【摘要】：變壓器是電力系統(tǒng)的核心設(shè)備之一,其運(yùn)行狀況直接影響電力系統(tǒng)的安全穩(wěn)定運(yùn)行。繞組變形是變壓器的主要故障類型之一,嚴(yán)重威脅電力系統(tǒng)正常運(yùn)行。為準(zhǔn)確診斷變壓器繞組變形,減少變壓器故障,在總結(jié)分析變壓器繞組變形檢測(cè)方法的研究現(xiàn)狀和存在的一些問題的基礎(chǔ)上,利用變壓器運(yùn)行和檢修過程中測(cè)量到的電氣信息,從不同角度研究了變壓器繞組變形的診斷方法,研究?jī)?nèi)容包括以下幾個(gè)方面:找出了一種基于頻響阻抗法的變壓器繞組變形檢測(cè)方法。頻率響應(yīng)分析法在實(shí)際測(cè)試時(shí),因現(xiàn)場(chǎng)噪聲的干擾及接線長(zhǎng)度等的影響,導(dǎo)致診斷的準(zhǔn)確性受到限制。研究了變壓器繞組在低頻段和中頻段的阻抗頻率特性,以頻率響應(yīng)分析法的測(cè)試接線為基礎(chǔ),測(cè)量相關(guān)的電壓和電流值后,計(jì)算和處理以獲得阻抗頻率曲線,對(duì)比曲線間的差異實(shí)現(xiàn)對(duì)變壓器繞組變形的診斷。其診斷結(jié)果可以和頻率響應(yīng)分析法的判定結(jié)果互相驗(yàn)證,減少在頻率響應(yīng)分析法測(cè)試中由于現(xiàn)場(chǎng)環(huán)境干擾等造成的誤判。仿真結(jié)果表明,該方法能有效識(shí)別變壓器繞組不同類型的變形。提出了一種基于完整集成經(jīng)驗(yàn)?zāi)B(tài)分解和變量預(yù)測(cè)模型模式識(shí)別的變壓器繞組變形在線故障定位方法。暫態(tài)過電壓陡峭的上升沿中包含大量的高頻成分,當(dāng)其沖擊變壓器時(shí),電壓行波在繞組變形位置的能量損耗和正常位置存在差異,這種差異會(huì)體現(xiàn)在行波的某些頻率成分中。在繞組末端測(cè)量暫態(tài)過電壓信號(hào),采用具有自適應(yīng)白噪聲的完整集成經(jīng)驗(yàn)?zāi)B(tài)分解方法處理數(shù)據(jù)以得到本征模態(tài)分量,計(jì)算相關(guān)系數(shù)作為故障特征量,經(jīng)基于變量預(yù)測(cè)模型的模式識(shí)別方法訓(xùn)練后得到預(yù)測(cè)變量模型,對(duì)變形位置進(jìn)行定位。仿真結(jié)果表明,本方法能可靠反映繞組變形位置,具有較高的實(shí)用價(jià)值。提出了一種基于變分模態(tài)分解和概率密度估計(jì)的變壓器繞組變形在線檢測(cè)方法。短路電抗是判斷變壓器繞組是否變形的重要依據(jù),其在實(shí)際測(cè)量中受現(xiàn)場(chǎng)噪聲干擾而呈現(xiàn)一定的隨機(jī)性,從而影響對(duì)繞組狀態(tài)的判斷。首先,將變分模態(tài)分解應(yīng)用于電氣信號(hào)消噪處理,得到基波模態(tài)分量;然后,利用該基波模態(tài)分量在線計(jì)算短路電抗值;最后,對(duì)檢測(cè)周期內(nèi)求得的短路電抗樣本,通過參數(shù)估計(jì)得到短路電抗參數(shù)正態(tài)分布的概率密度函數(shù),根據(jù)其正態(tài)分布均值的估計(jì)值計(jì)算短路電抗偏差系數(shù),評(píng)估繞組的當(dāng)前狀態(tài)。仿真驗(yàn)證了該方法能穩(wěn)定地得到短路電抗估計(jì)值,避免噪聲和設(shè)備測(cè)量誤差對(duì)檢測(cè)的干擾,從而可靠檢測(cè)繞組變形。
[Abstract]:Transformer is one of the core equipments in power system. Its operation condition directly affects the safe and stable operation of power system. Winding deformation is one of the main fault types of transformer, which seriously threatens the normal operation of power system. In order to accurately diagnose transformer winding deformation and reduce transformer fault, on the basis of summarizing and analyzing the present research situation and some existing problems of transformer winding deformation detection method, the electrical information measured during transformer operation and maintenance is used. The diagnosis method of transformer winding deformation is studied from different angles. The research contents include the following aspects: a method of transformer winding deformation detection based on frequency response impedance method is found out. When the frequency response analysis method is tested, the accuracy of diagnosis is limited because of the interference of field noise and connection length. The impedance frequency characteristics of transformer windings in low and medium frequency bands are studied. Based on the test wiring of frequency response analysis, the relative voltage and current are measured, and the impedance frequency curves are calculated and processed. Contrast the difference between curves to realize the diagnosis of transformer winding deformation. The diagnostic results can be verified with the results of the frequency response analysis method, and the misjudgment caused by the field environment interference in the frequency response analysis method can be reduced. Simulation results show that this method can effectively identify different types of deformation of transformer windings. An on-line fault location method for transformer winding deformation based on integrated empirical mode decomposition and variable prediction model pattern recognition is proposed. The steep rise edge of transient overvoltage contains a large number of high-frequency components. When the transient overvoltage is impacted on the transformer, the energy loss of the voltage traveling wave in the winding deformation position is different from that in the normal position, which will be reflected in some frequency components of the traveling wave. The transient overvoltage signal is measured at the end of the winding. The integrated empirical mode decomposition method with adaptive white noise is used to process the data to obtain the intrinsic mode component, and the correlation coefficient is calculated as the fault characteristic. After training the pattern recognition method based on the variable prediction model, the prediction variable model is obtained, and the deformation position is located. The simulation results show that the method can reliably reflect the position of winding deformation and has high practical value. A method for on-line detection of transformer winding deformation based on variational mode decomposition and probability density estimation is proposed. Short-circuit reactance is an important basis for judging whether transformer windings are deformed or not. In actual measurement, short-circuit reactance presents a certain randomness due to field noise interference, thus affecting the judgment of winding state. Firstly, the variational mode decomposition is applied to the de-noising process of electrical signal to obtain the fundamental mode component, and then the short-circuit reactance is calculated on-line by using the fundamental mode component. Finally, the probability density function of the normal distribution of short-circuit reactance parameters is obtained by parameter estimation for short-circuit reactance samples obtained during the detection period, and the deviation coefficient of short-circuit reactance is calculated according to the estimated value of the mean value of short-circuit reactance distribution. Evaluate the current state of the winding. The simulation results show that the proposed method can stably obtain the short-circuit reactance estimation and avoid the interference of noise and measurement error, thus the winding deformation can be detected reliably.
【學(xué)位授予單位】：華北電力大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TM407

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本文編號(hào)：2301609