本文關(guān)鍵詞：風(fēng)電機(jī)組運行工況辨識與變槳系統(tǒng)故障診斷　出處：《沈陽工業(yè)大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 風(fēng)電機(jī)組 電動變槳系統(tǒng) 工況辨識 故障診斷 特征參數(shù)篩選

【摘要】：隨著風(fēng)力發(fā)電技術(shù)的高速發(fā)展,風(fēng)電機(jī)組的單機(jī)容量越來越大。然而,隨著風(fēng)電場的大規(guī)模建設(shè),風(fēng)力發(fā)電機(jī)組的運行維護(hù)費用高和故障率高等問題也凸顯了出來,如何提高風(fēng)電機(jī)組運行可靠性及利用率成為風(fēng)力發(fā)電急需解決的問題。變槳系統(tǒng)是風(fēng)電機(jī)組的重要組成部分,但由于其運行環(huán)境惡劣、組件繁多、啟停頻繁,導(dǎo)致故障頻發(fā),本文在分析風(fēng)電機(jī)組及其變槳系統(tǒng)工作原理的基礎(chǔ)上,利用風(fēng)電機(jī)組歷史運行數(shù)據(jù)信息及變槳系統(tǒng)故障信息,研究基于SCADA數(shù)據(jù)的風(fēng)電機(jī)組運行工況辨識和變槳系統(tǒng)運行狀態(tài)故障診斷方法,其研究內(nèi)容主要包括:1)簡述直驅(qū)風(fēng)電機(jī)組及電動變槳系統(tǒng)以及SCADA系統(tǒng)的構(gòu)成和工作原理,針對風(fēng)電機(jī)組運行參數(shù)進(jìn)行分析,并對SCADA監(jiān)控系統(tǒng)所采集的歷史數(shù)據(jù)進(jìn)行數(shù)據(jù)預(yù)處理。采用基于信息熵的特征參數(shù)相關(guān)性分析對變槳系統(tǒng)運行參數(shù)進(jìn)行分析。2)將風(fēng)電機(jī)組運行參數(shù)能量控制模式和限功率標(biāo)志作為分類特征參數(shù),提出了基于自組織神經(jīng)網(wǎng)絡(luò)的混合屬性聚類方法進(jìn)行風(fēng)電機(jī)組運行工況劃分,該算法以自組織特征映射神經(jīng)網(wǎng)絡(luò)為框架,采用基于樣本概率的異構(gòu)值差度量風(fēng)電機(jī)組運行參數(shù)混合屬性數(shù)據(jù)的相異性。利用分類特征項在Voronoi集合中出現(xiàn)頻率作為分類屬性數(shù)據(jù)參考向量更新規(guī)則的基礎(chǔ),通過混合更新規(guī)則實現(xiàn)數(shù)值屬性和分類屬性數(shù)據(jù)規(guī)則的更新。在此基礎(chǔ)上,在自組織神經(jīng)網(wǎng)絡(luò)的競爭層后增加一層輸出層,使其變?yōu)橛斜O(jiān)督的分類學(xué)習(xí)網(wǎng)絡(luò),提出有監(jiān)督的混合屬性數(shù)據(jù)自組織映射分類模型,實現(xiàn)風(fēng)電機(jī)組運行的工況辨識。3)針對風(fēng)電機(jī)組電動變槳系統(tǒng)故障診斷問題,對風(fēng)電變槳系統(tǒng)進(jìn)行特征屬性篩選,建立在不同工況下的風(fēng)電變槳系統(tǒng)異常識別模型,該模型以主元分析為基礎(chǔ),將電動變槳系統(tǒng)運行數(shù)據(jù)投影到主元模型的主元子空間和殘差子空間上,通過判斷其對應(yīng)的T2和SPE是否超出對應(yīng)的控制限來進(jìn)行異常識別;同時根據(jù)貢獻(xiàn)圖法找出異常的特征屬性;最后通過故障子空間理論進(jìn)行基于SPE的故障重構(gòu),實現(xiàn)風(fēng)電變槳系統(tǒng)故障診斷。
[Abstract]:With the rapid development of wind power generation technology, the single unit capacity of wind turbine is increasing. However, with the large-scale construction of wind farm. The problems of high operating and maintenance cost and high failure rate of wind turbine are also highlighted. How to improve the operational reliability and utilization ratio of wind turbine has become an urgent problem for wind power generation. Variable propeller system is an important part of wind turbine, but because of its poor operating environment, various components, frequent start and stop. On the basis of analyzing the working principle of wind turbine and its variable propeller system, this paper makes use of the historical operation data of wind turbine and the fault information of variable propeller system. The method of wind turbine operating condition identification and fault diagnosis of variable propeller system based on SCADA data is studied. The main research contents include: (1) introduce the composition and working principle of direct-drive wind turbine, electric variable propeller system and SCADA system, and analyze the operating parameters of wind turbine unit. The historical data collected by the SCADA monitoring system are preprocessed and the operational parameters of the propeller system are analyzed by the correlation analysis of characteristic parameters based on information entropy. The energy control mode and the limited power mark of the wind turbine operating parameters are taken as the classification characteristic parameters. A hybrid attribute clustering method based on self-organizing neural network is proposed to partition the operating conditions of wind turbine. The algorithm is based on self-organizing feature mapping neural network. The heterogeneity value difference based on sample probability is used to measure the heterogeneity of the mixed attribute data of wind turbine operating parameters. The frequency of classification feature in Voronoi set is used to update the reference vector of classification attribute data. The basis of the rules. The data rules of numerical attributes and classification attributes are updated by mixed updating rules. On this basis, a layer of output layer is added after the competition layer of self-organized neural networks to make it a supervised classification learning network. A supervised self-organizing mapping classification model of hybrid attribute data is proposed to realize the operating condition identification of wind turbine. 3) to solve the problem of fault diagnosis of electric variable propeller system of wind turbine. The characteristic attributes of wind turbine variable propeller system are screened, and the abnormal identification model of wind power variable propeller system under different working conditions is established. The model is based on principal component analysis. The operation data of the electric propeller system are projected onto the principal subspace and residual subspace of the principal component model, and the abnormal identification is carried out by judging whether the corresponding T2 and SPE exceed the corresponding control limit. At the same time, according to the contribution diagram method to find out the characteristic attributes of the anomaly; Finally, fault reconstruction based on SPE is carried out by fault subspace theory, and fault diagnosis of wind power variable propeller system is realized.
【學(xué)位授予單位】：沈陽工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TM315

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