کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
385523 660868 2011 8 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Particle swarm optimization based K-means clustering approach for security assessment in power systems
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
Particle swarm optimization based K-means clustering approach for security assessment in power systems
چکیده انگلیسی

Security assessment is a major concern in planning and operation studies of a power system. Conventional method of security evaluation performed by simulation involves long computer time and generates voluminous results. This paper presents a K-means clustering approach for classifying power system states as secure/insecure under a given operating condition and contingency. This paper demonstrates how the traditional K-means clustering algorithm can be profitably modified to be used as a classifier algorithm. The proposed algorithm combines particle swarm optimization (PSO) with the traditional K-means algorithm to satisfy the requirements of a classifier. The proposed PSO based K-means clustering technique is implemented in IEEE 30 Bus, 57 Bus, 118 Bus and 300 Bus standard test systems for static security and transient security evaluation. The simulation results of the proposed algorithm are compared with unsupervised K-means clustering, which uses different methods for cluster center initialization.

Research highlights
► Presents K-means clustering approach for power system security classification.
► Proposed algorithm combines particle swarm optimization with traditional K-means algorithm.
► We have used different methods for cluster center initialization in K-means algorithm.
► PSOKM algorithm applied for static and transient security assessment in power system networks.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Expert Systems with Applications - Volume 38, Issue 9, September 2011, Pages 10839–10846
نویسندگان
, ,