Electric equipment fault forecasting method based on multi-dimension time sequence

Abstract

The invention provides an equipment fault forecasting method based on a multi-dimension time sequence. A data excavation method based on the multi-dimension time sequence is provided according to highly densely sampled on-line operating electric measurement data in a power system, a variation feature of other equipment related to the fault, namely a 'precursor event' is excavated according to a historical time sequence training data building and time sequence decomposition algorithm, a feature event generation algorithm and a fault relationship excavation algorithm based on relation rules, the relation forms an equipment fault forecasting model, and powerful support is provided for fault forecasting and judgment of complex non-linear electric equipment through combination with on-line monitoring data. By means of the electric equipment fault forecasting method based on the multi-dimension time sequence, faults or impact possibly striking on the core equipment of a power enterprise is forecast in advance by effectively using the massive high-density operating monitored historical data of the equipment, and prevention measures are taken in time to avoid the faults and impact.

Description

technical field [0001] The invention belongs to the interdisciplinary research field of computer technology and electric power, and specifically proposes a multi-dimensional time series-based equipment failure prediction method for electric power systems. Background technique [0002] In the power industry, some equipment is large-scale equipment that maintains the operation of the power grid, such as transformers in substations, steam turbines, generators, and excitation systems in power stations. The normal progress will also cause huge losses. Serious accidents of large steam turbines at home and abroad are typical examples. Therefore, in order to take preventive measures in time and avoid unnecessary losses, it is very important to predict the failure of these core devices. [0003] The traditional time series forecasting is to use a linear model to fit the data series, which has a good result for the linear system, but it is not suitable for the forecasting of the non...

AI Technical Summary

Problems solved by technology

In the current research, the transient data generated when the fault occurs, such as wave recording files, alarms, etc., are often used for relatively independent single analysis, and it is difficult to realize the prediction of these faults and impacts

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