Volume 20, Issue 2 (4-2001)
2001, 20(2): 27-42 |
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M.E. Hamedani Golshan, H. Ghoudjehbaklou and H. Seifi. Application of Pattern Recognition Algorithms for Clustering Power System to Voltage Control Areas and Comparison of Their Results. Journal of Advanced Materials in Engineering (Esteghlal) 2001; 20 (2) :27-42
URL: http://jame.iut.ac.ir/article-1-226-en.html
URL: http://jame.iut.ac.ir/article-1-226-en.html
Abstract: (6771 Views)
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.
Keywords: Voltage stability Voltage weak area, Voltage control area, Corrective control, Pattern recognition, Kohonen neural network, Fuzzy C-Means algorithm, Fuzzy Kohonen algorithm.
