NDLI: A new kernel-based clustering algorithm for the multi-machine equivalent of large power systems

Content Provider	IEEE Xplore Digital Library
Author	Xingzhi Wang Zheng Yan Li Li Dong Xie
Copyright Year	2008
Description	Author affiliation: Dept. of Electr. Eng., Shanghai Jiao Tong Univ., Shanghai (Xingzhi Wang; Zheng Yan; Li Li; Dong Xie)
Abstract	Formulating the power system equations with respect to the systempsilas inertial center, it is shown that a large disturbance tends to pull each generator from the rest of the generators, forming a multi-machine equivalent. Clustering generators is a useful general technique in data mining of large-scale power grid data sets. This paper presents a new kernel-based clustering algorithm for multi-machine equivalent of large power systems. Based on the multi-machine equivalent concept, transient stability assessment of a multi-machine power system is made, and a criterion for identifying the generators likely to separate from the system (by loss of synchronism) and the energy associated with this separation is developed. The criterion is applied successfully to a 491-generator power system. Experimental results indicate that the new algorithm is efficient and effective at finding both good clustering and the appropriate number of clusters.
Starting Page	3936
Ending Page	3939
File Size	699843
Page Count	4
File Format	PDF
ISBN	9781424438266
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-10-17
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	IEEE Consumer Electronics Society(CES)
Subject Keyword	Clustering algorithms Power systems Power generation Power system stability Power system transients Equations Mesh generation Data mining Large-scale systems Power grids
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Fast Transient Stability Assessment Based on Data Mining for Large-Scale Power System

Transient Stability Analysis of Large-Scale Power Systems Based on Reduce Feature

Multi-support vector machine power system transient stability assessment based on relief algorithm

A message-passing distributed-memory parallel power flow algorithm

A Data Driven Knowledge Acquisition Method and Its Application in Power System Dynamic Stability Assessment

Outlier Identification and Justification Using Multi-Objective PSO based Clustering Algorithm in Power System

A New Clustering Method Based on Weighted Kernel K-Means for Non-linear Data

Real-Time Transient Stability Prediction Based on Relevance Vector Learning Mechanism for Large-Scale Power System

A scalable parallel subspace clustering algorithm for massive data sets

A new kernel-based clustering algorithm for the multi-machine equivalent of large power systems

Similar Documents

Fast Transient Stability Assessment Based on Data Mining for Large-Scale Power System

Transient Stability Analysis of Large-Scale Power Systems Based on Reduce Feature

Multi-support vector machine power system transient stability assessment based on relief algorithm

A message-passing distributed-memory parallel power flow algorithm

A Data Driven Knowledge Acquisition Method and Its Application in Power System Dynamic Stability Assessment

Outlier Identification and Justification Using Multi-Objective PSO based Clustering Algorithm in Power System

A New Clustering Method Based on Weighted Kernel K-Means for Non-linear Data

Real-Time Transient Stability Prediction Based on Relevance Vector Learning Mechanism for Large-Scale Power System

A scalable parallel subspace clustering algorithm for massive data sets

A new kernel-based clustering algorithm for the multi-machine equivalent of large power systems