本文選題：故障診斷 + 雙饋風(fēng)力發(fā)電機(jī)��； 參考：《南京航空航天大學(xué)》2014年博士論文

【摘要】：風(fēng)電系統(tǒng)使用壽命通常達(dá)20年以上,常年經(jīng)受酷暑嚴(yán)寒和極端溫差的考驗(yàn),受無規(guī)律變向、變負(fù)荷的風(fēng)力作用以及強(qiáng)陣風(fēng)的沖擊等原因使得風(fēng)電系統(tǒng)不可避免地會(huì)發(fā)生故障。因此,及早發(fā)現(xiàn)故障,減少故障所造成的損失是風(fēng)電技術(shù)推廣應(yīng)用的重要研究內(nèi)容,本文對風(fēng)電系統(tǒng)故障診斷技術(shù)進(jìn)行了深入的研究。本文首先介紹了雙饋風(fēng)力發(fā)電系統(tǒng)的工作原理,分別在3種不同的坐標(biāo)系下建立了雙饋風(fēng)力發(fā)電機(jī)的數(shù)學(xué)模型,將雙饋風(fēng)力發(fā)電機(jī)模型采用小擾動(dòng)分析法進(jìn)行了線性化處理,又進(jìn)一步建立了雙饋風(fēng)力發(fā)電機(jī)的空間矢量模型。對雙饋風(fēng)力發(fā)電機(jī)正常運(yùn)行時(shí)的氣隙磁密、定子并聯(lián)支路環(huán)流特性、定子振動(dòng)特性、轉(zhuǎn)子振動(dòng)特性進(jìn)行了分析。在對風(fēng)電系統(tǒng)故障原因、故障特征及故障機(jī)理進(jìn)行分析的基礎(chǔ)上,分別建立了雙饋風(fēng)力發(fā)電機(jī)定子短路故障模型、氣隙偏心故障模型和轉(zhuǎn)子繞組匝間短路故障模型。分析了生物免疫系統(tǒng)的基本原理及其在多條件、多變量情況下的數(shù)學(xué)模型。對人工免疫算法、單克隆免疫策略算法進(jìn)行了理論分析并對其收斂性進(jìn)行了理論證明,給出了免疫單克隆策略、多克隆策略算法的執(zhí)行步驟。描述了風(fēng)電系統(tǒng)故障免疫狀態(tài)空間,建立了基于免疫算法的風(fēng)電系統(tǒng)故障診斷響應(yīng)模型。在此基礎(chǔ)上借鑒生物免疫系統(tǒng)理論與運(yùn)行機(jī)制將人工免疫系統(tǒng)的理論與風(fēng)電系統(tǒng)故障診斷相融合,建立了適合風(fēng)電系統(tǒng)故障診斷的改進(jìn)的人工免疫系統(tǒng)。建立了風(fēng)電系統(tǒng)網(wǎng)側(cè)變流器、機(jī)側(cè)變流器數(shù)學(xué)模型和控制策略,在克隆選擇、神經(jīng)網(wǎng)絡(luò)算法的基礎(chǔ)上,將自適應(yīng)動(dòng)態(tài)克隆選擇算法良好的優(yōu)化性能與BP神經(jīng)網(wǎng)絡(luò)相結(jié)合,提出了一種基于自適應(yīng)動(dòng)態(tài)克隆選擇算法的神經(jīng)網(wǎng)絡(luò)系統(tǒng),將優(yōu)化的神經(jīng)網(wǎng)絡(luò)應(yīng)用于風(fēng)電系統(tǒng)變流器故障診斷。實(shí)驗(yàn)結(jié)果表明,該方法可以避開局部最小值,算法收斂速度快,具有較好的故障診斷性能。發(fā)電機(jī)是風(fēng)電系統(tǒng)的關(guān)鍵組成部分,針對風(fēng)力發(fā)電機(jī)的單一故障,本文對雙饋風(fēng)力發(fā)電機(jī)定子短路故障下氣隙磁密、并聯(lián)支路特性進(jìn)行了分析,基于雙饋風(fēng)力發(fā)電機(jī)電壓、電流和通量建立了風(fēng)力發(fā)電機(jī)定子故障數(shù)學(xué)模型。提出了一種免疫記憶動(dòng)態(tài)克隆策略算法,并將其應(yīng)用于雙饋風(fēng)力發(fā)電機(jī)定子繞組單一故障診斷,將雙饋風(fēng)力發(fā)電機(jī)的4種故障特征量作為免疫記憶動(dòng)態(tài)克隆策略系統(tǒng)的抗原。用雙饋風(fēng)力發(fā)電機(jī)工作狀態(tài)參數(shù)對免疫記憶動(dòng)態(tài)克隆策略系統(tǒng)進(jìn)行訓(xùn)練,將訓(xùn)練階段獲得的記憶數(shù)據(jù)應(yīng)用于發(fā)電機(jī)故障診斷。該方法使用記憶單元作為類別標(biāo)簽,記憶單元根據(jù)種群自適應(yīng)性平均值不斷更新,當(dāng)種群自適應(yīng)度的標(biāo)準(zhǔn)偏差為零時(shí),存儲(chǔ)器單元不改變,可確保算法早期的收斂性。實(shí)驗(yàn)結(jié)果表明所提出的基于免疫記憶動(dòng)態(tài)克隆策略算法風(fēng)電系統(tǒng)故障診斷系統(tǒng)具有比較好的分類效果,對于雙饋風(fēng)力發(fā)電機(jī)定子繞組單一故障診斷是適用和有效的。當(dāng)風(fēng)電系統(tǒng)運(yùn)行時(shí),雙饋風(fēng)力發(fā)電機(jī)定子電流中的故障信號(hào)比較微弱,故障分量的頻率與基頻分量的頻率非常接近,故障分量的幅值也較小,易被泄漏的基頻分量及噪聲淹沒。本文分別對轉(zhuǎn)子故障、偏心故障、偏心與轉(zhuǎn)子復(fù)合故障下的氣隙磁密、定子并聯(lián)支路環(huán)流特性進(jìn)行了理論分析。在此基礎(chǔ)上將小波分析與人工免疫系統(tǒng)相結(jié)合,針對風(fēng)力發(fā)電機(jī)的復(fù)合故障,提出了一種基于小波-抗體記憶克隆算法的雙饋風(fēng)力發(fā)電機(jī)偏心和轉(zhuǎn)子復(fù)合故障診斷方法。該方法首先對雙饋風(fēng)力發(fā)電機(jī)定子電流信號(hào)進(jìn)行小波分析,經(jīng)小波分析計(jì)算出小波系數(shù),由小波系數(shù)計(jì)算出雙饋風(fēng)力發(fā)電機(jī)故障信號(hào)的能量,經(jīng)過歸一化處理形成故障特征量。將故障特征量隱喻為抗原,采用抗體克隆記憶算法生成抗體,經(jīng)過選擇、克隆、變異、抗體再選擇、抗體記憶以及壓縮等操作產(chǎn)生新的抗體,用產(chǎn)生的新抗體對風(fēng)力發(fā)電機(jī)復(fù)合故障進(jìn)行診斷。實(shí)驗(yàn)結(jié)果表明,本文提出的基于小波和抗體記憶克隆算法相結(jié)合的方法取得了比較好的風(fēng)電系統(tǒng)故障診斷效果。針對風(fēng)電系統(tǒng)綜合故障,分別對風(fēng)電系統(tǒng)偏心、定子、轉(zhuǎn)子繞組短路故障以及復(fù)合故障下的振動(dòng)特性進(jìn)行了理論分析,提出了一種基于自適應(yīng)多克隆策略算法的風(fēng)電系統(tǒng)綜合故障診斷方法。該方法將風(fēng)電系統(tǒng)振動(dòng)信號(hào)、電流信號(hào)作為故障的特征量,將其隱喻為免疫故障診斷系統(tǒng)的抗原。系統(tǒng)根據(jù)每個(gè)抗原、抗體種群親合度的大小,自適應(yīng)地調(diào)節(jié)抗體對抗原的適應(yīng)性和抗體種群規(guī)模的大小,將抗原歸并至某一組確定的抗體種群中。多克隆算法引入了交叉重組操作,使抗體的多樣性在進(jìn)化過程中得到了增加,這使得算法在避免陷入局部極小值的能力和局部搜索能力方面都得到了提高,將該方法應(yīng)用于風(fēng)電系統(tǒng)綜合故障診斷取得了比較好的效果�；谏鲜鲅芯�,利用GE智能平臺(tái)的集成開發(fā)環(huán)境和IFIX組態(tài)軟件開發(fā)了基于免疫算法的風(fēng)電系統(tǒng)綜合故障診斷軟件,完成了系統(tǒng)調(diào)試和算法驗(yàn)證工作。用故障診斷系統(tǒng)對風(fēng)電系統(tǒng)綜合故障進(jìn)行診斷,通過實(shí)驗(yàn)比較了采用不同復(fù)合故障信號(hào)診斷方法對風(fēng)電系統(tǒng)故障診斷結(jié)果的影響。實(shí)驗(yàn)結(jié)果表明,利用風(fēng)電系統(tǒng)振動(dòng)、風(fēng)電系統(tǒng)電流復(fù)合信號(hào)對風(fēng)電系統(tǒng)故障進(jìn)行診斷可以取得比較好的診斷效果,系統(tǒng)具有良好的故障診斷能力。最后,本文對研究成果進(jìn)行了全面總結(jié),并對基于人工免疫系統(tǒng)的風(fēng)電系統(tǒng)故障診斷技術(shù)進(jìn)一步研究進(jìn)行了展望。
[Abstract]:The service life of the wind power system is usually over 20 years, and is subjected to the test of severe heat and extreme temperature difference for a long time. The wind power system will inevitably be broken down by the irregular change of direction, the wind force of changing load and the impact of strong gust. Therefore, the early detection of the fault and the loss caused by the failure are the popularization of the wind power technology. In this paper, the fault diagnosis technology of wind power system is deeply studied in this paper. Firstly, the working principle of doubly fed wind power generation system is introduced. The mathematical model of doubly fed wind generator is established in 3 different coordinate systems, and the doubly fed wind generator model is linear with small disturbance analysis. The space vector model of the doubly fed wind generator is further established. The air gap magnetic density of the doubly fed wind generator in normal operation, the circulation characteristic of the stator parallel branch, the stator vibration characteristics and the rotor vibration characteristics are analyzed. On the basis of the analysis of the fault causes, the fault characteristics and the fault mechanism of the wind power system, the wind power generator is divided into two parts. The stator short fault model of the doubly fed wind generator, the air gap eccentricity fault model and the rotor winding short circuit fault model are not established. The basic principle of the biological immune system and the mathematical model under the condition of multi condition and multivariable are analyzed. The artificial immune algorithm and the monoclonal immunization strategy algorithm are analyzed and collected. The convergence is proved by the theory, the immune monoclonal strategy and the implementation step of the multi clone strategy algorithm are given. The fault immune state space of the wind power system is described, and the fault diagnosis response model of the wind power system based on the immune algorithm is established. On the basis of this, the theory and operation mechanism of the biological immune system are used for the theory of artificial immune system. Combined with fault diagnosis of wind power system, an improved artificial immune system suitable for fault diagnosis of wind power system is established. The wind power system network side converter, the mathematical model and control strategy of the side converter are set up. On the basis of the clonal selection and neural network algorithm, the optimal performance of the adaptive dynamic clonal selection algorithm and the BP God are good. Through the combination of network, a neural network system based on adaptive dynamic clonal selection algorithm is proposed. The optimized neural network is applied to the fault diagnosis of wind power system converter. The experimental results show that the method can avoid the local minimum value, the algorithm converges fast and has good fault diagnosis performance. The generator is a wind power system. In view of the single fault of the wind generator, this paper analyzes the air gap magnetic density and the parallel branch characteristics of the doubly fed wind generator stator short circuit fault. Based on the voltage, current and flux of the doubly fed wind generator, the mathematical model of the stator fault of the wind generator is established. A dynamic clonal strategy for immune memory is proposed. The algorithm is applied to the single fault diagnosis of the stator winding of a doubly fed wind generator, and the 4 fault characteristics of the doubly fed wind generator are used as the antigen of the dynamic cloning strategy system of the immune memory. The training phase of the immune memory dynamic clone system is trained with the working state parameters of the doubly fed wind generator, and the record of the training phase is obtained. Memory unit is used as class label, memory unit is constantly updated according to the adaptive average of the population. When the standard deviation of population adaptive degree is zero, the memory unit is not changed, and the convergence of the algorithm can be ensured. The experimental results show that the proposed immune memory is based on the immune memory. The dynamic cloning strategy algorithm has a good classification effect in wind power system fault diagnosis system. It is applicable and effective for the single fault diagnosis of the stator winding of the doubly fed wind generator. When the wind power system runs, the fault signals in the stator current of the doubly fed wind generator are relatively weak, the frequency of the fault components and the frequency of the fundamental frequency components. It is very close, the amplitude of the fault component is also small, and it is easy to be flooded with the fundamental frequency component and noise of the leakage. This paper analyses the rotor fault, eccentric fault, the air gap magnetic density under the compound fault of the eccentric and the rotor, and analyses the circulation characteristics of the stator parallel branch. On this basis, the wavelet analysis is combined with the artificial immune system, and the wind power is combined with the wind force. A hybrid fault diagnosis method of doubly fed wind generator is proposed based on the wavelet antibody memory cloning algorithm. The method of wavelet analysis is used to analyze the stator current signal of doubly fed wind generator, and the wavelet coefficients are calculated by wavelet analysis. The doubly fed wind generator is calculated by the wavelet coefficients. The energy of the fault signal is processed by normalization. The fault feature is metaphorical as an antigen, and the antibody cloned memory algorithm is used to generate antibodies. After selection, cloning, mutation, antibody selection, antibody memory and compression, new antibodies are produced, and a new antibody produced by the production of a new antibody is used to diagnose the complex fault of wind turbine generator. The experimental results show that a better fault diagnosis effect is obtained by combining the wavelet and the antibody memory algorithm, and the vibration characteristics of the wind power system eccentricity, the stator, the rotor winding short circuit and the complex fault are analyzed. A comprehensive fault diagnosis method for wind power system based on adaptive multi clone strategy algorithm is proposed. This method uses the vibration signal and current signal of the wind power system as the characteristic quantity of the fault, and metaphores it as the antigen of the immune fault diagnosis system. The original adaptability and the size of the antibody population size the antigen into a certain group of antibody populations. The polyclonal algorithm introduces the cross recombination operation to increase the diversity of the antibody in the evolutionary process, which makes the algorithm improve the ability to avoid the local minimum and the local search ability. This method has been applied to the comprehensive fault diagnosis of wind power system. Based on the above research, the integrated development environment of GE intelligent platform and the IFIX configuration software are used to develop the integrated fault diagnosis software of wind power system based on immune algorithm, and the system debugging and calculation verification are completed. The comprehensive fault diagnosis is carried out, and the effects of different fault signal diagnosis methods on the fault diagnosis results of wind power system are compared through experiments. The experimental results show that the wind power system vibration can be used to diagnose the fault of the wind power system, and the system has good results. In the end, this paper makes a comprehensive summary of the research results, and looks forward to the further research on the fault diagnosis technology based on the artificial immune system.

【學(xué)位授予單位】：南京航空航天大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2014
【分類號(hào)】：TM614

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本文編號(hào)：1809769