Cost Control Model of Power Grid Maintenance using Fuzzy Pattern Recognition Theory

M. Bhargavi*, S. Vijayalakshmi**
*-** Assistant Professor, Department of Computer Science and Engineering, Sree Vidyanikethan Engineering College, Tirupati, India.
Periodicity:March - May'2016
DOI : https://doi.org/10.26634/jpr.3.1.8104

Abstract

In power enterprises, the construction process is complicated using lines and equipment maintenance, the cost is affected by meteorological and geographical factors, which influence mode in uncertain. In this paper, the authors use a predictive control model to control the project cost using fuzzy clustering method and the threshold intervals of the objective function in clusters. This model uses relative fuzzy operator to build fuzzy matrix, construct correlation between factors, and describe the factors' effect. Extract the cluster's Eigen function, define the boundaries of various clusters, and determine the type of the predicted points and the range of the objective function. When the actual cost of the maintenance project is within the range calculated by the cost model, then it is normal. If the actual cost exceeds this range, then further analysis of all the aspects of the cost is needed to find out the reason.

Keywords

Cost Management, Predictive Control, Fuzzy Clustering, Control Interval

How to Cite this Article?

Bhargavi, M., and Vijayalakshmi, S. (2016). Cost Control Model of Power Grid Maintenance using Fuzzy Pattern Recognition Theory. i-manager’s Journal on Pattern Recognition, 3(1), 16-22. https://doi.org/10.26634/jpr.3.1.8104

References

[1]. Hobbs, Peter. (2003). Essential Managers: Project Management. Beijing:China's International Broadcasting Publishing House.
[2]. Liu, Huifen, (2002). “The Appliance of Standard Cost Method In Cost Control Syste”. Journal of Industrial Technological Economics, Vol.3, pp.70-71.
[3]. Christopher P. Holland, Duncan R. Shaw, Peter Kawalek, et al. (2005). “BP's multi-enterprise asset management system”. Information and Software Technology, Vol.47, No.15, pp.999-1007.
[4]. Cooper, R. (2000). “Cost management: From Frederick Taylor to the present”. Journal of Cost Management, Vol.12, No.3, pp.34-36.
[5]. T.C. Berends, and J. S. Dhillon. (2004). “An Analysis of Contract Cost Phasing on Engineering Construction and Construction Projects”. The Engineering Economist, Vol.4, No.23, pp.327- 337.
[6]. Ma, Xingbin, Meng Xiangjun, et al. (2014). “A Research On the Reliability, Adaptability and Economy of Power Grid Operation (M)”. Shandong University Press, Vol.8, pp.48-52.
[7]. Chen, Yingchun, Song Yexin and Wu Xiaoping, (2002). “Cost Prediction of Warship Maintenance Based On Fuzzy Logic”. Journal of Naval University of Engineering, Vol.14, No.1, pp.6-9.
[8]. Liu, Baoping, Sun Shengxiang, et al. (2004). “The Appliance of ANFIS Network In Cost Prediction of Warship Maintenance”. Journal of Naval University of Engineering, Vol.8.
[9]. A. K. Jain, (2010). “Data clustering: 50 years beyond Kmeans”. Pattern Recognition Letters, Vol.31, pp.651-666.
[10]. M. Falasconi, A. Gutierrez, M. Pardo, G. Sberveglieri, and S. Marco. (2010). “A stability based validity method for fuzzy clustering”. Pattern Recognition, Vol.43, No.4, pp.1292-1305.
[11]. Zkim Le, (1996). “Fuzzy relation compositions and pattern recognition”. Information Sciences, Vol.89, pp.107-130.
[12]. K. L. Wu, J. Yu, and M. S. Yang, (2005). “A novel fuzzy clustering algorithm based on a fuzzy scatter matrix with optimality test”. Pattern Recognition Letters, Vol.26, No.5, pp.639-652.
[13]. Ruspini E H. (1969). “A new approach to clustering”. Information and Control, Vol.15, No.1, pp.22-32.
[14]. Pal N R, Pal K, Bezdek J C, et al. (2005). “A possibilistic fuzzy C-Means clustering algorithm”. IEEE Trans Fuzzy Systems, Vol.13, No.4, pp.517-530.
[15]. X. Z. Wang, Y. D. Wang, and L. J. Wang, (2004). “Improving fuzzy feature C-means clusteringbased on feature-weight learning” [J]. Pattern Recognition Letters, Vol.25, No.10, pp.1123-1132.
[16]. Wang, Peizhuang, (1985). Project of Random Sets and Fuzzy Sets, Beijing Normal University Press, pp.58-66.
[17]. Han, Liya, (1988). Fuzzy Valued Function's Integration, Beijing Normal University Press, Vol.3.
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