Security prediction of high voltage transmission system (HVTS) based on k-nearest neighbor (k - NN) classifier technique

Article type: Research Article

Authors: Jaya, C. K.^{; *} | Sunitha, R. | Mathew, Abraham T.

Affiliations: Department of Electrical Engineering, National Institute of Technology, Calicut, Kerala-673601, India

Correspondence: [*] Corresponding authors. Jaya C. K., Department of Electrical Engineering, National Institute of Technology, Calicut, Kerala-673601, India. E-mail: [email protected].

Abstract: Even in the presence of renewable sources in the customer’s own premises, the utility’s supply needs to be maintained reliably to ensure the availability of electricity. Security prediction of the high voltage transmission system (HVTS) is significant in the modern scenario since the power failures results in huge economic loss or sometimes human life and comfort. The responsibility of the system operator is to supply electricity to its customers with a reasonably high degree of reliability and good power quality. This calls for assessing the security levels and initiating early steps to mitigate the effect of failures. Such prediction of the system security levels ensures availability of service to customers and helps in the operations planning. Paper proposes a pattern recognition approach using the k-nearest neighbor (k-NN) classifier for the security level predictions for HVTS from the failure rates assessed from the historical data on operation. This method is employing the newly developed Gaussian fuzzy index formulated by the authors using the failure rates in the HVTS. Both simulation and the validation using field data have been done and the results are given in the paper. The overall accuracy obtained is near 89.88%.

Keywords: Power System Security, High voltage transmission system, Gaussian fuzzy index, Security Prediction

DOI: 10.3233/JIFS-181617

Journal: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 6, pp. 5773-5782, 2019