Evaluation of different monitoring parameters for synchronous machine fault diagnostics

Abstract

Early detection and diagnosis of faults in synchronous machine (SM) are crucial since they drive critical applications. In this paper, the authors exploited the combined information from numerical simulation and experimental validation to evaluate the merits of certain fault indicators, for detecting machine faults. A specially designed SM was chosen for both simulation and measurement with the ability to physically introduce electro-mechanical failures. Shorted turn fault was made through accessible intermediate terminals of respective stator and rotor windings. Eccentricity faults of different intensity were created by suitable changeover of customized end-shield. Finite element method was used to develop healthy and faulty simulation models. The experimental setup facilitated measurement and subsequent calculation of key parameters such as stator branch currents, phase current, circulating current, rotor field current and shaft voltage. From the investigation of both simulation and experimental data, field current and shaft voltage were found to be two effective fault indicators from safety and reliability prospects. Both indicators have the ability to identify and discriminate between electro-mechanical and electrical faults. Further, it is demonstrated that with availability of physical dimensions and measured rotor parameters, a know-how can be created to detect failures, well before a catastrophic event occurs.

Application of Identification Reference Nets for the Preliminary Modeling on the Example of Electrical Machines

“…At the EM design stage, the model is dedicated to optimizing both its structure and parameters [7,8]. In terms of diagnosis, the model is the basis for synthesizing the appropriate state examination algorithm [9][10][11], while in the case of their use and control, the model is necessary for making the right decisions related to relevant exploiting activities [12,13]. It is noteworthy that the classical modeling methods are burdened with significant implementation costs [14], which very often prevents the implementation of new control techniques for the electric power system (EPS).…”

Industrial Data-Driven Monitoring Based on Incremental Learning Applied to the Detection of Novel Faults

“…A great deal of industrial applications is supported by the transformation of electrical to mechanical energy through electric machines, which, nowadays, are particularly oriented to permanent-magnet synchronous motors (PMSM). The PMSM are considered a clear future choice due to their high-speed operation and precise torque control, high torque to current ratio as well as a high-power density and high efficiency [3][4][5]. However, despite its characteristics, the appearance of unexpected faults may occur at any time due to wear and tear of different parts, causing undesired breakdowns that affect productivity and, in consequence, economic effectiveness.…”

Diagnosis of interturn fault in stator winding of turbo‐generator

“…There are the most probable fault occurrences in the stator of the synchronous generator, and it generally forms 60% of faults in the machine. 3 The stator fault is commenced by the insulation failure between two conductors or between a conductor and the ground. The insulation destruction may be caused by the electrical, mechanical, and thermal stresses.…”

“…Fault may occur in different parts of the machine. There are the most probable fault occurrences in the stator of the synchronous generator, and it generally forms 60% of faults in the machine . The stator fault is commenced by the insulation failure between two conductors or between a conductor and the ground.…”