose the wind turbine equipment’s fault.