基于自適應(yīng)指紋識(shí)別的電力系統(tǒng)復(fù)雜原發(fā)故障診斷方法
發(fā)布時(shí)間：2018-09-09 10:41
【摘要】：根據(jù)“8·14”大停電等典型電力系統(tǒng)故障的演變規(guī)律,針對(duì)電力系統(tǒng)的故障一般都可以劃分為緩慢的相繼開(kāi)斷、快速的相繼開(kāi)斷、短暫的振蕩、大面積雪崩和漫長(zhǎng)的恢復(fù)等5個(gè)階段的特點(diǎn),本文把研究目標(biāo)確定為實(shí)現(xiàn)對(duì)電力系統(tǒng)的復(fù)雜原發(fā)故障的快速準(zhǔn)確診斷,希望設(shè)計(jì)一種能夠自適應(yīng)電網(wǎng)變化的實(shí)時(shí)的電力系統(tǒng)故障診斷方法,在故障發(fā)生的初期就可以快速分析出故障節(jié)點(diǎn),從而有效輔助調(diào)度員進(jìn)行故障處理,阻止故障的進(jìn)一步擴(kuò)大。通過(guò)對(duì)國(guó)內(nèi)外電力系統(tǒng)故障診斷方法的研究現(xiàn)狀的分析,本文發(fā)現(xiàn)目前提出的各種故障診斷方法都存在著兩個(gè)共性問(wèn)題：1)電網(wǎng)運(yùn)行方式的復(fù)雜多變性對(duì)故障診斷的影響問(wèn)題：電網(wǎng)網(wǎng)架結(jié)構(gòu)及運(yùn)行方式的變化很頻繁,導(dǎo)致這些故障診斷方法的模型、規(guī)則、算法均難以適應(yīng),從而影響了這些方法的實(shí)用化；2)開(kāi)關(guān)保護(hù)信號(hào)的不準(zhǔn)確性和不完備性對(duì)故障診斷的影響問(wèn)題：開(kāi)關(guān)保護(hù)裝置的誤動(dòng)拒動(dòng)及動(dòng)作信號(hào)的誤報(bào)漏報(bào)導(dǎo)致目前這些故障診斷方法的診斷結(jié)果的錯(cuò)誤率較高,也影響了這些方法的實(shí)用性；對(duì)于電網(wǎng)運(yùn)行方式的復(fù)雜多變性問(wèn)題,運(yùn)行方式的頻繁變化對(duì)故障診斷的影響主要體現(xiàn)在故障時(shí)對(duì)保護(hù)裝置以及備自投裝置的動(dòng)作行為邏輯的分析上。本文借鑒復(fù)雜適應(yīng)系統(tǒng)(Complex Adaptive System,CAS)理論的研究方法,結(jié)合本文故障診斷方法的具體需求,提出了一種對(duì)保護(hù)及備自投裝置的模型及其動(dòng)作邏輯的自適應(yīng)建模分析算法,給電網(wǎng)各設(shè)備賦予了動(dòng)態(tài)自適應(yīng)電網(wǎng)變化的能力,在此基礎(chǔ)上提出了一種電網(wǎng)復(fù)雜原發(fā)故障的預(yù)想故障集自適應(yīng)分析搜索方法,實(shí)現(xiàn)不用人工干預(yù)自動(dòng)分析出當(dāng)前電網(wǎng)的所有可能發(fā)生的預(yù)想故障,而且當(dāng)電網(wǎng)發(fā)生變化時(shí),可以基于變化后的電網(wǎng)運(yùn)行方式重新自動(dòng)更新這些預(yù)想故障。預(yù)想故障集的構(gòu)建是本文自適應(yīng)指紋識(shí)別故障診斷方法的基礎(chǔ)。針對(duì)開(kāi)關(guān)保護(hù)信號(hào)的不確定性問(wèn)題,本文提出了一種綜合利用電網(wǎng)故障時(shí)的動(dòng)作信號(hào)信息和電網(wǎng)各支路潮流等量測(cè)信息作為診斷依據(jù)的新思想,通過(guò)提取不同類型故障發(fā)生時(shí)電網(wǎng)各支路潮流的不同變化特征作為區(qū)別每種故障的“潮流指紋”,并提取故障發(fā)生時(shí)開(kāi)關(guān)保護(hù)動(dòng)作信號(hào)之間的動(dòng)作邏輯關(guān)系形成用來(lái)量化表征各種電網(wǎng)故障的“動(dòng)作信號(hào)指紋”,借鑒實(shí)際人體指紋識(shí)別系統(tǒng)的成熟經(jīng)驗(yàn),采用模式識(shí)別的方法進(jìn)行故障的識(shí)別診斷。為了實(shí)現(xiàn)該思想,本文重點(diǎn)研究了對(duì)這兩種指紋的提取方法：1)通過(guò)對(duì)電網(wǎng)故障時(shí)開(kāi)關(guān)保護(hù)動(dòng)作信息的深入挖掘分析,提出了從動(dòng)作情況、動(dòng)作之間的時(shí)序約束關(guān)系、拓?fù)浼s束關(guān)系、相關(guān)量測(cè)變化的約束關(guān)系等方面來(lái)量化電網(wǎng)故障的動(dòng)作信號(hào)指紋的自適應(yīng)提取方法。該方法一方面繼承了已有的專家系統(tǒng)知識(shí)庫(kù)的診斷知識(shí),另一方面通過(guò)這種把離散型信號(hào)轉(zhuǎn)化為連續(xù)性變量的量化處理,可以在很大程度上避免專家系統(tǒng)推理機(jī)會(huì)受信號(hào)準(zhǔn)確性的影響而導(dǎo)致推理錯(cuò)誤問(wèn)題,而且量化后的結(jié)果可以方便的實(shí)現(xiàn)與其他故障診斷方法的結(jié)合,如下文的基于潮流指紋的故障診斷。2)提出了一種基于主成分分析方法的潮流指紋特征向量提取方法：通過(guò)對(duì)原始特征的在均方差最小意義上的線性變換,剔除具有相關(guān)性和重疊性的變量,在不損失原始數(shù)據(jù)的主要信息的前提下,實(shí)現(xiàn)了以較少的綜合性正交變量來(lái)替代原始變量,構(gòu)建出維數(shù)較低的故障潮流指紋主成分特征向量。實(shí)踐證明,基于該空間進(jìn)行故障診斷具有更高的效率和精度。在此基礎(chǔ)上,本文根據(jù)潮流指紋和動(dòng)作信號(hào)指紋的特點(diǎn)分別設(shè)計(jì)了相應(yīng)的故障識(shí)別策略,并在分析了這兩種方法各自的診斷盲區(qū)后,進(jìn)一步提出了一種基于動(dòng)作信號(hào)指紋和潮流指紋的故障診斷融合識(shí)別策略。該策略基于D-S證據(jù)理論的證據(jù)組合方法,把潮流指紋信息與動(dòng)作信號(hào)指紋信息相融合,很大程度上解決了由于上述兩問(wèn)題對(duì)故障診斷的影響,從而提高本文自適應(yīng)故障識(shí)別診斷方法的準(zhǔn)確性和實(shí)用性。最后,本文用一個(gè)具體的地區(qū)電網(wǎng)故障實(shí)例,對(duì)本文提出的復(fù)雜原發(fā)故障自適應(yīng)指紋識(shí)別診斷方法進(jìn)行實(shí)際應(yīng)用分析,證明了本方法在電力系統(tǒng)故障診斷中的有效性和實(shí)用性。
[Abstract]:According to the evolution law of typical power system faults such as "8.14" blackout, power system faults can be generally divided into five stages: slow successive interruption, fast successive interruption, short oscillation, large area avalanche and long recovery. The research goal is to realize the complex origin of power system. In order to diagnose faults quickly and accurately, we hope to design a real-time fault diagnosis method which can adapt to the changes of power grid. In the early stage of faults, fault nodes can be quickly analyzed, which can effectively assist dispatchers to deal with faults and prevent further expansion of faults. Based on the analysis of the research status of the fault diagnosis methods, this paper finds that there are two common problems in all kinds of fault diagnosis methods: 1) the influence of the complex and changeable operation modes of the power grid on the fault diagnosis: the frequent changes of the grid structure and operation modes lead to the difficulty of the models, rules and algorithms of these fault diagnosis methods. 2) The influence of the inaccuracy and incompleteness of the switching protection signal on the fault diagnosis: the misoperation of the switching protection device and the false alarm and missed alarm of the action signal lead to the high error rate of the diagnosis results of the current fault diagnosis methods, and also affect the reality of these methods. For the complex and changeable operation mode of power grid, the influence of frequent change of operation mode on fault diagnosis is mainly reflected in the analysis of action logic of protective devices and automatic switching devices. In order to meet the specific requirements of fault diagnosis methods, an adaptive modeling and analysis algorithm for the model and operation logic of protective and standby automatic switching devices is proposed, which endows each device with the ability of dynamic self-adapting to the changes of power grid. On this basis, an adaptive analysis and search method for the expected fault set of complex primary faults in power grid is proposed. All the possible expected faults in the current power grid can be automatically analyzed without manual intervention, and when the power grid changes, these expected faults can be automatically updated based on the changed operation mode of the power grid. In this paper, a new idea is proposed, which integrates the action signal information of power system faults and the measurement information of power flow of each branch of the power system as the diagnostic basis. Taking the action logic relation between the action signals of the switching protection when the fault occurs to form the "action signal fingerprint" which is used to quantify and characterize various power network faults, and referring to the mature experience of the actual human fingerprint identification system, the method of pattern recognition is used to identify and diagnose the faults. In order to realize this idea, this paper focuses on the two methods. This paper presents an adaptive fingerprint extraction method to quantify the fingerprint of power system fault action signals from the following aspects: action condition, time sequence constraint relation between actions, topological constraint relation, constraint relation of correlation measurement change, etc. On the one hand, it inherits the diagnostic knowledge of the existing expert system knowledge base, on the other hand, by quantifying the discrete signals into continuous variables, it can largely avoid the inference error caused by the influence of the accuracy of the signals on the inference opportunities of the expert system, and the quantified results can be easily realized. Combining with other fault diagnosis methods, the following is the fault diagnosis based on power flow fingerprint. 2) A power flow fingerprint feature vector extraction method based on principal component analysis is proposed. By linear transformation of the original features in the sense of minimum mean square deviation, the variables with correlation and overlap are eliminated without losing the original features. On the premise of the main information of the data, the principal component eigenvector of fault power flow fingerprint with lower dimension is constructed by replacing the original variables with fewer comprehensive orthogonal variables. Practice proves that fault diagnosis based on this space has higher efficiency and accuracy. After analyzing the blind areas of the two methods, a fault diagnosis fusion identification strategy based on action signal fingerprint and power flow fingerprint is proposed. The strategy is based on the combination of D-S evidence theory and power flow fingerprint. The fusion of fingerprint information solves the influence of the above two problems on fault diagnosis to a great extent, so as to improve the accuracy and practicability of the adaptive fault diagnosis method in this paper. Application analysis shows that this method is effective and practical in power system fault diagnosis.
【學(xué)位授予單位】：華北電力大學(xué)(北京)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2014
【分類號(hào)】：TM732
本文編號(hào)：2232146
[Abstract]:According to the evolution law of typical power system faults such as "8.14" blackout, power system faults can be generally divided into five stages: slow successive interruption, fast successive interruption, short oscillation, large area avalanche and long recovery. The research goal is to realize the complex origin of power system. In order to diagnose faults quickly and accurately, we hope to design a real-time fault diagnosis method which can adapt to the changes of power grid. In the early stage of faults, fault nodes can be quickly analyzed, which can effectively assist dispatchers to deal with faults and prevent further expansion of faults. Based on the analysis of the research status of the fault diagnosis methods, this paper finds that there are two common problems in all kinds of fault diagnosis methods: 1) the influence of the complex and changeable operation modes of the power grid on the fault diagnosis: the frequent changes of the grid structure and operation modes lead to the difficulty of the models, rules and algorithms of these fault diagnosis methods. 2) The influence of the inaccuracy and incompleteness of the switching protection signal on the fault diagnosis: the misoperation of the switching protection device and the false alarm and missed alarm of the action signal lead to the high error rate of the diagnosis results of the current fault diagnosis methods, and also affect the reality of these methods. For the complex and changeable operation mode of power grid, the influence of frequent change of operation mode on fault diagnosis is mainly reflected in the analysis of action logic of protective devices and automatic switching devices. In order to meet the specific requirements of fault diagnosis methods, an adaptive modeling and analysis algorithm for the model and operation logic of protective and standby automatic switching devices is proposed, which endows each device with the ability of dynamic self-adapting to the changes of power grid. On this basis, an adaptive analysis and search method for the expected fault set of complex primary faults in power grid is proposed. All the possible expected faults in the current power grid can be automatically analyzed without manual intervention, and when the power grid changes, these expected faults can be automatically updated based on the changed operation mode of the power grid. In this paper, a new idea is proposed, which integrates the action signal information of power system faults and the measurement information of power flow of each branch of the power system as the diagnostic basis. Taking the action logic relation between the action signals of the switching protection when the fault occurs to form the "action signal fingerprint" which is used to quantify and characterize various power network faults, and referring to the mature experience of the actual human fingerprint identification system, the method of pattern recognition is used to identify and diagnose the faults. In order to realize this idea, this paper focuses on the two methods. This paper presents an adaptive fingerprint extraction method to quantify the fingerprint of power system fault action signals from the following aspects: action condition, time sequence constraint relation between actions, topological constraint relation, constraint relation of correlation measurement change, etc. On the one hand, it inherits the diagnostic knowledge of the existing expert system knowledge base, on the other hand, by quantifying the discrete signals into continuous variables, it can largely avoid the inference error caused by the influence of the accuracy of the signals on the inference opportunities of the expert system, and the quantified results can be easily realized. Combining with other fault diagnosis methods, the following is the fault diagnosis based on power flow fingerprint. 2) A power flow fingerprint feature vector extraction method based on principal component analysis is proposed. By linear transformation of the original features in the sense of minimum mean square deviation, the variables with correlation and overlap are eliminated without losing the original features. On the premise of the main information of the data, the principal component eigenvector of fault power flow fingerprint with lower dimension is constructed by replacing the original variables with fewer comprehensive orthogonal variables. Practice proves that fault diagnosis based on this space has higher efficiency and accuracy. After analyzing the blind areas of the two methods, a fault diagnosis fusion identification strategy based on action signal fingerprint and power flow fingerprint is proposed. The strategy is based on the combination of D-S evidence theory and power flow fingerprint. The fusion of fingerprint information solves the influence of the above two problems on fault diagnosis to a great extent, so as to improve the accuracy and practicability of the adaptive fault diagnosis method in this paper. Application analysis shows that this method is effective and practical in power system fault diagnosis.
【學(xué)位授予單位】：華北電力大學(xué)(北京)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2014
【分類號(hào)】：TM732
【相似文獻(xiàn)】
相關(guān)博士學(xué)位論文 前1條
1 劉延樂(lè);基于自適應(yīng)指紋識(shí)別的電力系統(tǒng)復(fù)雜原發(fā)故障診斷方法[D];華北電力大學(xué)(北京);2014年
，本文編號(hào)：2232146
本文鏈接：http://sikaile.net/kejilunwen/dianlilw/2232146.html
最近更新
教材專著
