Showing 6 results for H. Seifi
R. Hooshmand, H. Seifi, and V. Tahani,
Volume 15, Issue 2 (1-1997)
Abstract
In this article, an effective method to control a power system during emergency conditions is presented. Based on Fuzzy Linear Programming (FLP), a new technique is developed to solve the Load Shedding and Generation Reallocation (LSGR) optimization Problem. The objective function consists of terms of load curtailments and deviations in generation schedules. The constraints are power system variables limitations. The objective function and constraints coefficients are uncertain, thus it is more appropriate to use fuzzy linear programming. Considering the network frequency as an essential variable, and using the electrical load model, a fuzzy environment is prepared to solve more realistically and successfully the LSGR optimization problem. The results of various cases of fuzzy and crisp modes of the problem are demonstrated. It will be observed that the application of the FLP on one hand, will provide a more realistic model of power systems, and on the other hand, will cause a reduction in the values of the objective function.
M. Abedi, S. A. Taher, A. K. Sedigh and H. Seifi,
Volume 17, Issue 2 (4-1998)
Abstract
This paper deals with the design and evaluation of a robust controller for static VAR compensator (SVC) in remote industrial power systems to enhance the voltage profile for three-phase single cage induction motor (SCIM) loads. The controller design is based on H∞ theory to deal with uncertainties arising in industrial network modelling.
The performance of the H∞ controller has been evaluated extensively through non-linear time domain simulation. It is concluded that the robust controller (RSVC) enhances the voltage profile for SCIM loads compared with the optimal (OSVC) type which consists of optimal state feedback (LQR).
G. R. Yousefi and H. Seifi,
Volume 19, Issue 2 (1-2001)
Abstract
Load modeling is widely used in power system studies. Two types of modeling, namely, static and dynamic, are employed. The current industrial practice is the static modeling. Static modelss are algebraic equations of active and reactive power changes in terms of voltage and frequency deviations. In this paper, a component based on static modeling is employed in which the aggregate model is derived based on the sensitivity coefficients and participation factors of load components. As an induction motor comprises a significant portion of industrial loads, Artificial Neural Network (ANN) is employed to derive its static model readily from nameplate data as accurately as possible.
M.e. Hamedani Golshan, H. Ghoudjehbaklou and H. Seifi,
Volume 20, Issue 2 (4-2001)
Abstract
Finding the collapse susceptible portion of a power system is one of the purposes of voltage stability analysis. This part which is a voltage control area is called the voltage weak area. Determining the weak area and adjecent voltage control areas has special importance in the improvement of voltage stability. Designing an on-line corrective control requires the voltage weak area to be determined by a sufficiently rapid and precise method. In this paper, a new algorithm based on assigning a vector to
each power system bus is presented. These vectors indicate buses conditions from the viewpoint of voltage stability. In this new method, using the clustering methods such as kohonen neural network, fuzzy C-Means algorithm and fuzzy kohonen algorithm, voltage control areas are determined The proposed method has advantages such as determining PV and PQ buses which belong to the weak area simultanously, under all operating conditions and without a need to system model. Also by comparing the results of applying clustering methods, it has been observed that, due to simplicity of implementation and precision of the results, the two dimensional kohonen neural network is a more suitable tool for clustering power system to voltage control areas than the fuzzy C-Means and fuzzy kohonen methods.
Keywords: Voltage stability, Voltage weak area, Voltage control area, Corrective control, Pattern recognition, Kohonen neural network, Fuzzy C-Means algorithm, Fuzzy Kohonen algorithm.
R. Keypour, H. Seifi, A. Yazdian,
Volume 21, Issue 1 (7-2002)
Abstract
In this paper, two algorithms have been developed for allocation and size determination of Active Power Filters (APF) in power systems. In the first algorithm, the objective is to minimize harmonic voltage distortion. The objective in the second algorithm is to minimize the new APF injection currents while satisfying harmonic standards. Genetic algorithm is proposed for these two optimization problems. The simulation results for an 18-bus system show the effectiveness of the genetic algorithm for these two optimization problems.
Keywords: Genetic Algorithm, Active Power Filter, Harmonics, Allocation, Optimization
S. H. Hosseini, H. Seifi, M. Parsa, M. R. Omidkhah, M. Farmad and M. Gaznavi,
Volume 24, Issue 1 (7-2005)
Abstract
Generation Expansion Planning (GEP) is one of major modules of power system planning studies, normally performed for the nex 10-30 years horizon. The current industrial practices are to find the generation requirements based on a
nodal analysis. In this way, the allocations are not determined and subequent studies are required to find the exact locations
which as decomposed from the earlier stage, may result in non-optimum solution. A new approach is proposed in this paper in which, based on dynamic programming and sensitivity factors, GEP is performed with due to consideration of transmission
system effects. In this way, the allocations of justified generation plants are also determined. The results for Iranian Power Grid for the years 2011 to 2021 are demonstracted.
