Principle Component Analysis Based Data Mining for Contingency Analysis on IEEE 30 Bus Power System
Lekshmi M1, M S Nagaraj2
1Lekshmi M, Research Scholar, Jain University, Asso. Prof. Dept of EEE,AIT, Bangalore, India.
2Dr.M S Nagaraj, HOD Dept. of EEE,BIET Davanagare, India.
Manuscript received on November 15, 2019. | Revised Manuscript received on 20 November, 2019. | Manuscript published on December 10, 2019. | PP: 878-882 | Volume-9 Issue-2, December 2019. | Retrieval Number: B7116129219/2019©BEIESP | DOI: 10.35940/ijitee.B7116.129219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: This Paper is an attempt to develop a Data Mining tool for the contingency of the power system. By mining the big data in the power system and analyzing the early detection of the contingency in the power system a larger cost cutting can be planned. As Mining would reduce the computational complexity of the contingency analysis this attempt would lead to reduction in the hardware use. This paper uses Multiclass Relevance Vector Machine(MCRVM) and Multiclass Support vector machine(MCSVM) in order to mine the data which include the voltage, power generated , power angles , power demand in different lines of the power system. The Data mining would need a data transformation technique, which would reduce the dimensionality of the data introduced for mining. The combination of Data cleansing and the Principal Component Analysis would act as the data transformation technique in this paper. A Matlab based simulation is carried using the IEEE 30 bus system for the contingency analysis by incorporating the loading risk assessment strategy using the Multiclass SVM and RVM and the results are compared and the outputs are tabulated. Active power performance index and the reactive power performance index are used in contingency analysis of the IEEE 30 bus system thus used and the accuracy of classification and the speed of classification with the different methods and the contingency rankings are found and displayed.
Keywords: Contingency Analysis, Principle Component Analysis, Artificial Neural Network, Support Vector Machine
Scope of the Article: Artificial Intelligence and Machine Learning