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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|
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|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.
|Appears in Collections:||電機工程學系所|
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