Power generation equipment parameter warning method based on DBN network

Abstract

The invention belongs to the field of power generation equipment online monitoring and fault diagnosis, and discloses a power generation equipment parameter warning method based on a DBN network. Thewarning method includes the following steps: (a) acquiring operation parameter data of power generation equipment; (b) building a DBN network model of cross fitting between the operation parameters ofthe equipment; (c) training the DBN network model; (d) using the trained DBN network model to acquire the predicted values of target parameters in real time; and (e) comparing the actual values of the operation parameters of the equipment with the predicted values in real time, and giving a warning for the parameters. Based on the basic idea that the current operation parameters of the equipmentreflect the state of the equipment, a cross fitting model between the parameters is built using the correlation between the parameters of the equipment, and the model is incrementally trained througha small-batch training method. Through the method, the problem that the time series model is inaccurate under variable-load conditions is solved, and meanwhile, the influence of equipment maintenanceand natural deterioration on the accuracy of the model is avoided, and the safe and economic operation ability of the equipment is improved.

Description

technical field [0001] The invention belongs to the field of on-line monitoring and fault diagnosis of power generation equipment, and more specifically relates to a parameter early warning method for power generation equipment based on a DBN network. Background technique [0002] With the continuous deepening of my country's electric power reform and the rapid development of inter-regional interconnected power grids, the safe and reliable operation of power generation equipment has become a key issue of concern for major power generation companies. It can be said that the safe and reliable operation of power generation equipment is directly related to the survival of enterprises, and the traditional equipment failure post-processing mode can no longer meet the safety of power generation in the new form, not only cannot guarantee the reliability of power generation equipment operation, but also virtually increases maintenance costs. Therefore, the budding intelligent fault e...

AI Technical Summary

Problems solved by technology

[0005] Aiming at the above defects or improvement needs of the prior art, the present invention provides a DBN network-based power generation equipment parameter early warning method, which builds and trains the DBN network model by collecting historical data and current data of multiple operating parameters of the equipment, and utilizes the equipment The correlation between operating parameters is used to predict the monitoring value of operating parameters at the next moment, thereby solving the technical problem of inaccurate prediction of time series models when unplanned adjustments occur in equipment operating conditions

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