[1]陳 太,鄭作霖,婁堅鑫,等.220 kV變壓器油中溶解氣體體積分數動態預警方法[J].高壓電器,2019,55(08):164-170.[doi:10.13296/j.1001-1609.hva.2019.08.023]
             CHEN Tai,ZHENG Zuolin,LOU Jianxin,et al.Dynamic Early-warning Method Based on Dissolved Gas in Oil of 220 kV Transformer[J].High Voltage Apparatus,2019,55(08):164-170.[doi:10.13296/j.1001-1609.hva.2019.08.023]
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            220 kV變壓器油中溶解氣體體積分數動態預警方法()
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            《高壓電器》[ISSN:1001-1609/CN:61-11271/TM]

            卷:
            第55卷
            期數:
            2019年08期
            頁碼:
            164-170
            欄目:
            研究與分析
            出版日期:
            2019-08-15

            文章信息/Info

            Title:
            Dynamic Early-warning Method Based on Dissolved Gas in Oil of 220 kV Transformer
            作者:
            陳 太1 鄭作霖1 婁堅鑫1 康 铮2 吳志偉2 鄭躍勝2 舒勝文2
            (1. 福建和盛高科技産業有限公司, 福州 350003; 2. 福州大學電氣工程與自動化學院, 福州 350108)
            Author(s):
            CHEN Tai1 ZHENG Zuolin1 LOU Jianxin1 KANG Zheng2 WU Zhiwei2 ZHENG Yuesheng2 SHU Shengwen2
            (1. Fujian Hoshing Hi-Tech Industrial Co.,Ltd., Fuzhou 350003, China; 2. College of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350108, China)
            關鍵詞:
            變壓器 油中溶解氣體 動態阈值 分布模型 在線監測
            Keywords:
            transformer dissolved gas in oil dynamic threshold distribution model online monitoring
            DOI:
            10.13296/j.1001-1609.hva.2019.08.023
            摘要:
            油中溶解氣體分析是監測電力變壓器運行狀態最常用的方法之一。針對現有標准對油中 溶解氣體體積分數預警阈值設定單一和裕度過大等不足,提出了一種基于數據統計和分布模型 的220 kV變壓器油中溶解氣體體積分數動態預警方法。首先,對某電網近10萬條220 kV變壓器 油中溶解氣體體積分數數據進行統計與分類處理,並建立了油中溶解氣體體積分數的分布模型 ;然後,根據油中溶解氣體體積分數實際預警數據統計結果,通過分布模型的逆累積運算計算 得到動態預警值,從而實現了一種油中溶解氣體體積分數的動態預警方法;最後,將動態預警 方法與現有標准以及10%劃分規則的計算結果進行了對比分析,並討論了預警阈值的動態特性。 結果表明,該方法與現有標准相比能夠降低漏預警率,與10%劃分規則相比能夠降低誤預警率, 即在漏報警和誤報警之間取得了一個較好的平衡點,具有一定的工程應用價值。
            Abstract:
            Dissolved gas analysis in oil is one of the most common methods for monitoring the operating state of power transformers. The existing standard set the early-warning threshold for volume fraction of dissolved gas in oil as a fixed and too large value. In response to this problem, a dynamic early-warning method based on data statistics and distribution model is proposed in this paper. Firstly, nearly 100,000 volume fraction data of dissolved gas in oil for 220 kV transformer in a certain power grid is statistically and classified, and the volume fraction distribution model of dissolved gas in oil is established. Then, based on the actual volume fraction early-warning data of dissolved gas in oil, the dynamic early-warning value is calculated by the inverse accumulation operation of distribution model. Thus, a dynamic early-warning method for the volume fraction of dissolved gas in oil has been realized. Finally, the dynamic early-warning method is compared with the existing standard and the calculation results of the 10% partition principle and the dynamic characteristics of early-warning threshold is discussed. The results show that this method can reduce missed alarms compared with the existing standard, also can reduce false alarms compared with the 10% partition principle. In other words, a good balance between missed alarm and false alarm has been achieved, which has a certain value of engineering application.

            參考文獻/References:

            [1] 孫才新,陳偉根,李 儉. 電氣設備油中氣體在線監測與故障診斷技術[M]. 北京:科學出版社,2003. SUN Caixin,CHEN Weigen,LI Jian. On-line monitoring and fault diagnosis technology for gas in electric equipment oil[M]. Beijing:Science Press,2003.
            [2] 電力設備預防性試驗規程:DL/T 596—1996[S].1996. Preventive test code for electric power equipment:DL/T 596—1996[S].1996.
            [3] MACKENZIE E A,CROSSEY J,DE PABLO A,et al. On-line monitoring and diagnostics for power transformers[C]//IEEE International symposium on Electrical Insulation. San Diego,USA:IEEE,2010:1-5.
            [4] 許 坤,周建華,茹秋實,等. 變壓器油中溶解氣體在線監測技術發展與展望[J]. 高電壓技術,2005,31(8):30-32. XU Kun,ZHOU Jianhua,RU Qiushi,et al. Development and prospect of transformer oil dissolved gas on-line monitoring technology[J]. High Voltage Engineering,2005,31(8):30-32.
            [5] 操敦奎. 變壓器油色譜分析與故障診斷[M]. 北京:中國電力出版社,2010. CAO Dunkui. Transformer oil chromatographic analysis and fault diagnosis[M]. Beijing:China Electric Power Press,2010.
            [6] 變電設備在線監測裝置技術規範 第2部分:變壓器油中溶解氣體在線監測裝置:DL/T 1498. 2—2016[S].2016. Technical specification for on-line monitoring device of transformation equipment-Part 2:On-line monitoring device of gases dissolved in transformer oil:DL/T 1498. 2—2016[S].2016.
            [7] 變電設備在線監測裝置檢驗規範 第2部分:變壓器油中溶解氣體在線監測裝置:DL/T 1432. 2—2016[S].2016. Testing specification for on-line monitoring device of transformation equipment-Part 2:On-line monitoring device of gases dissolved in transformer oil:DL/T 1432. 2—2016[S].2016.
            [8] 變壓器油中溶解氣體分析和判斷導則:GB/T 7252—2001[S].2001. Guide to the analysis and the diagnosis of gases dissolved in transformer oil:GB/T 7252—2001[S].2001.
            [9] 宋安琪. 基于現場油色譜數據的變壓器診斷阈值的研究[D]. 北京:華北電力大學,2013. SONG Anqi. Diagnostic threshold of transformers:A study base on on-site dissolved gases[D]. Beijing:North China Electric Power University,2013.
            [10] MOSINSKI F,PIOTROWSKI T. New statistical methods for evaluation of DGA data[J]. IEEE Transactions on Dielectrics and Electrical Insulation,2003,10(2):260-265.
            [11] 高樹國,王學磊,李慶民,等. 基于MCD穩健統計分析的變壓器油色譜異常值檢測及分布特性[J]. 高電壓技術,2014,40(11):3477-3482. GAO Shuguo,WANG Xuelei,LI Qingmin,et al. Outliers detection and distribution characteristics of the transformer DGA data based on MCD robust statistics[J]. High Voltage Engineering,2014,40(11):3477-3482.
            [12] 鄭一鳴,孫淑蓮,孫 翔,等. 基于自適應小波分析的在線油色譜數據預處理方法[J]. 浙江電力,2016,35(11):1-6. ZHENG Yiming,SUN Shulian,SUN Xiang,et al. Preprocessing method of online oil chromatographic data based on adaptive wavelet analysis[J]. Zhejiang Electric Power,2016,35(11):1-6.
            [13] 章 彬,李 穆,呂啓深,等. 基于數理統計的變壓器油色譜聯合預警策略[J]. 高壓電器,2016,52(5):186-191. ZHANG Bin,LI Mu,LYU Qishen,et al. Joint early-warning strategies of transformer oil chromatogram based on mathematical statistic[J]. High Voltage Apparatus,2016,52(5):186-191.
            [14] 張少涵,應宗明. 在線監測系統數據替代預防性試驗數據分析[C]//全國輸變電設備狀態檢修技術交流研討會. 西安:[s. n. ],2009,95-100. ZHANG Shaohan,YING Zongming. Study on replacing preventive test data with online monitoring system data[C]//Power Transmission and Equipment Status Maintenance Technical Seminar. Xi’an:[s. n. ],2009,95-100.
            [15] 葛余博. 概率論與數理統計[M]. 北京:清華大學出版社,2005. GE Yubo. Probability theory and mathematical statistics[M]. Beijing:Tsinghua University Press,2005.
            [16] 茅群霞. 缺失值處理統計方法的模擬比較研究及應用[D]. 成都:四川大學,2005. MAO Qunxia. A simulated comparative study and application of statistical methods in datasets with missing values[D]. Chengdu:Sichuan University,2005.
            [17] 李慶揚. 數值分析基礎教程[M]. 北京:高等教育出版社,2001. LI Qingyang. Numerical analysis basic tutorial[M]. Beijing:Higher Education Press,2001.
            [18] 虞 鴻,李 波,蔣裕豐. 基于威布爾分布的大壩變形監控指標研究[J]. 水力發電,2009,35(6):90-93. YU Hong,LI Bo,JIANG Yufeng. Study on monitoring indexes of dam deformation based on Weibull distribution[J]. Water Power,2009,35(6):90-93.
            [19] 張 黎. 電氣設備壽命分布分析[D]. 濟南:山東大學,2005. ZHANG Li. Analysis of life distribution of electrical equipment[D]. Jinan:Shandong University,2005.
            [20] Mineral oil-impregnated electrical equipment in service—guide to the interpretation of dissolved and free gases analysis:IEC 60599:2015[S]. 2015.
            [21] SOKOLOV V,BASSETTO A,MAK J,et al. Transformer risk assessment considerations[C]//Proceedings of the Euro.Tech. 2002 Conference. Bimingham,UK:[s. n. ],2002:1-18.
            [22] 梁博淵,劉 偉,楊欣桐. 變壓器健康狀況評估與剩余壽命預測[J]. 電網與清潔能源,2010,26(11):37-43. LIANG Boyuan,LIU Wei,YANG Xintong. Transformer condition assessment and residual life prediction[J]. Advances of Power System & Hydroelectric Engineering,2010,26(11):37-43.
            [23] 李 珊,歐世鋒,李克文,等. 基于浴盆曲線的配電變壓器故障率模型[J]. 信息技術,2018(3):83-88. LI Shan,OU Shifeng,LI Kewen,et al. Fault rate model of distribution transformer based on bathtub curve[J]. Information Technology,2018(3):83-88.

            備注/Memo

            備注/Memo:
            陳 太(1982—),男,高級工程師,主要研究方向爲電力狀態監測及電力智能化技術。 鄭作霖(1978—),男,算法工程師,主要研究方向爲人工智能、大數據和智能電網軟件技術。 婁堅鑫(1973—),男,高級工程師,主要研究方向爲輸變電設備在線監測技術和變電站智能輔助監控技術。 康 铮(1993—),男,碩士研究生,主要研究方向爲高電壓與絕緣技術。 吳志偉(1995—),男,碩士研究生,主要研究方向爲輸電線路防雷評估。 鄭躍勝(1981—),男,博士,教授,主要研究方向爲高電壓試驗技術、氣固複合絕緣技術和電磁場數值計算及應用。 舒勝文(1987—),男,博士,高級工程師,主要研究方向爲高電壓新技術、輸變電設備狀態檢測與防災減災(通信作者)。收稿日期:2019-01-06; 修回日期:2019-03-17
            更新日期/Last Update: 2019-08-16