i-manager Publications

K-Means Algorithm Based Input Reduction for Voltage Security State Classification of Power Distribution System

Gagari Deb*

Department of Electrical Engineering, Tripura University (A Central University), Agartala, Tripura, India.

Periodicity:October - December'2025
DOI : https://doi.org/10.26634/jps.13.3.22929

Abstract

Recent literature investigates that voltage insecurity may be dangerous for the power system as it causes sudden voltage collapse. For making real-time decisions regarding the security of the power system, it is very much essential to determine the current operating state of the system. This paper recommends a combined artificial neural network scheme to classify the operating conditions of a power system into normal, alarming, or insecure states. By choosing only the important input variables and eliminating unrelated ones, greater presentation is expected with lesser computational efforts. Here the K-means clustering method reduces the number of inputs for the proposed neural network structure to calculate the voltage security state with sufficient accuracy and speed. The success of the suggested technique is verified by two standard IEEE systems and one practical 85-bus system. Results show that the proposed K-means algorithm provides a compact artificial neural network model that can effectively and correctly recognize the working state of the power system with fewer numbers of inputs

Keywords

K-Means Clustering Algorithm, LVQ, Security State Classification, SOFM, Voltage Security.

How to Cite this Article?

Deb, G. (2025). K-Means Algorithm Based Input Reduction for Voltage Security State Classification of Power Distribution System. i-manager’s Journal on Power Systems Engineering, 13(3), 36-48. https://doi.org/10.26634/jps.13.3.22929

References

[1]. Ahila, R., Sadasivam, V., & Manimala, K. J. A. S. C. (2015). An integrated PSO for parameter determination and feature selection of ELM and its application in classification of power system disturbances. Applied Soft Computing, 32, 23-37.

[2]. Aly, M. M., & Abdel-Akher, M. (2018). A robust quasi-direct continuous power-flow analysis using two-bus equivalents for voltage stability analysis of radial distribution systems. Electrical Engineering, 100(1), 47-57.

[3]. Bahmanyar, A. R., & Karami, A. (2014). Power system voltage stability monitoring using artificial neural networks with a reduced set of inputs. International Journal of Electrical Power & Energy Systems, 58, 246-256.

[4]. Baz, I., Geymen, A., & Er, S. N. (2009). Development and application of GIS-based analysis/synthesis modeling techniques for urban planning of Istanbul Metropolitan Area. Advances in Engineering Software, 40(2), 128-140.

[5]. Chakraborty, K., De, A., & Chakrabarti, A. (2012). Voltage stability assessment in power network using self organizing feature map and radial basis function. Computers & Electrical Engineering, 38(4), 819-826.

[6]. Das, A. K., Sengupta, S., & Bhattacharyya, S. (2018). A group incremental feature selection for classification using rough set theory based genetic algorithm. Applied Soft Computing, 65, 400-411.

[7]. De, A., Chakraborty, K., & Chakrabarti, A. (2012). Classification of power system voltage stability conditions using Kohonen's self-organising feature map and learning vector quantisation. European Transactions on Electrical Power, 22(3), 412-420.

[8]. Deb, G., & Chakraborty, K. (2017). Artificial Neural Network Based Voltage Stability Analysis of Radial Distribution System. International Journal of Applied Engineering Research, 12(8), 1524-1528.

[9]. Deb, G., & Chakraborty, K. (2018). Gauss-Seidel Method based Voltage Security Analysis of Distribution System. International Journal of Electrical and Computer Engineering, 8(1), 43.

[10]. Deb, G., Chakraborty, K., & Deb, S. (2019). Spider monkey optimization technique–based allocation of distributed generation for demand side management. International Transactions on Electrical Energy Systems, 29(5), e12009.

[11]. Devaraj, D., & Roselyn, J. P. (2011). On-line voltage stability assessment using radial basis function network model with reduced input features. International Journal of Electrical Power & Energy Systems, 33(9), 1550-1555.

[12]. Devaraj, D., Roselyn, J. P., & Rani, R. U. (2007). Artificial neural network model for voltage security based contingency ranking. Applied Soft Computing, 7(3), 722-727.

[13]. Devaraj, D., Yegnanarayana, B., & Ramar, K. (2002). Radial basis function networks for fast contingency ranking. International Journal of Electrical Power & Energy Systems, 24(5), 387-393.

[14]. Fränti, P., & Sieranoja, S. (2018). K-means properties on six clustering benchmark datasets. Applied Intelligence, 48(12), 4743-4759.

[15]. Gan, G., & Ng, M. K. P. (2017). K-means clustering with outlier removal. Pattern Recognition Letters, 90, 8-14.

[16]. Gu, S., Cheng, R., & Jin, Y. (2018). Feature selection for high-dimensional classification using a competitive swarm optimizer. Soft Computing, 22(3), 811-822.

[17]. Hamada, M. M., Wahab, M. A., & Hemdan, N. G. (2010). Simple and efficient method for steady-state voltage stability assessment of radial distribution systems. Electric Power Systems Research, 80(2), 152-160.

[18]. Hashemi, S., & Aghamohammadi, M. R. (2013). Wavelet based feature extraction of voltage profile for online voltage stability assessment using RBF neural network. International Journal of Electrical Power & Energy Systems, 49, 86-94.

[19]. Hassan, L. H., Moghavvemi, M., Almurib, H. A., & Steinmayer, O. (2013). Current state of neural networks applications in power system monitoring and control. International Journal of Electrical Power & Energy Systems, 51, 134-144.

[20]. Huang, J., Li, G., Huang, Q., & Wu, X. (2017). Joint feature selection and classification for multilabel learning. IEEE Transactions on Cybernetics, 48(3), 876-889.