發(fā)布時間：2018-03-14 02:51

  本文選題：輸電線路故障　切入點：故障原因辨識　出處：《山東大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：輸電線路是電網(wǎng)的重要組成部分,覆蓋范圍廣,運行環(huán)境惡劣復(fù)雜,極易由于自然災(zāi)害、人為破壞等原因而發(fā)生故障。作為省級電網(wǎng)的骨干電力網(wǎng)絡(luò),220kV及以上的高壓交流輸電線路的故障必然給電網(wǎng)帶來沖擊,威脅電網(wǎng)的安全穩(wěn)定運行。同時,繼電保護裝置的快速動作可能導(dǎo)致故障線路損壞痕跡不明顯,增加了故障檢修困難程度。在線路跳閘后,準確、及時地判斷出故障的可能誘因同時結(jié)合測距位置以及當?shù)氐匦螤顩r,可指導(dǎo)巡線,快速、準確地發(fā)現(xiàn)故障點,減少故障排除時間,提高電力系統(tǒng)的供電可靠性及和運行穩(wěn)定性,具有重大的經(jīng)濟效益及社會效益。目前國內(nèi)外的辨識研究較少,多為針對某種故障的規(guī)律統(tǒng)計以及防治措施建立,尚無系統(tǒng)的辨識方法。本文針對輸電線路常見的雷擊、風偏、鳥閃、污閃、樹閃以及山火六種原因引起的輸電線路單相故障進行研究。在各故障類型故障機理分析的基礎(chǔ)上,對故障特征規(guī)律進行深入剖析,結(jié)合歷史經(jīng)驗對故障相關(guān)的外部特征以及內(nèi)部特征進行總結(jié)。外部特征包括故障發(fā)生時對應(yīng)的天氣、季節(jié)以及時間特征,內(nèi)部特征則指反映故障本質(zhì)的故障重合閘情況、故障相電流直流含量、三次諧波含量、過零點畸變情況以及過渡電阻大小和性質(zhì),并通過實際故障錄波數(shù)據(jù)的處理分析進行驗證。更進一步,本文采用基于Fisher分數(shù)的特征挖掘方法實現(xiàn)對六種故障模型辨識而言各特征的重要性程度計算并進行排序,從而探究出各故障原因類型辨識的主要影響因素,有針對性地建立不同的故障原因辨識模型。鑒于輸電線路故障樣本的不完備,本文采用具有強泛化能力的支持向量機(SVM)實現(xiàn)故障原因的分類辨識,算法以結(jié)構(gòu)風險最小化為目標,在一定程度上避免了過學(xué)習問題。針對六種故障原因分別帶入相關(guān)特征,利用樣本進行訓(xùn)練建立辨識模型,并且使用粒子群算法(PSO)對各模型參數(shù)進行優(yōu)化。預(yù)測階段,將待測故障樣本進行相應(yīng)的特征分析,并分別帶入該六種故障模型,得出對應(yīng)于各種故障類型的概率,取其中的最大值所對應(yīng)的故障原因類型為判別結(jié)果并進行驗證測試。并對于算法提出了基于樣本數(shù)據(jù)不平衡問題以及算法自學(xué)習能力的改進。最后通過測試結(jié)果表明,基于PSO優(yōu)化的SVM算法辨識效果得到了提升,并且通過選取特征重要性排序靠前的特征可以在保證辨識準確率的同時簡化模型計算量,提高效率。綜上,本文基于雷擊、風偏、鳥閃、污閃、樹閃以及山火六大輸電線路故障的綜合分析研究,提出一種綜合故障外部特征以及故障內(nèi)部特征的PSO-SVM故障原因自學(xué)習辨識的方法。本方法建立在對實際故障數(shù)據(jù)的挖掘分析的基礎(chǔ)上,理論依據(jù)充分,算法仿真證明準確率高。同時本方法所用故障數(shù)據(jù)信息容易獲取,應(yīng)用時可結(jié)合當?shù)毓收弦?guī)律進行擴展,能夠?qū)崿F(xiàn)對常見故障原因的有效識別,滿足工程實際要求。
[Abstract]:Transmission line is an important part of the power network. It covers a wide area and runs in a harsh and complex environment, so it is easy to be caused by natural disasters. The failure of HVAC transmission line, which is the backbone of the provincial power network, will inevitably impact the power network and threaten the safe and stable operation of the power network. The rapid action of relay protection device may lead to the failure line damage trace is not obvious, increase the trouble degree of fault maintenance. After the line tripping, accurate, The possible cause of fault can be judged in time by combining location of location and local terrain, which can guide the inspection line, find fault point quickly and accurately, reduce the time of troubleshooting, improve the reliability of power supply and operation stability of power system. It has great economic and social benefits. At present, there are few researches on identification at home and abroad, most of them are statistics and prevention measures for certain faults, and there is no systematic identification method. In this paper, the common lightning strike and wind deviation of transmission lines are discussed. The single-phase fault of transmission line caused by bird flicker, pollution flashover, tree flash and hill fire is studied. Based on the analysis of fault mechanism of each fault type, the fault characteristic law is deeply analyzed. Combined with historical experience, the external and internal characteristics of the fault are summarized. The external features include the weather, season and time characteristics corresponding to the occurrence of the fault, while the internal features refer to the fault reclosing situation, which reflects the nature of the fault. The DC content of the fault phase current, the third harmonic content, the distortion after 00:00 and the size and properties of the transition resistance are verified by the processing and analysis of the actual fault recording data. In this paper, Fisher score based feature mining method is used to calculate and sort the importance of each feature to identify six fault models, so as to find out the main factors affecting the identification of each fault cause type. In view of the incomplete fault samples of transmission lines, support vector machine (SVM), which has strong generalization ability, is used to classify and identify the fault causes. The algorithm aims at minimizing structural risk and avoids the problem of overlearning to a certain extent. PSO is used to optimize the parameters of each model. In the stage of prediction, the fault samples under test are analyzed and brought into the six fault models respectively, and the probability corresponding to various fault types is obtained. Taking the fault cause type corresponding to the maximum value of the algorithm as the discriminant result and the verification test, the problem of unbalance based on the sample data and the improvement of the self-learning ability of the algorithm are proposed. Finally, the test results show that, The identification effect of SVM algorithm based on PSO optimization is improved, and by selecting the feature of feature importance ranking, we can simplify the calculation of the model and improve the efficiency while ensuring the accuracy of identification. Comprehensive analysis and research on the faults of six transmission lines, bird flash, pollution flashover, tree flash and mountain fire, In this paper, a method of self-learning identification of PSO-SVM fault cause based on external and internal fault features is proposed. The method is based on the mining and analysis of actual fault data, and the theoretical basis is sufficient. At the same time, the fault data information used in this method is easy to obtain, and can be extended in combination with the local fault law, which can effectively identify the common fault causes and meet the practical engineering requirements.
【學(xué)位授予單位】：山東大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TM755

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