Abstract
Assessing and mitigating problems associated with voltage security remains a critical concern for many power system operators. It is well understood that voltage stability, is driven by the balance of reactive power in a system. Of particular interest is to identify those areas in a system that may suffer reactive power deficiencies. Establishing the reactive power reserve requirements in these Voltage Control Areas (VCAs), to ensure system integrity is of paramount importance. Since speed of analysis is critical for on-line applications the approach will address the development of a scheme whereby VCAs can be identified using data mining techniques from on-line power system snapshot. The database with user-friendly interface for storing/retrieving result of Modal Analysis can be used to construct decision trees (DTs) for each of the identified VCAs using key power system attributes. In on-line application, the relevant attributes is extracted from a system snapshot and is dropped on DTs to determine which of the pre-determined VCAs can exist in the present power system condition.
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Lis, R.A. (2011). Application of Data Mining Techniques to Identify Critical Voltage Control Areas in Power System. In: Jędrzejowicz, P., Nguyen, N.T., Hoang, K. (eds) Computational Collective Intelligence. Technologies and Applications. ICCCI 2011. Lecture Notes in Computer Science(), vol 6922. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23935-9_15
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DOI: https://doi.org/10.1007/978-3-642-23935-9_15
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