本文選題：風(fēng)電機(jī)組　切入點：狀態(tài)評估　出處：《華僑大學(xué)》2014年碩士論文

【摘要】：隨著全球風(fēng)電產(chǎn)業(yè)的迅速發(fā)展，風(fēng)電機(jī)組裝機(jī)容量大幅增加，結(jié)構(gòu)日趨復(fù)雜，運行環(huán)境復(fù)雜多變，使得機(jī)組的運行與維護(hù)面臨著前所未有的挑戰(zhàn)。實時評估機(jī)組狀態(tài)，對異常運行狀態(tài)及時識別并進(jìn)行預(yù)警，，可以保障機(jī)組安全高效地運行，對優(yōu)化檢修策略具有重大意義。在充分利用SCADA系統(tǒng)提供的大量監(jiān)測數(shù)據(jù)，且不增加額外傳感器等其他監(jiān)測設(shè)備的情況下，綜合考慮監(jiān)測項目之間的關(guān)系，建立了風(fēng)電機(jī)組實時狀態(tài)評估模型及齒輪箱異常預(yù)警模型。 利用粗糙集約簡方法選擇模型的特征參數(shù)，即在平凡約簡的基礎(chǔ)上，利用屬性重要度為啟發(fā)信息再進(jìn)行二次約簡，并結(jié)合自然算法、半自然算法及等頻率法的離散化方法和遺傳算法、約翰遜算法的約簡算法的約簡結(jié)果提取出模型的特征參數(shù)。以有功功率為決策屬性，SCADA系統(tǒng)其他監(jiān)測量為條件屬性，約簡得到機(jī)組實時狀態(tài)評估模型的特征參數(shù)；分別以齒輪箱輸入軸溫度、齒輪箱輸出軸溫度以及齒輪箱油溫等三個齒輪箱監(jiān)測項目為決策屬性，SCADA系統(tǒng)中其他監(jiān)測量為條件屬性，約簡后得到齒輪箱異常預(yù)警模型的特征參數(shù)。 建立機(jī)組實時狀態(tài)評估模型，結(jié)合SCADA越限報警系統(tǒng)，與有功功率預(yù)測模型得到的預(yù)測值與實際值的差值來實時評估機(jī)組狀態(tài)。經(jīng)實際監(jiān)測數(shù)據(jù)驗證，基于粗糙集與TS模糊神經(jīng)網(wǎng)絡(luò)建立的預(yù)測模型優(yōu)于BP神經(jīng)網(wǎng)絡(luò)和SVM模型的預(yù)測精度，并可以準(zhǔn)確識別機(jī)組處于異常運行狀態(tài)。齒輪箱異常預(yù)警模型基于齒輪箱輸入軸溫度、輸出軸溫度以及齒輪箱油溫等三個溫度變量的預(yù)測模型構(gòu)建。與以往研究齒輪箱溫度量變化趨勢預(yù)測僅從該溫度量前一段時間的若干個時間序列值入手分析不同，本文利用粗糙集屬性約簡算法引入了對齒輪箱溫度量有影響的其他監(jiān)測量，同時也加入其上一時刻的溫度值作為特征參數(shù)，建立TS模糊神經(jīng)網(wǎng)絡(luò)預(yù)測模型，以輸出溫度量預(yù)測值與實際值的差值是否越限來實現(xiàn)齒輪箱異常預(yù)警目的。 本文提出的機(jī)組實時狀態(tài)評估方法和齒輪箱異常預(yù)警方法均是基于風(fēng)電機(jī)組SCADA系統(tǒng)的真實的監(jiān)測數(shù)據(jù)，可操作性強(qiáng)，且具有良好的推廣性。
[Abstract]:With the rapid development of the global wind power industry, the installed capacity of wind turbine units is increasing dramatically, the structure is becoming more and more complex, and the operating environment is complex and changeable, which makes the operation and maintenance of wind power units face unprecedented challenges.Evaluating the status of the unit in real time, identifying and warning the abnormal operating state in time can ensure the safe and efficient operation of the unit, and it is of great significance to optimize the maintenance strategy.Taking full advantage of the large amount of monitoring data provided by the SCADA system and without adding additional sensors and other monitoring equipment, taking into account the relationship between the monitoring items,The real-time evaluation model of wind turbine and the gearbox anomaly warning model are established.The rough set reduction method is used to select the characteristic parameters of the model, that is, on the basis of trivial reduction, using attribute importance as the heuristic information, the quadratic reduction is carried out, and the natural algorithm is combined.The discretization method and genetic algorithm of semi-natural algorithm and equal frequency method, and the reduction result of Johnson algorithm are used to extract the characteristic parameters of the model.Taking active power as decision attribute and other monitoring parameters of SCADA system as conditional attributes, the characteristic parameters of the real-time state evaluation model of the unit are obtained, and the temperature of the shaft is input to the gearbox, respectively.Three gearbox monitoring items, such as gearbox output shaft temperature and gearbox oil temperature, are decision attributes and other monitoring quantities in SCADA system are conditional attributes. After reduction, the characteristic parameters of gearbox anomaly warning model are obtained.The real-time state evaluation model of the unit is established, and the difference between the predicted value and the actual value obtained from the prediction model of active power and the difference between the predicted value and the actual value are combined with the SCADA over-limit alarm system to evaluate the unit state in real time.The prediction model based on rough set and TS fuzzy neural network is better than that of BP neural network and SVM model, and it can accurately identify the unit in abnormal operation state.The gearbox anomaly warning model is constructed based on three temperature variables, namely, the temperature of the gearbox input shaft, the temperature of the output shaft and the oil temperature of the gearbox.Different from the previous research on the prediction of gearbox temperature change trend only from several time series values of the previous time series of the gearbox temperature quantity, this paper introduces other monitoring quantities which have influence on the gearbox temperature quantity by using the rough set attribute reduction algorithm.At the same time, the temperature value at the last moment is added as the characteristic parameter, and the TS fuzzy neural network prediction model is established to realize the goal of gearbox anomaly warning by whether the difference between the predicted value of output temperature quantity and the actual value exceeds the limit.Both the real-time status evaluation method and the gearbox anomaly warning method proposed in this paper are based on the real monitoring data of wind turbine SCADA system. They are easy to operate and have good generalization.
【學(xué)位授予單位】：華僑大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TM315

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