Power station is an important basic power generation organization, and its operation status is related to the continuous power generation capacity. At present, a large number of physical network equipment and intelligent equipment are used in pumped storage power station, which makes its data mass growth and its operation state become a difficult problem. Accurate operation monitoring results can provide decision support that power generation planners and government, but also reasonably dispatch corresponding resources. In the past, decision tree algorithm was used in operation condition monitoring, which has the problem of data distortion and affects the accuracy of monitoring results. Based on the above reasons, this paper combines the wavelet function and decision tree algorithm, proposes an improved decision tree algorithm to eliminate redundant data in order, and uses wavelet function to cluster distorted data, so as to improve the accuracy and computational efficiency of the algorithm. Matlab simulation results show that: decision tree algorithm can eliminate 90% of redundant data, reduce the impact of feature data extraction on decision tree. At the same time, the improved accuracy is 98%, the calculation time is less than 25s is better than that, the decision tree algorithm. Therefore, the improved algorithm can optimize the condition monitoring of pumped storage power station.
Fault Diagnosis of Single Point Cutting Tool through Vibration Signal Using Decision Tree Algorithm
