本文選題：狀態(tài)檢修 + 漸變過(guò)程��； 參考：《山東大學(xué)》2014年博士論文

【摘要】：變電設(shè)備的狀態(tài)對(duì)電網(wǎng)安全可靠運(yùn)行起著非常關(guān)鍵的作用,且隨著電網(wǎng)規(guī)模的擴(kuò)大和電氣設(shè)備容量的增加,這種作用更為顯著。變電設(shè)備一旦故障,將直接造成用戶(hù)停電,從而帶來(lái)經(jīng)濟(jì)損失,甚至威脅人身安全。因此,研究變電設(shè)備的潛伏故障檢測(cè)、狀態(tài)評(píng)估和檢修措施至關(guān)重要。 傳統(tǒng)的定期檢修制度因存在成本高、潛伏故障檢測(cè)能力差等問(wèn)題,正逐步被基于狀態(tài)的檢修制度替代。狀態(tài)檢修,即根據(jù)設(shè)備狀態(tài)確定合適的檢修時(shí)機(jī)和檢修措施,以實(shí)現(xiàn)人、財(cái)、物的最優(yōu)配置,其基礎(chǔ)是設(shè)備狀態(tài)的在線(xiàn)監(jiān)測(cè)與評(píng)估。目前研究主要思路是通過(guò)綜合分析電氣量和非電氣量監(jiān)測(cè)數(shù)據(jù),利用各種算法評(píng)估設(shè)備狀態(tài),取得了較好的實(shí)際效果。但是,狀態(tài)檢修更關(guān)注設(shè)備狀態(tài)的變化過(guò)程,這是準(zhǔn)確確定檢修時(shí)機(jī),進(jìn)而實(shí)現(xiàn)設(shè)備利用率最大化、并對(duì)生產(chǎn)影響最小的基礎(chǔ)。而已有研究側(cè)重于設(shè)備當(dāng)前健康狀況評(píng)估,缺少對(duì)設(shè)備狀態(tài)漸進(jìn)變化過(guò)程的細(xì)致分析,迫切需要研究相應(yīng)分析方法。利用豐富的電氣信息,建立其與設(shè)備狀態(tài)之間的關(guān)聯(lián),為設(shè)備狀態(tài)評(píng)估提供輔助分析信息具有無(wú)需附加額外裝置,量測(cè)數(shù)據(jù)豐富,獲取方便的優(yōu)勢(shì)。因此,論文基于電氣信息,從數(shù)據(jù)挖掘角度出發(fā),對(duì)比設(shè)備端口模型參數(shù)概率分布差異實(shí)現(xiàn)漸變過(guò)程特征提取,分析了雷擊、外部短路故障等沖擊對(duì)變電設(shè)備狀態(tài)變化過(guò)程的影響,并在把握漸變過(guò)程規(guī)律基礎(chǔ)上提取未來(lái)變化趨勢(shì)特征,以期為檢修措施的制定提供更多輔助信息,有利于狀態(tài)檢修的進(jìn)一步實(shí)施。論文的創(chuàng)新性工作如下： (1)變電設(shè)備狀態(tài)漸變過(guò)程分析方法：變電設(shè)備狀態(tài)漸進(jìn)變化過(guò)程是由諸多微小變化累積而成,這些微小變化可以通過(guò)基于廣義伏安特性構(gòu)建設(shè)備端口模型的參數(shù)變化規(guī)律來(lái)間接反映。然而,外界環(huán)境和量測(cè)誤差的影響,導(dǎo)致相應(yīng)參數(shù)辨識(shí)結(jié)果呈現(xiàn)較強(qiáng)的隨機(jī)性,其內(nèi)在趨勢(shì)特征規(guī)律難以提取。因此,提出基于統(tǒng)計(jì)學(xué)的變電設(shè)備狀態(tài)漸變過(guò)程分析方法。首先,將變電設(shè)備運(yùn)行過(guò)程分成多個(gè)時(shí)段,采用非參數(shù)核密度估計(jì)法計(jì)算各個(gè)時(shí)段內(nèi)設(shè)備端口模型參數(shù)的概率密度函數(shù),并提取參數(shù)的概率特征。然后,分析不同時(shí)段內(nèi)變電設(shè)備端口模型參數(shù)概率特征的差異,從而定義了四個(gè)表征變電設(shè)備狀態(tài)漸變過(guò)程的指標(biāo)：端口模型參數(shù)概率密度函數(shù)最大值對(duì)應(yīng)參數(shù)值Ckmax,表示該時(shí)段內(nèi)端口模型參數(shù)的最大可能值；各時(shí)段內(nèi)Ckmax相對(duì)第一個(gè)時(shí)段內(nèi)C1max的差值,表示設(shè)備損傷隨運(yùn)行時(shí)間的不斷積累；各時(shí)段內(nèi)端口模型參數(shù)相對(duì)于第一個(gè)時(shí)段內(nèi)的變化概率,表示設(shè)備不斷遠(yuǎn)離初始狀態(tài)；各時(shí)段內(nèi)端口模型參數(shù)相對(duì)于告警狀態(tài)下對(duì)應(yīng)參數(shù)概率分布的變化概率,表示設(shè)備逐漸靠近告警狀態(tài)。最后,利用這些指標(biāo)分析端口模型參數(shù)漸變過(guò)程,得到指標(biāo)序列,從而為分析變電設(shè)備狀態(tài)變化趨勢(shì)提供輔助分析基礎(chǔ)。所提方法從統(tǒng)計(jì)的角度出發(fā),通過(guò)大量歷史樣本數(shù)據(jù)挖掘概率特征分析漸變過(guò)程,受少數(shù)不良數(shù)據(jù)影響小,具有良好的抗干擾能力和魯棒性。其中,概率密度函數(shù)的計(jì)算采用非參數(shù)核密度估計(jì)法,不需要預(yù)先假設(shè)設(shè)備端口模型參數(shù)的分布,減少了主觀(guān)因素的影響；變電設(shè)備端口模型參數(shù)通過(guò)偏最小二乘回歸辨識(shí)得到,保證了結(jié)果準(zhǔn)確可靠。以分析變壓器繞組形變累積效應(yīng)為例,通過(guò)蒙特卡洛法獲取漏電感參數(shù),實(shí)現(xiàn)變壓器繞組形變累積過(guò)程的模擬；利用定義的四個(gè)指標(biāo)對(duì)該漸變過(guò)程進(jìn)行分析,結(jié)果表明該方法有效可行。 (2)沖擊對(duì)變電設(shè)備狀態(tài)漸變過(guò)程影響的分析方法：變電設(shè)備運(yùn)行過(guò)程中,不可避免的遭受來(lái)自雷擊、外部短路故障等沖擊的影響,沖擊導(dǎo)致的變電設(shè)備狀態(tài)變化隱含著設(shè)備安全信息,必須引起足夠重視。量化分析外部沖擊帶來(lái)端口模型參數(shù)的變化對(duì)于后續(xù)變化過(guò)程特征提取十分必要。但是,外界環(huán)境和量測(cè)誤差造成的端口模型參數(shù)隨機(jī)波動(dòng),增加了檢測(cè)的難度。因此,考慮端口模型參數(shù)的隨機(jī)波動(dòng)特性,提出分別基于概率密度函數(shù)差異和自適應(yīng)積分算法的兩種檢測(cè)與分析方法。前一種方法中,端口模型參數(shù)變化的檢測(cè)通過(guò)分析相鄰時(shí)間窗口內(nèi)參數(shù)的概率分布差異實(shí)現(xiàn),變化的幅度通過(guò)概率密度函數(shù)最大值對(duì)應(yīng)參數(shù)值的差值反映,該方法檢測(cè)準(zhǔn)確,計(jì)算量較大,適用于沖擊過(guò)后量化分析端口模型參數(shù)的變化；后一種方法中,利用相鄰時(shí)間窗口內(nèi)端口模型參數(shù)差值樣本的均值不同對(duì)變電設(shè)備狀態(tài)變化進(jìn)行檢測(cè),并直接用該均值反映端口模型參數(shù)變化幅度,該方法計(jì)算快速,能及時(shí)檢測(cè)端口模型參數(shù)在沒(méi)有達(dá)到報(bào)警或保護(hù)動(dòng)作條件時(shí)的突變。在這兩種方法中,門(mén)檻值均由歷史數(shù)據(jù)自適應(yīng)確定,能夠同時(shí)協(xié)調(diào)檢測(cè)的靈敏度和準(zhǔn)確度。通過(guò)改變變壓器漏電感參數(shù)模擬雷擊、短路故障等沖擊造成的變壓器狀態(tài)變化,仿真分析結(jié)果驗(yàn)證了這兩種方法的有效性和可靠性。 (3)間接反映設(shè)備狀態(tài)的端口模型參數(shù)變化趨勢(shì)特征分析方法：狀態(tài)檢修需要分別從長(zhǎng)時(shí)間尺度和短時(shí)間尺度對(duì)變電設(shè)備狀態(tài)的變化趨勢(shì)進(jìn)行把握。為此,根據(jù)提取的端口模型參數(shù)漸變過(guò)程分析指標(biāo)序列,利用經(jīng)驗(yàn)?zāi)B(tài)分解提取指標(biāo)的趨勢(shì)分量,建立長(zhǎng)時(shí)間尺度下未來(lái)時(shí)段內(nèi)指標(biāo)的預(yù)測(cè)模型,預(yù)估達(dá)到變電設(shè)備告警狀態(tài)對(duì)應(yīng)端口模型參數(shù)的時(shí)段,進(jìn)而為估計(jì)變電設(shè)備當(dāng)前狀態(tài)距離告警狀態(tài)的時(shí)間進(jìn)行輔助分析,為變電設(shè)備狀態(tài)評(píng)估、檢修措施制定提供有益的輔助依據(jù)。為詳細(xì)分析未來(lái)短時(shí)間尺度下端口模型參數(shù)變化情況,提出基于狀態(tài)轉(zhuǎn)移概率矩陣預(yù)測(cè)概率分布的方法；通過(guò)統(tǒng)計(jì)相鄰時(shí)間窗口內(nèi)端口模型參數(shù)在各個(gè)參數(shù)變化區(qū)間的轉(zhuǎn)移情況,建立狀態(tài)轉(zhuǎn)移概率矩陣,并預(yù)測(cè)后續(xù)時(shí)間窗口內(nèi)端口模型參數(shù)的分布,進(jìn)而輔助分析未來(lái)設(shè)備狀態(tài)變化細(xì)節(jié)。以分析變壓器繞組形變累積效應(yīng)導(dǎo)致的變壓器狀態(tài)變化為例,在表征繞組形變累積過(guò)程的端口模型參數(shù)指標(biāo)序列基礎(chǔ)上,對(duì)當(dāng)前參數(shù)距離告警狀態(tài)對(duì)應(yīng)參數(shù)值的時(shí)間進(jìn)行了估計(jì),能夠?yàn)樽儔浩鳡顟B(tài)評(píng)估提供輔助依據(jù),有利于檢修措施的制定。為模擬變壓器臨近告警狀態(tài)的場(chǎng)景,利用蒙特卡洛法獲取三個(gè)相鄰時(shí)間窗口內(nèi)漏電感參數(shù)；使用前兩個(gè)時(shí)間窗口內(nèi)端口模型參數(shù)樣本計(jì)算狀態(tài)轉(zhuǎn)移概率矩陣,并預(yù)測(cè)第三個(gè)時(shí)間窗口內(nèi)端口模型參數(shù)的分布；最后,計(jì)算其與直接利用蒙特卡洛模擬獲得第三個(gè)窗口內(nèi)參數(shù)樣本的相似度,結(jié)果驗(yàn)證了短時(shí)間尺度下預(yù)測(cè)方法的有效性。
[Abstract]:The status of the power - changing equipment plays a very important role in the safe and reliable operation of the power grid , and with the expansion of the scale of the power grid and the increase of the capacity of the electrical equipment , the effect is more obvious . Once the substation fails , it will directly cause the user to power off , thus causing economic loss and even threatening the personal safety . Therefore , it is important to study the latent fault detection , state assessment and overhaul measures of the power transformer equipment .

In this paper , based on the analysis of electrical quantity and non - electric quantity monitoring data , it is necessary to study the state of equipment .

( 1 ) The state gradual change process analysis method of the power transformation equipment : The gradual change process of the state of the power transformation equipment is accumulated by many small changes , which can be indirectly reflected by constructing the parameter variation law of the equipment port model based on the generalized volt - ampere characteristic .
The difference between Ckmax and C1max during each time period indicates the continuous accumulation of equipment damage with running time ;
the parameter of the port model in each time period is relative to the change probability in the first time period , indicating that the device is continuously moving away from the initial state ;
in that method , a non - parametric kernel density estimation method is adopted to analyze the probability characteristic of a large number of historical sample data mining , and the influence of subjective factors is reduced by not need to pre - assume the distribution of the parameter of the equipment port model .
The parameters of the port model of the transformation equipment are identified by the partial least square regression identification , and the result is ensured to be accurate and reliable . By analyzing the deformation accumulation effect of the transformer winding , the leakage inductance parameter is obtained by Monte Carlo method , and the simulation of the deformation accumulation process of the transformer winding is realized ;
The gradient process is analyzed by using four defined indexes , and the results show that the method is effective and feasible .

( 2 ) The analysis method of the influence of the impact on the state gradual change process of the power transformation equipment : During the operation of the power transformer , it is inevitable to suffer from the impact of lightning , external short circuit fault and so on . The change of the port model parameters caused by the impact of the external environment and the measurement error is very necessary . However , the detection of the parameter change of the port model can be realized by analyzing the difference of the probability distribution of the parameters in the adjacent time windows .
in that lat method , the state change of the power transformation equipment is detected by using the mean value of the difference sample of the port model parameter in the adjacent time window , and the change amplitude of the port model parameter is directly reflected by the mean value , the method is fast , the sensitivity and the accuracy of the detection can be simultaneously coordinated by the historical data adaptive determination , and the simulation analysis result verifies the validity and the reliability of the two methods .

( 3 ) The change trend characteristic analysis method of port model parameters that indirectly reflects the state of the equipment : the state maintenance needs to grasp the change tendency of the state of the transformer equipment from the long time scale and the short time scale , respectively .
The state transition probability matrix is established by counting the transition of the port model parameters in the adjacent time windows in each parameter change interval , the distribution of the port model parameters in the subsequent time window is predicted , and the details of the state change of the future equipment are analyzed , and the time of the parameter value corresponding to the current parameter distance alarm state is estimated based on the parameter index sequence of the port model characterizing the deformation accumulation process of the transformer .
calculating the state transition probability matrix by using the port model parameter samples in the first two time windows and predicting the distribution of the port model parameters in the third time window ;
Finally , the similarity of the parameter samples in the third window is obtained by Monte Carlo simulation , and the validity of the prediction method under the short time scale is verified .
【學(xué)位授予單位】：山東大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2014
【分類(lèi)號(hào)】：TM711

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