Synchronized measurements-based wide-area static security assessment and classification of power systems using case based reasoning classifiers. (May 2018)

• Record Type: Journal Article
• Authors: T, Venkatesh; Jain, Trapti
• Is Part Of: Computers & electrical engineering. Volume 68(2018)
• Page Start: 513
• Page End: 525
• Publication Date: 2018-05
• Subjects: Case-based reasoning -- Class imbalance -- Composite security index -- Feature selection -- Phasor measurement unit -- Power system monitoring -- Rule based expert systems -- Static security assessment -- Synchronized measurements -- Wide-area security; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2018.05.008
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: This paper proposes a human intuition-based concept of Case-Based Reasoning (CBR) for Static Security Assessment (SSA) of power systems. Prior to security assessment, a composite Security Index (SI) defined in terms of line overload and voltage violation, is computed for each training pattern to label them into either secure or insecure class. Out of all phasor measurements obtained using Phasor Measurement Units (PMUs), only the real and reactive power flows, which are identified as relevant features are given as inputs to the proposed CBR system. The proposed approach has been implemented on IEEE 14 bus, IEEE 30 bus and Indian 246 bus networks and the results have been compared with those obtained using Support Vector Machine (SVM) and Artificial Neural Network (ANN) based classifiers. The results indicate that the proposed approach is more efficient, reliable and outperforms the above said classifiers in terms of classification accuracy at a comparable computation cost.