Fault data prediction method, device and computer device of generator set

Abstract

The invention relates to a fault data prediction method, a device, a computer device and a storage medium of a generator set. The method comprises: acquiring operating parameters in the current operation process of the generator set; carrying out decision classification on the operation parameters by using an established decision tree model; acquiring the output classification information after the decision tree model decision classification; outputting fault data prediction results based on the classification information. By using the fault data prediction method of the generator set, the accuracy of the fault analysis can be improved, and the occurrence of safety accidents can be reduced, therefore, the fault prediction accuracy is high.

Description

technical field [0001] The present application relates to the technical field of power systems, and in particular to a method, device, computer equipment and storage medium for predicting fault data of generating sets. Background technique [0002] With the development of the economy, the user's demand for electricity continues to increase, so the capacity of the power plant generator set, the correlation between the generator sets, and the complexity of the unit are gradually increasing, resulting in an increase in the failure rate of the generator set. The failure of the generator set will affect the power generation of the power plant, making it impossible to complete the electricity required by the user on time, which will inevitably cause huge economic losses to the power company. In order to ensure that the power plant completes the power generation demand on time, the fault analysis of the generating set is extremely important. [0003] There are two traditional faul...

AI Technical Summary

Problems solved by technology

Since most generator sets are dynamically changing multi-coupling nonlinear systems, the generator sets are interlocked, the operating parameters are not independent, and the operating status of the generator sets is fuzzy, which makes the first fault analysis method prone to judgment errors. Low accuracy of failure analysis

The second method has defects such as slow convergence speed, difficulty in selecting the number of hidden layers, and the number of nodes, and also has the problem of low accuracy of fault analysis.

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