Abstract: One of the most difficult problems in the operation of large synchronous turbine-generators is the detection of shorted turns in the DC-field of the rotor. Not only is the existence of a shorted turn in the field winding hard to detect, its correction may result in an expenditure of several hundred thousand dollars when including the cost of replacing the lost power generation with more expensive sources such as large nuclear powered machines. Unfortunately, this expense is incurred even in the case of a wrong diagnosis. This is because the major expense results from the disassembly and assembly of the machine and in the added cost of alternative production. Proper localization, and more important, accurate determination of the actual existence of a shorted-turn, is therefore essential to avoid huge unnecessary monetary losses. A general solution to this problem has so far remained elusive. In this project a twin signal signature sensing is used to monitor, detect, and localize shorts in power system equipment with windings, including rotors, transformers, motors and large synchronous turbine-generators. There has, to date, been no effective way to do so. The most obvious approach, time domain reflectometry, fails due to the reactive coupling in the windings. Twin signal signature sensing of shorts results from identical signals being simultaneously injected in both sides of the windings. The transmitted signals are difference to obtain the signature signal of the device. Through the monitoring of the evolution of the signature signals, development of winding shorts can be diagnosed through the process of novelty detection. Windings with shorts previously fingerprinted can be subjected to tests to localize the shorts. The standard layered perceptron neural network appears ideal to make these decisions. Preliminary work, performed on both downed and rotating loaded rotors, has been quite promising in demonstrating the effectiveness of the twin signal signature sensing approach to winding short evolution monitoring.
For more information, please contact M. A. El-Sharkawi