New Classifier Design for Static Security Evaluation Using Artificial In-telligence Techniques
Year: 2016
Type: article
Abstract: This paper proposes evaluation and classification classifier for static security evaluation (SSE) and classifica-tion. Data are generated on (30, 57, 118 and 300) bus IEEE test systems used to design the classifiers. The implementation decision tree methods on several IEEE test systems involved appropriateness SSE and classi-fication by using four algorithms of DT’s. Empirically, with the present of FSA, the implementation results indicate that these classifiers have the capability for system security evaluation and classification. Lastly, FSA is efficient and effective approach for real-time evaluation and classification classifier design.
Authors: Ibrahim Saeh, Wazir Mustafa, Nasir Al-geelani
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
Funder: Universiti Teknologi Malaysia
OpenAlex ID: W4253308380