Rough Sets Based Simplified Analysis of Energy Loss Indicators in Power Plant

Abstract:

There are many kinds of energy loss indicators in power plant, and there are some relevance among the various indicators. So extraction of the key indicators plays an important role between in energy loss analysis of power plants and optimal management of thermal power plants. Based on the characteristics of these indicators, the idea of rough sets was applied to the energy loss analysis of thermal power plants, then we proposed a new algorithm -- use fuzzy C means algorithm (FCM) to discrete cluster the energy loss indicators of thermal power plant, and then analysis simplified the results with algorithm Johnson. Real experiments (Chaozhou 1,2 and Ningde 3,4 assembling units which of the same type in the SIS system under the THA working condition)’ results had proved high accuracy and valuable of the algorithm.

• Periodical: Advanced Materials Research (Volumes 383-390)
• Pages: 4130-4133
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.383-390.4130
• Online since: November 2011
• Authors: Song Feng Tian, Wei Wang, Yun Feng Tian, Shuang Bai Liu
• Keywords: Attribute Reduction, Energy Loss Indicators, Rough Set (RS)

References

[1] Pawlak Z. Rough Sets: The Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishers: (1991).

[2] Pawlak Z. Rough sets and intelligence data analysis. Information Sciences, 2002, 147(1): 1-12.

[3] PAWLAK, Z．Rough Sets: International Journal of Computer and Information Sciences, 1982, 11: 341－356.

[4] NIKHIL R P, KUHU P, JAMES M K, et al. A possibilistic fuzzy c-means clustering algorithm[J]. IEEE Transaction on Fuzzy Systems, 2005, 13(4): 517-530.

DOI: 10.1109/tfuzz.2004.840099

[5] JACEK M L. Generalized weighted conditional fuzzy clustering[J]. IEEE Transactions on Fuzzy Systems, 2003, 11(6)：709-715.

DOI: 10.1109/tfuzz.2003.819844

[6] Beadk J C. Pattern recognition with fuzzy objective function algorithms[M]. New York: Plenum Press, 1981: 34-42.

[7] Austin E. Exploratory and confirmatory factor analysis: Understanding concepts and applications. British Journal of Mathematical & Statistical Psychology, 2006, 59 (1): 218-219. Table 1. The Rules Simpilified By Rough Sets Num. Reduction rule Number of rules.

[1] C3(2) AND C5(2) AND C6(3) AND C8(1) AND C9(3) => D(1).

[1] [2] C3(2) AND C5(2) AND C6(2) AND C8(2) AND C9(3) => D(1).

[2] [3] C3(1) AND C5(2) AND C6(1) AND C8(1) AND C9(2) => D(1).

[4] [4] C3(3) AND C5(1) AND C6(3) AND C8(2) AND C9(3) => D(1).

[1] [5] C3(1) AND C5(3) AND C6(2) AND C8(1) AND C9(2) => D(2).

[2] [6] C3(2) AND C5(3) AND C6(2) AND C8(1) AND C9(2) => D(2).

[4] [7] C3(1) AND C5(3) AND C6(3) AND C8(2) AND C9(2) => D(2).

[2] [8] C3(3) AND C5(1) AND C6(3) AND C8(3) AND C9(2) => D(2).

[1] [9] C3(2) AND C5(1) AND C6(2) AND C8(3) AND C9(2) => D(2).

[4] [10] C3(1) AND C5(2) AND C6(1) AND C8(2) AND C9(2) => D(2).

[2] [11] C3(1) AND C5(1) AND C6(3) AND C8(3) AND C9(3) => D(2).

[1] [12] C3(3) AND C5(1) AND C6(2) AND C8(3) AND C9(3) => D(1).

[1] [13] C3(1) AND C5(2) AND C6(3) AND C8(3) AND C9(3) => D(2).

[2] [14] C3(3) AND C5(1) AND C6(2) AND C8(2) AND C9(1) => D(1).

[1] [15] C3(3) AND C5(2) AND C6(2) AND C8(3) AND C9(1) => D(1).

[1] [16] C3(2) AND C5(1) AND C6(2) AND C8(2) AND C9(1) => D(2).

[6] [17] C3(3) AND C5(1) AND C6(2) AND C8(3) AND C9(1) => D(2).

[1] [18] C3(2) AND C5(2) AND C6(1) AND C8(3) AND C9(1) => D(2).

[3] [19] C3(3) AND C5(2) AND C6(1) AND C8(3) AND C9(1) => D(2).

[1] Table 2. The Relationship of Each Simplified Attribute Condition attribute C3 C5 C6 Corresponding condition attribute C5 C6 C8 C9 C3 C6 C8 C9 C3 C5 C8 C9 Correlation 0. 555 0. 745 0. 223 0. 29 0. 555 0. 003 0. 482 0. 222 0. 745 0. 003 0. 042 0. 118 Condition attribute C8 C9 - Corresponding condition attribute C3 C5 C6 C9 C3 C5 C6 F8 - - - - Correlation 0. 223 0. 482 0. 042 0. 11 0. 29 0. 222 0. 118 0. 11.

DOI: 10.7717/peerj.7041/fig-5