Dynamic Identification of Internal Damages in Induction Motors Based on Analysis Vector of Stator Currents

Abstract

The relevance of research is conditioned with huge dependence of thermal power plants (TPP) from reliable operation of the electrical drives of common auxiliaries. Almost the entire electric drive of common auxiliaries at TPP is operating based on powerful high-voltage squirrel cage induction motor (SCIM). Usually, work specifics of high-voltage SCIMs at TPPs are severe starting conditions (feeding pumps, grinders, crushers, smoke exhausters, blowing fans, etc.). SCIM failures of critical mechanisms at TPP lead either to the shutdown of the power unit, or at least to decrease of the level of electrical generation. At the same time, despite the most difficult and usually irreversible consequences from damage to squirrel cage rotor, (SCR) there is no protection against it. The problem is revealed only during the overhaul period. The breakage of the SCR bars is latent. It can exist for a sufficiently long time without having a critical effect on the machine work mode. Nevertheless, the fact of the malfunction can be considered as an emergency state of the SCIM.In this paper we purpose is development and experimental testing of diagnosis method of SCIM bar break based on mathematical data processing from sensors of stator current. For solving of set goal the theoretical and experimental research methods were applied. Theoretical ones include theory of electric drives, theory of electrical machines, numerical methods for approximating and smoothing experimental data, method of regression analysis. This research was conducted on the experimental installation which was specially created for goals of bar break diagnosis.During approximation of received signals of stator current the original detection algorithm of diagnosis attribute of rotors winding damage is proposed. The criterion of rotor circuit damage was identified and dependence of this criterion on the damage level of SCIM squirrel cage is obtained.