Running state monitoring and diagnosing method of hydraulic turbine set

Abstract

The invention discloses a running state monitoring and diagnosing method of a hydraulic turbine set. The method comprises friction fault monitoring steps that 1) friction detection signals, including a horizontal vibration signal, a vertical vibration signal and a water leading throw signal, of a top cover of the detected hydraulic turbine set are collected; 2) a friction characteristic vector is constructed according to the friction detection signals of the top cover; 3) a weighted vector method is carried out on different friction characteristic vectors to generate a weighed friction characteristic vector; and 4) vector distance between the weighted friction characteristic vector and a preset typical friction characteristic vector is calculated, and if the vector distance is lower than a given threshold, it is determined that a friction fault occurs in the detected hydraulic turbine set. The method can be used to detect and diagnose the friction fault occurred in the detected hydraulic turbine set accurately, functions of a state monitoring system of a hydraulic power plant are completed, more useful information is provided for remote analysis and diagnosis of experts, and occurred faults can be handled in a targeted manner.

Description

technical field [0001] The invention relates to a state monitoring analysis and diagnosis technology of a rotating machine, in particular to a method for monitoring and diagnosing an operating state of a water turbine unit. Background technique [0002] With the increase of the unit capacity of hydroelectric units and the increasing proportion in the power system, its importance is becoming more and more prominent. Due to the aging of unit equipment and metal structures in some old power plants after many years of operation, some defects have affected the safe operation of the power plant. In particular, remote monitoring technologies such as unit stability, cavitation, cracks, and energy indicators are still incomplete, and remote real-time diagnosis and decision-making technologies and methods are lacking. Therefore, the study of monitoring and real-time decision-making technology based on the working status of hydroelectric units can effectively ensure the safe operation...

AI Technical Summary

Problems solved by technology

At present, the research on monitoring and diagnosis of hydropower units mainly focuses on the monitoring of operating parameters, but lacks effective decision-making and diagnosis techniques.

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