Wind generating set fault prediction method based on D-S evidence fusion

Abstract

A wind generating set fault prediction method based on D-S evidence fusion is disclosed. In the method, for two kinds of signals, two support vector machines after parameter optimization are constructed, the two support vector machines are taken as two evidences, and after D-S fusion, a final prediction fault type is given. The method has advantages that (1) in a traditional vibration method, only a vibration signal is analyzed, and a neural network, a decision tree and other machine learning algorithm models are constructed according to a vibration energy characteristic vector; but the vibration signal is only observed so that some fault states can be misclassified, for instance, a bearing damage and rotor eccentricity can cause a vibration signal abnormity, and at this time, a current signal can be used to distinguish two kinds of fault states; and (2) a prediction model established in the method can be stored, historical data does not need to be repeatedly extracted and trained, and under a real-time prediction environment of a wind field, a prediction result can be quickly provided.

Description

technical field [0001] The invention relates to a fault prediction method for a wind power generating set, in particular to a method for fusing vibration energy characteristics and current energy characteristics with D-S evidence, and belongs to the technical field of wind turbine fault prediction. Background technique [0002] With the widespread promotion and use of clean energy, wind power technology has become an important research object of clean and renewable energy. However, most of the wind farms are inaccessible and the natural environment is extremely harsh. Real-time monitoring of the status of wind turbines has become more and more difficult. Especially the large-scale promotion of offshore wind turbines has also made wind turbine fault diagnosis challenging. However, wind turbines are expensive to manufacture. If the wind turbines are found to be in poor condition at an early stage and personnel are dispatched to repair them on site in time, some failures will c...

AI Technical Summary

Problems solved by technology

However, most of the wind farms are inaccessible and the natural environment is extremely harsh. It becomes more and more difficult to monitor the status of wind turbines in real time. Especially, the large-scale promotion of offshore wind turbines also makes the fault diagnosis of wind turbines challenging.

However, wind turbines are expensive to build. If the wind turbines are found to be in poor condition at an early stage, and personnel are dispatched to repair them on the spot in time, some faults can be prevented from continuing to deteriorate and eventually causing great economic damage.

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