Showing 3 results for Abbaszadeh
F. Bagheri, H. Khaloozadeh, K. Abbaszadeh,
Volume 3, Issue 3 (October 2007)
Abstract
This paper presents a parametric low differential order model, suitable for
mathematically analysis for Induction Machines with faulty stator. An adaptive Kalman
filter is proposed for recursively estimating the states and parameters of continuous–time
model with discrete measurements for fault detection ends. Typical motor faults as interturn
short circuit and increased winding resistance are taken into account. The models are
validated against winding function induction motor modeling which is well known in
machine modeling field. The validation shows very good agreement between proposed
method simulations and winding function method, for short-turn stator fault detection.
F. Tootoonchian, K. Abbaszadeh, M. Ardebili,
Volume 8, Issue 3 (September 2012)
Abstract
Resolvers are widely used in electric driven systems especially in high precision servomechanisms. Both encapsulated and pancake resolvers suffer from a major drawback: static eccentricity (SE). This drawback causes a significant increase in resolver output position error (RPE) which could not be corrected electronically. To reduce RPE, this paper proposes a novel structure with axial flux. Proposed topology, design guidelines, optimization procedure and several key features to improve the sensitivity of axial flux resolver (AFR) against SE are studied. Furthermore, to minimize RPE an optimized design is attained. The machines are investigated in detail by using d-q model and 3D time stepping finite-element analysis. The results of theses two methods are compared and both prototype machines (proposed and optimized) are built. In order to evaluate proposed topologies, an experimental test setup is devised. Finally, the experimental results of the prototype machines verified the analysis results.
S. Hajiaghasi, K. Abbaszadeh, A. Salemnia,
Volume 15, Issue 1 (March 2019)
Abstract
Interturn fault detection is a challenging issue in power transformer protection. In this paper, interturn faults of distribution transformer are studied and a new online detection method based on vibration analysis is proposed. Transformer electromagnetic forces are analyzed by time stepping finite element (TSFE) modeling of interturn fault. Since the vibration associated with inter-turn faults is caused by electromagnetic forces, axial and radial electromagnetic forces for various interturn faults are studied. Transformer winding vibration under interturn faults is studied through an equivalent mathematical model combined with electromagnetic force analysis. The results show that it is feasible to predict the interturn winding faults of transformer windings with the transformer vibration analysis method. Simulation and experimentation studies are carried out on 20/0.4 kV, 50 kVA distribution transformer. The results confirm the effectiveness of the proposed method.
