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

【摘要】：變電設(shè)備的狀態(tài)對電網(wǎng)安全可靠運行起著非常關(guān)鍵的作用,且隨著電網(wǎng)規(guī)模的擴大和電氣設(shè)備容量的增加,這種作用更為顯著。變電設(shè)備一旦故障,將直接造成用戶停電,從而帶來經(jīng)濟損失,甚至威脅人身安全。因此,研究變電設(shè)備的潛伏故障檢測、狀態(tài)評估和檢修措施至關(guān)重要。 傳統(tǒng)的定期檢修制度因存在成本高、潛伏故障檢測能力差等問題,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)確定合適的檢修時機和檢修措施,以實現(xiàn)人、財、物的最優(yōu)配置,其基礎(chǔ)是設(shè)備狀態(tài)的在線監(jiān)測與評估。目前研究主要思路是通過綜合分析電氣量和非電氣量監(jiān)測數(shù)據(jù),利用各種算法評估設(shè)備狀態(tài),取得了較好的實際效果。但是,狀態(tài)檢修更關(guān)注設(shè)備狀態(tài)的變化過程,這是準確確定檢修時機,進而實現(xiàn)設(shè)備利用率最大化、并對生產(chǎn)影響最小的基礎(chǔ)。而已有研究側(cè)重于設(shè)備當前健康狀況評估,缺少對設(shè)備狀態(tài)漸進變化過程的細致分析,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)評估提供輔助分析信息具有無需附加額外裝置,量測數(shù)據(jù)豐富,獲取方便的優(yōu)勢。因此,論文基于電氣信息,從數(shù)據(jù)挖掘角度出發(fā),對比設(shè)備端口模型參數(shù)概率分布差異實現(xiàn)漸變過程特征提取,分析了雷擊、外部短路故障等沖擊對變電設(shè)備狀態(tài)變化過程的影響,并在把握漸變過程規(guī)律基礎(chǔ)上提取未來變化趨勢特征,以期為檢修措施的制定提供更多輔助信息,有利于狀態(tài)檢修的進一步實施。論文的創(chuàng)新性工作如下： (1)變電設(shè)備狀態(tài)漸變過程分析方法：變電設(shè)備狀態(tài)漸進變化過程是由諸多微小變化累積而成,這些微小變化可以通過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)律來間接反映。然而,外界環(huán)境和量測誤差的影響,導(dǎo)致相應(yīng)參數(shù)辨識結(jié)果呈現(xiàn)較強的隨機性,其內(nèi)在趨勢特征規(guī)律難以提取。因此,提出基于統(tǒng)計學(xué)的變電設(shè)備狀態(tài)漸變過程分析方法。首先,將變電設(shè)備運行過程分成多個時段,采用非參數(shù)核密度估計法計算各個時段內(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,從而定義了四個表征變電設(shè)備狀態(tài)漸變過程的指標：端口模型參數(shù)概率密度函數(shù)最大值對應(yīng)參數(shù)值Ckmax,表示該時段內(nèi)端口模型參數(shù)的最大可能值；各時段內(nèi)Ckmax相對第一個時段內(nèi)C1max的差值,表示設(shè)備損傷隨運行時間的不斷積累；各時段內(nèi)端口模型參數(shù)相對于第一個時段內(nèi)的變化概率,表示設(shè)備不斷遠離初始狀態(tài)；各時段內(nèi)端口模型參數(shù)相對于告警狀態(tài)下對應(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,利用這些指標分析端口模型參數(shù)漸變過程,得到指標序列,從而為分析變電設(shè)備狀態(tài)變化趨勢提供輔助分析基礎(chǔ)。所提方法從統(tǒng)計的角度出發(fā),通過大量歷史樣本數(shù)據(jù)挖掘概率特征分析漸變過程,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)的計算采用非參數(shù)核密度估計法,不需要預(yù)先假設(shè)設(shè)備端口模型參數(shù)的分布,減少了主觀因素的影響；變電設(shè)備端口模型參數(shù)通過偏最小二乘回歸辨識得到,保證了結(jié)果準確可靠。以分析變壓器繞組形變累積效應(yīng)為例,通過蒙特卡洛法獲取漏電感參數(shù),實現(xiàn)變壓器繞組形變累積過程的模擬；利用定義的四個指標對該漸變過程進行分析,結(jié)果表明該方法有效可行。 (2)沖擊對變電設(shè)備狀態(tài)漸變過程影響的分析方法：變電設(shè)備運行過程中,不可避免的遭受來自雷擊、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端口模型參數(shù)的變化對于后續(xù)變化過程特征提取十分必要。但是,外界環(huán)境和量測誤差造成的端口模型參數(shù)隨機波動,增加了檢測的難度。因此,考慮端口模型參數(shù)的隨機波動特性,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與分析方法。前一種方法中,端口模型參數(shù)變化的檢測通過分析相鄰時間窗口內(nèi)參數(shù)的概率分布差異實現(xiàn),變化的幅度通過概率密度函數(shù)最大值對應(yīng)參數(shù)值的差值反映,該方法檢測準確,計算量較大,適用于沖擊過后量化分析端口模型參數(shù)的變化；后一種方法中,利用相鄰時間窗口內(nèi)端口模型參數(shù)差值樣本的均值不同對變電設(shè)備狀態(tài)變化進行檢測,并直接用該均值反映端口模型參數(shù)變化幅度,該方法計算快速,能及時檢測端口模型參數(shù)在沒有達到報警或保護動作條件時的突變。在這兩種方法中,門檻值均由歷史數(shù)據(jù)自適應(yīng)確定,能夠同時協(xié)調(diào)檢測的靈敏度和準確度。通過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)變化趨勢特征分析方法：狀態(tài)檢修需要分別從長時間尺度和短時間尺度對變電設(shè)備狀態(tài)的變化趨勢進行把握。為此,根據(jù)提取的端口模型參數(shù)漸變過程分析指標序列,利用經(jīng)驗?zāi)B(tài)分解提取指標的趨勢分量,建立長時間尺度下未來時段內(nèi)指標的預(yù)測模型,預(yù)估達到變電設(shè)備告警狀態(tài)對應(yīng)端口模型參數(shù)的時段,進而為估計變電設(shè)備當前狀態(tài)距離告警狀態(tài)的時間進行輔助分析,為變電設(shè)備狀態(tài)評估、檢修措施制定提供有益的輔助依據(jù)。為詳細分析未來短時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)測概率分布的方法；通過統(tǒng)計相鄰時間窗口內(nèi)端口模型參數(shù)在各個參數(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)測后續(xù)時間窗口內(nèi)端口模型參數(shù)的分布,進而輔助分析未來設(shè)備狀態(tài)變化細節(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程的端口模型參數(shù)指標序列基礎(chǔ)上,對當前參數(shù)距離告警狀態(tài)對應(yīng)參數(shù)值的時間進行了估計,能夠為變壓器狀態(tài)評估提供輔助依據(jù),有利于檢修措施的制定。為模擬變壓器臨近告警狀態(tài)的場景,利用蒙特卡洛法獲取三個相鄰時間窗口內(nèi)漏電感參數(shù)；使用前兩個時間窗口內(nèi)端口模型參數(shù)樣本計算狀態(tài)轉(zhuǎn)移概率矩陣,并預(yù)測第三個時間窗口內(nèi)端口模型參數(shù)的分布；最后,計算其與直接利用蒙特卡洛模擬獲得第三個窗口內(nèi)參數(shù)樣本的相似度,結(jié)果驗證了短時間尺度下預(yù)測方法的有效性。
[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)位級別】：博士
【學(xué)位授予年份】：2014
【分類號】：TM711

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