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Application of Fuzzy Neural Networks (FNNs) to Estimate Boiler Drum Water Level in A Thermal Power Plant
|關鍵字:||Fuzzy Neural Network;模糊類神經網路||出版社:||電機工程學系所||引用:||許金和“火力發電大全”高雄復文圖書出版社pp.5-1~9-55 ,May. 2002  A. J. Gaikwad, P. K. Vijayan, S. Bhartiya, R. Kumar, H. G. Lele, and K. K. Vaze, “Selection of steam drum level control method for multiple drum interacting loops pressure tube-type BWR,” IEEE Transactions on Nuclear Science, vol.58, no.2, pp. 479-488, April 2011.  Y. Zhang and H. Li “A self-adjusting fuzzy control for the drum water level,” IEEE International Conference on Information and Automation (ICIA), pp. 2087-2091, June 2010.  M. Wu, M. Wu, J. Huang, and L. Shao Ling, “Intelligent control system of water level for boiler drum based on OPC and MATLAB,” 30th Chinese Control Conference (CCC), pp. 4461-4464, July 2011.  C. F. Juang and C. T. Lin, “An on-line self-constructing neural fuzzy inference network and its applications,” IEEE Trans. Fuzzy Systems, vol. 6. no. 1, pp. 12-32, Feb. 1998.  J. S. Jang, “ANFIS: Adaptive-network-based fuzzy inference system,” IEEE Trans. Syst., Man, Cybern., vol. 23, no. 3, pp. 665-685, May 1993.  C. J. Lin and C. T. Lin, “An ART-based fuzzy adaptive learning control network,” IEEE Trans. Fuzzy Systems, vol. 5, no. 4, pp. 477-496, Nov. 1997.  D. Kukolj and E. Levi, “Identification of complex systems based on neural and Takagi-Sugeno fuzzy model,” IEEE Trans. Sys., Man, Cybern., Part B: Cybernetics, vol. 34, no. 1, pp. 272-282, 2004.  C. S. Ouyang; W. J. Lee and S. J. Lee, “A TSK-type neurofuzzy network approach to system modeling problems,” IEEE Trans. Sys., Man, Cybern., Part B: Cybernetics, vol. 35, no. 4, pp. 751- 767, 2005.  N. N. Karnik , J. M. Mendel, and Q. Liang, “Type-2 fuzzy logic systems,” IEEE Trans. on Fuzzy Systems, vol. 7, no. 6, pp. 643-658, 1999.  J. M. Mendel and R. I. John, “Type-2 fuzzy sets made simple,” IEEE Trans. On Fuzzy Systems, vol. 10, no. 2, pp. 117-127, 2002.  J. M. Mendel, Uncertain Rule-Based Fuzzy Logic System: Introduction and New Directions, Prentice Hall, Upper Saddle River, NJ2001.  Q. Liang and J. M. Mendel, “Equalization of nonlinear time-varying channels using type-2 fuzzy adaptive filters,” IEEE Trans. Fuzzy systems, vol. 8, no. 551-563, 2000.  H. B. Mitchell, “Pattern recognition using type-2 fuzzy sets,” Information Sciences, vol. 170, pp. 409-418, 2005.  P. Melin and O. Castillo, “Intelligent control of non-linear dynamic plants using type-2 fuzzy logic and neural networks,” Proc. IEEE Int. Conf. Fuzzy Systems, Budapest, Hungary, July, 2004.  H. Hagras, “A hierarchical type-2 fuzzy logic control architecture for autonomous mobile robots,” IEEE Trans. Fuzzy Systems, vol. 12, no. 524-539, 2004.  M. Melgarejo and C. Pena-Reyes, “Hardware architecture and FPGA implementation of a type-2 fuzzy system,” Proc. Of Great Lakes Symposium on VLSI (GLSVLSI), Boston, USA, pp. 458-261, 2004.  R. I. John, P. R. Innocent, and M. R. Barnes, “Neuro-fuzzy clustering of radiographic tibia image data using type-2 fuzzy sets,” Information Sciences, vol. 125, pp. 203-220, 2000.  A. G. Luigi Di Lascio and A. Nappi, “Medical differential diagnosis through type-2 fuzzy sets,” Proc. Of IEEE Int. Conf. Fuzzy Systems, pp. 371-376, 2005.  C. F. Juang and Y. W. Tsao, “A self-evolving interval type-2 fuzzy neural network with on-line structure and parameter learning,” IEEE Trans. Fuzzy Systems, vol. 16, no. 6, pp. 1411-1424, Dec. 2008.  Q. Liang and J. M. Mendel, “Interval type-2 fuzzy logic systems: theory and design,” IEEE Trans. On Fuzzy Systems, vol. 8, no. 5, pp. 535-550, 2000.  J. M. Mendel, “Computing derivatives in interval type-2 fuzzy logic system,” IEEE Trans. On Fuzzy Systems, vol. 12. no. 1, pp. 84-98, Feb. 2004.||摘要:||
本論文研究提出第一型模糊類神經網路(SONFIN)及第二型模糊類神經網路(SEIT2FNN)對台中火力電廠燃煤機組鍋爐之汽鼓水位估測之比較。第一型模糊類神經網路(SONFIN)的每一條模糊規則的前件部使用第一型的模糊集合，而後件部使用Takagi-Sugeno-Kang (TSK)型模糊規則及實數係數。第二型模糊類神經網路(SEIT2FNN)中的每一條模糊規則的前件部使用第二類型模糊集合，而後件部使用TSK模糊規則形及區間系數。兩種類型之模糊類神經網路前件與後件部參數均使用梯度下降法(Gradient Descent Algorithm)對參數學習。網路的輸入值為過剩空氣、蒸汽量、調壓閥開度、鍋爐飼水流量、鍋爐飼煤量、第一段過熱器噴水、第二段過熱器噴水、IR吹灰器、IK吹灰器，輸出值為T秒後的汽鼓水位估測值。論文利用台中火力發電廠之中六機收集的三組資料來驗證估測效果。分別模擬取樣時間T為4秒與8秒的正常情形與當取樣時間為4秒時機組發生異常時情形。結果顯示第二型模糊類神經網路(SEIT2FNN)相較於第一型模糊類神經網路(SONFIN)有較佳之估測效果。
This thesis applies a type-1 fuzzy neural network (FNN), the self-constructing neural fuzzy inference network (SONFIN), and a type-2 FNN, the self-evolving interval type 2 fuzzy neural network (SEIT2FNN), to estimate the boiler drum water level of coal fuel unit in Taichung Thermal Power Plant. The antecedent part of SONFIN uses a type-1 fuzzy set and the consequent part is of Takagi-Sugeno-Kang (TSK) type with crisp combination coefficients. In contrast, the antecedent part of SEIT2FNN uses an interval type-2 fuzzy set and the consequent is of TSK type with interval combination coefficients. Both SONFIN and SEIT2FNN use a gradient descent algorithm for antecedent and consequent parameter learning. The inputs of a network are the amounts of excessive O2, steam flow, governor valve, feed water flow, coal flow, water sprays on the first and second stages, IR sootblower, and IK sootblower, and the output is the estimated boiler drum water level after T seconds. The data were collected from six units of Taichung Thermal Power Plant. Three cases were simulated, including two normal conditions with sampling periods of 4 seconds and 8 seconds and an abnormal condition with sampling period of 4 seconds. Estimation results show that the SEIT2FNN outperforms the SONFIN.
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