本文選題：變壓器 + 局部放電��； 參考：《重慶大學(xué)》2014年博士論文

【摘要】：電力變壓器是電力系統(tǒng)重要的樞紐設(shè)備，其安全穩(wěn)定運行對于電力系統(tǒng)尤為關(guān)鍵。電力變壓器的運行可靠性很大程度上取決于其絕緣的可靠性。局部放電與變壓器內(nèi)部絕緣缺陷具有緊密聯(lián)系，通過局部放電在線監(jiān)測能夠及時判斷變壓器內(nèi)部絕緣狀態(tài)，對防止電力變壓器事故發(fā)生，保障電力系統(tǒng)安全穩(wěn)定運行具有重要意義。本文針對傳感器技術(shù)、抗干擾、模式識別等變壓器局部放電在線監(jiān)測與故障診斷的三個主要問題，總結(jié)分析了變壓器局部放電在線監(jiān)測研究現(xiàn)狀，對局部放電監(jiān)測的分形天線優(yōu)化、自適應(yīng)去噪與識別等問題進(jìn)行了深入的研究。論文主要包括以下內(nèi)容： ①根據(jù)希爾伯特分形天線的設(shè)計原理，提出變壓器局部放電超高頻監(jiān)測傳感器設(shè)計準(zhǔn)則。通過仿真分析，研究了導(dǎo)體寬度、導(dǎo)體厚度、介質(zhì)厚度、饋電點位置對分形天線的駐波比、增益和方向性的影響規(guī)律；提出了結(jié)合遺傳算法和仿真計算的分形天線優(yōu)化方法，以檢測頻帶內(nèi)駐波比不大于2為設(shè)計目標(biāo)，實現(xiàn)了四階希爾伯特分形天線的設(shè)計；通過對人工油紙絕緣缺陷模型的局部放電檢測，證明了優(yōu)化后的分形天線能夠滿足變壓器局部放電超高頻檢測的要求。 ②研究了混沌振子濾波器的原理與設(shè)計方法，提出了抑制窄帶周期干擾的自適應(yīng)混沌振子濾波器。提出了基于Lyapunov指數(shù)法判別混沌振子的運動狀態(tài)，設(shè)計并實現(xiàn)了消除局部放電監(jiān)測窄帶周期性干擾的自適應(yīng)混沌振子濾波器；通過局部放電信號去噪實例，對比分析了該方法與二階級聯(lián)IIR格型陷波器的去噪結(jié)果。結(jié)果表明，，自適應(yīng)混沌振子濾波器能夠從窄帶周期干擾中有效提取局部放電信號，去噪信號畸變率與幅值誤差顯著低于陷波器去噪結(jié)果。 ③基于經(jīng)驗?zāi)Ｊ椒纸饧靶〔ㄩ撝捣ㄔ恚岢隽司植糠烹娦盘柟逃心B(tài)自適應(yīng)最優(yōu)小波去噪方法。該方法首先對局部放電信號進(jìn)行經(jīng)驗?zāi)Ｊ椒纸獾玫蕉鄠�固有模態(tài)，對每個固有模態(tài)采取自適應(yīng)最優(yōu)小波去噪并相加重構(gòu)得到去噪后信號。固有模態(tài)小波分解時基于尺度系數(shù)能量最大原則自適應(yīng)選擇最優(yōu)小波。通過對染噪局部放電信號的去噪試驗，證明了固有模態(tài)自適應(yīng)最優(yōu)小波去噪對染噪局部放電信號造成的畸變更小。 ④根據(jù)局部放電超高頻監(jiān)測中的信號識別問題，提出了局部放電超高頻信號固有模態(tài)特征提取及識別方法。首先，設(shè)計了變壓器典型絕緣缺陷，通過試驗獲得大量局部放電超高頻樣本數(shù)據(jù)。其次，對局部放電超高頻信號進(jìn)行經(jīng)驗?zāi)B(tài)分析，提取固有模態(tài)的分形維數(shù)和能量系數(shù)作為特征量。最后，采取可能性模糊C-均值算法和反向傳播神經(jīng)網(wǎng)絡(luò)進(jìn)行分類識別，結(jié)果表明：反向傳播神經(jīng)網(wǎng)絡(luò)識別率更高；固有模態(tài)提取的分形特征識別正確率高于小波系數(shù)。 通過上述研究工作，本文實現(xiàn)了局部放電監(jiān)測分形天線的寬頻帶優(yōu)化設(shè)計，進(jìn)一步降低了局部放電信號自適應(yīng)去噪畸變率，顯著提升了局部放電超高頻信號多尺度特征參數(shù)識別正確率，解決了局部放電在線監(jiān)測系統(tǒng)的抗干擾性和檢測靈敏度難題，具有很強的實用價值和應(yīng)用前景。
[Abstract]:The power transformer is an important hub equipment of power system . Its safe and stable operation is especially key to the power system . The reliability of the power transformer depends greatly on the reliability of the insulation . The partial discharge is closely related to the internal insulation defects of the transformer . In this paper , the on - line monitoring and fault diagnosis of transformer ' s internal insulation can be determined in time by means of local discharge online monitoring .

On the basis of the design principle of Hilbert fractal antenna , the design criterion of UHF monitoring sensor for partial discharge of transformer is proposed . Through simulation analysis , the influence of conductor width , conductor thickness , dielectric thickness and feed point position on the standing wave ratio , gain and directivity of fractal antenna are studied .
In this paper , a fractal antenna optimization method combining genetic algorithm and simulation is proposed in order to detect that the standing wave ratio in the frequency band is not greater than 2 as the design target , and the design of the fourth - order Hilbert fractal antenna is realized ;
This paper proves that the optimized fractal antenna can meet the requirement of partial discharge ultra - high frequency detection of transformer by detecting the partial discharge of the model of the insulation defect of the artificial oil paper .

In this paper , the principle and design method of chaotic oscillator filter are studied , and an adaptive chaotic oscillator filter for suppressing narrow - band periodic interference is proposed . A self - adaptive chaotic oscillator filter for eliminating local discharge monitoring narrow - band periodic interference is designed and realized based on Lyapunov exponent method .
The de - noising results of this method and the second - class IIR lattice trap are compared and analyzed . The results show that the adaptive chaotic oscillator filter can effectively extract the local discharge signal from the narrow - band periodic interference , and the de - noising signal distortion ratio and amplitude error are significantly lower than that of the notch filter .

( 3 ) Based on the principle of empirical mode decomposition and wavelet threshold method , an adaptive optimal wavelet de - noising method for local discharge signal is proposed . The method comprises the following steps : firstly , performing empirical mode decomposition on local discharge signals to obtain a plurality of intrinsic modes ;

The characteristic feature extraction and identification method of local discharge ultra - high frequency signal are proposed according to the signal identification problem in local discharge ultra - high frequency monitoring . Firstly , the typical insulation defect of transformer is designed , and a lot of local discharge ultra - high frequency sample data is obtained by experiment . Secondly , the partial discharge ultra - high frequency signal is subjected to empirical mode analysis to extract the fractal dimension and energy coefficient of the natural mode as the feature quantity . Finally , the possibility fuzzy C - means algorithm and the reverse propagation neural network are adopted to classify and identify the characteristic . The results show that the recognition rate of the reverse propagation neural network is higher ;
The recognition accuracy of fractal feature extraction is higher than that of wavelet coefficient .

Through the research work , the broadband optimization design of partial discharge monitoring fractal antenna is realized , the self - adaptive de - noising rate of local discharge electric signal is further reduced , the recognition accuracy of local discharge ultra - high frequency signal multi - scale characteristic parameter is obviously improved , the anti - interference performance and the detection sensitivity problem of the local discharge on - line monitoring system are solved , and the local discharge monitoring fractal antenna has strong practical value and application prospect .
【學(xué)位授予單位】：重慶大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2014
【分類號】：TM855

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