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Showing 2 results for M. Kalantar
Reduced order model for doubly output induction generator in wind park using integral manifold theory
M. Kalantar, M. Sedighizadeh,
Volume 1, Issue 1 (January 2005)
Abstract
A dynamic reduced order model using integral manifold theory has been derived, which can be used to simulate the DOIG wind turbine using a double-winding representation of the generator rotor. The model is suitable for use in transient stability programs that can be used to investigate large power systems. The behavior of a wind farm and the network under various system disturbances was studied using this dynamic model. Simulation results of the proposed method represents that integral manifold method results fit the detailed model results with a higher precision than other methods.
FACTS Devices Allocation to Congestion Alleviation Incorporating Voltage Dependence of Loads
M. Gitizadeh, M. Kalantar,
Volume 4, Issue 4 (October 2008)
Abstract
This paper presents a novel optimization based methodology to allocate Flexible AC Transmission Systems (FACTS) devices in an attempt to improve the previously mentioned researches in this field. Static voltage stability enhancement, voltage profile improvement, line congestion alleviation, and FACTS devices investment cost reduction, have been considered, simultaneously, as objective functions. Therefore, multi-objective optimization without simplification has been used in this paper to find a logical solution to the allocation problem. The optimizations are carried out on the basis of location, size and type of FACTS devices. Thyristor Controlled Series Compensator (TCSC) and Static Var Compensator (SVC) are utilized to achieve the determined objectives. The problem is formulated according to Sequential Quadratic Programming (SQP) problem in the first stage. This formulation is used to accurately evaluate static security margin with congestion alleviation constraint incorporating voltage dependence of loads in the presence of FACTS devices and estimated annual load profile. The best trade-off between conflicting objectives has been obtained through Genetic Algorithm (GA) based fuzzy multi-objective optimization approach, in the next stage. The IEEE 14-bus test system is selected to validate the allocated devices for all load-voltage characteristics determined by the proposed approach.
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