Water-turbine generator-set magnetic pull force imbalance fault analysis method based on online data

Abstract

The invention provides a water-turbine generator-set magnetic pull force imbalance fault analysis method based on online data. The method comprises the following steps of according to an unexcited time 1X component vibration vector and an idle condition 1X component vibration vector, calculating vibration and swing variations caused by stator and rotor eccentricity before and after excitation; according to unexcited time 2X-8X component vibration vector and an idle condition 2X-8X component vibration vector, calculating vibration and swing variations generated because a rotor is not round before and after excitation; according to an unexcited time swing gap value and an idle condition swing gap value, calculating a swing gap variation generated because a stator is not round before and after excitation; according to the swing gap variation, calculating an axis offset orientation delta phi ave generated because the stator is not round before and after excitation; if deltaA1x is greater than or equal to deltaA1_1x, determining that a stator and rotor eccentricity fault exists; if deltaA2x_8x is greater than or equal to deltaA1_2x_8x, determining that an out-of-round rotor fault exists; and if delta Save is greater than or equal to deltaS1_ave, determining an out-of-round stator fault exists, wherein an out-of-round most prominent orientation of the stator is located at the delta phi ave. In the invention, according to the online data, a magnetic pull force imbalance fault can be automatically analyzed and determined in real time and an analysis and diagnosis report can be automatically generated.

Description

technical field [0001] The invention belongs to the field of electronics, and in particular relates to an online data-based method for analyzing the unbalanced fault of the magnetic pull force of a hydroelectric generating set. Background technique [0002] The real-time diagnosis of the operation status of hydropower units is directly related to the safe and stable operation of hydropower stations, power quality and power production costs and other important economic benefit indicators, and its social benefits are huge. With the continuous expansion of power plant scale and monitoring auxiliary system, the amount of control and monitoring data information of the unit is increasing, and the operating personnel can effectively monitor the operating status of the unit in real time and make rapid and accurate judgments on equipment failures. Therefore, it is very necessary to research and develop intelligent hydroelectric unit fault diagnosis system. It is inevitable that vari...

AI Technical Summary

Problems solved by technology

This algorithm is more accurate than the traditional empirical algorithm, but it is very inconvenient for practical application

Especially for large hydroelectric generating units, due to the large number of stator slots and rotor poles, the finite element calculation is subject to many restrictions

[0017] In addition, the traditional analysis method for the magnetic tension unbalance fault of the hydro-generator set is oriented to the detection in the general frequency range, and cannot analyze the values ​​of different working conditions and different frequency domain range intervals.

Therefore, the fault description of the magnetic pull imbalance cannot be clear and accurate

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