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Showing 2 results for Diagnosis

H. Yaghobi, K. Ansari, H. Rajabi Mashhadi,
Volume 7, Issue 4 (12-2011)
Abstract

A reliable and accurate diagnosis of inter-turn short circuit faults is a challenging problem in the area of fault diagnosis of electrical machines. The purpose of this challenge is to be more efficient in fault detection and to provide a reliable method with low-cost sensors and simple numerical algorithms which not only detect the occurrence of the fault, but also locate its position in the winding. Hence, this paper presents a novel method for diagnosis of different kinds of inter-turn winding faults in a salient-pole synchronous generator using the change in the magnetic flux linkage. It describes the influence of inter-turn winding faults on the magnetic flux linkage distribution of the generator. The main feature of the proposed method is its capability to identify the faulty coils under two types of inter-turn winding faults. Also, simple algorithm, low cost sensor and sensitivity are the other feature in the proposed technique. In this method, generator air gap flux linkage is measured via search coils sensor installed under the stator wedges. Theoretical approach based on Finite Element Method (FEM) together with experimental results derived from a 4-pole, 380U, 1500 rpm, 50 Hz, 50 KVA, 3-phase salient-pole synchronous generator confirm the validity of the proposed method.
H. Yaghobi,
Volume 13, Issue 1 (3-2017)
Abstract

Condition monitoring and protection methods based on the analysis of the machine's current are widely used according to non-invasive characteristics of current transformers. It should be noted that, these sensors are installed by default in the machine control center. On the other hand, condition monitoring based on mathematical methods has been proposed in literature. However, they are model based and are too complex. Artificial neural network (ANN) methods are robust and less model dependent for fault diagnosis when the fault signature can be directly achieved using the sampling data. In this procedure, the state of internal process will be ignored. Therefore, generalized regression neural network (GRNN) based method is presented in this paper that uses negative sequence currents (calculated from the machine's currents) as inputs to detect and locate an inter-turn fault in the stator windings of the induction motor. Turn-to-turn fault by changing the contact resistance and various numbers of shorted turns for realizing the fault severity has been modeled by Matlab/Simulink. The simulation and experimental results show that the proposed method is effective for the diagnosis of stator inter-turn fault in induction motor under the supply voltage unbalances.



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