Journal of Shandong University(Engineering Science) ›› 2021, Vol. 51 ›› Issue (4): 124-130.doi: 10.6040/j.issn.1672-3961.0.2020.538

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Prediction of converter valve cooling capacity and water inlet temperature based on LSTM

LIAO Yi1, LUO Wei2, JIANG Fengwei1, LI Yajin3, YU Dayang3*   

  1. 1. Guangzhou Bureau, China Southern Power Grid EHV Power Transmission Co., Ltd., Guangzhou 510405, Guangdong, China;
    2. China Southern Power Grid Co., Ltd., Guangzhou 510623, Guangdong, China;
    3. School of Electrical Engineering, Shandong University, Jinan 250061, Shandong, China
  • Published:2021-08-18

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Abstract: In order to solve the problems of lacking of intelligent prediction means for the condition of converter valve cooling system and the difficulty of evaluating the cooling capacity under extreme conditions, a prediction method of cooling margin of converter valve based on LSTM(long short-term memory,LSTM)was proposed. On the basis of quantitative evaluation of cooling margin index of valve cooling system, the long-term and long-term memory network was used to establish the prediction model. Considering the multi-source influencing factors, the feature quantity was selected through the correlation strength and the data sample set was constructed, which was used to train the model. Training was carried out to predict the development trend of water inlet temperature and cooling margin.The analysis model of cooling margin under extreme conditions was provided,which provided the basis for on-site treatment decision. The effectiveness and feasibility of the algorithm were analyzed and verified by an example of converter station.

Key words: valve cooling system, LSTM algorithm, water inlet temperature, cooling capacity, extreme conditions

CLC Number: 

  • TM721.1
[1] 洪乐洲,江一,翁洪志,等.换流站阀冷系统设计缺陷与改进措施[J].南方电网技术,2013,7(1):44-46. HONG Lezhou, JIANG Yi, WENG Hongzhi, et al. Design flaws andcorrective measures of valve cooling system at converter station[J]. Southern Power System Technology, 2013, 7(1):44-46.
[2] 毕文瑾,李亚龙.柔性直流换流阀控制系统运行安全性研究[J].电测与仪表,2019,56(17):26-30. BI Wenjin, LI Yalong. Research on operation safety of flexible DC converter valve control system[J].Electrical Measurement & Instrumentation, 2019, 56(17):26-30.
[3] 赵明,洪乐洲,严伟.换流站阀冷外冷水系统散热能力评估方法优化与应用[J].电工技术,2020(13):134-137. ZHAO Ming, HONG Lezhou, YAN Wei. Optimization and application of evaluation method for cooling capacity of external value cooling system in converter station[J].Electric Engineering, 2020(13):134-137.
[4] 廖毅,罗炜,蒋峰伟,等.换流阀冷却能力多维度分析与预警模型[J].南方电网技术,2020,14(7):1-9. LIAO Yi, LUO Wei, JIANG Fengwei, et al. Multi-dimensional analysis and early warning model for the cooling capacity of converter valve[J] Southern Power System Technology, 2020, 14(7):1-9.
[5] 陈强华. 基于多维时序数据的运维优化技术研究[D].济南:山东大学,2020. CHEN Qianghua. Research on operation and maintenance optimization technology based on multidimensional time series data[D]. Jinan: Shandong University, 2020.
[6] 彭红霞,文艳,王磊,等.基于两层知识架构的电力设备差异化运维技术[J].高压电器,2019,55(7):221-226. PENG Hongxia, WEN Yan, WANG Lei, et al.Differentialoperating maintenance technology of power equipment based on two-layer knowledgearchitecture[J].High Voltage Apparatus, 2019, 55(7):221-226.
[7] 蒋峰伟,廖毅,张思光.高压直流换流阀冷却系统冷却能力评估方法和提升措施研究[J].机电信息,2019(33):16-18. JIANG Fengwei, LIAO Yi, ZHANG Siguang. Research on evaluation method and lifting measures of cooling capacity of HVDC converter valve cooling system[J]. Mechanical and Electrical Information, 2019(33):16-18.
[8] 蒲天骄,乔骥,韩笑,等.人工智能技术在电力设备运维检修中的研究及应用[J]. 高电压技术,2020,46(2): 369-383. PU Tianjiao, QIAO Ji, HAN Xiao, et al. Research andapplication of artificial intelligence in operation and maintenance for power equipment[J]. High Voltage Engineering, 2020, 46(2):369-383.
[9] 王宏伟,张利民,姜建平,等.特高压站避雷器泄漏电流在线监测和分析系统[J].电瓷避雷器,2019(6):67-72. WANG Hongwei, ZHANG Limin, JIANGJianping, et al. On-line monitoring and analysis system of lightning arrester leakage current in UHV substation[J]. Insulators and Surge Arresters, 2019(6):67-72.
[10] 尹金良,朱永利.支持向量机参数优化及其在变压器故障诊断中的应用[J].电测与仪表,2012,49(5):11-16. YIN Jinliang, ZHU Yongli. Parameteroptimization for support vector machine and its application to fault diagnosis of power transformers[J].Electrical Measurement & Instrumentation, 2012, 49(5):11-16.
[11] SONG H, DAI J J, LUO L G, et al. Power transformer operating state prediction method based on an LSTM network[J]. Energies, 2018, 11(4): 914-928.
[12] 黄梦辉,蒋涛,董建军,等.基于LSTM的箱式变压器高压套管温度预测[J/OL].电测与仪表. [2020-12-17].http://58.194.172.13:80/rwt/CNKI/http/NNYHGLUDN3W-XTLUPMW4A/kcms/detail/23.1202.th.20201116.1707.002.html.
[13] 3HOCHREITER S, SCHMIDHUBER J. Long short- term memory[J]. Neural Computation, 1997, 9(8):1735-1780.
[14] 周虎兵,王玉磊,张曼,等.熵值法赋权的智能变电站二次设备效能模糊综合评价[J].电测与仪表,2018,55(3):73-79. ZHOU Hubing, WANG Yulei, ZHANG Man, et al. Comprehensive fuzzy evaluation of effectiveness of secondary equipment in smart substation based on entropy method[J]. Electrical Measurement & Instrumentation, 2018, 55(3):73-79.
[15] SZEKELY G J, RIZZO M L, BAKIROV N K. Measuring and testing dependence by correlation of distances[J]. Annals of Statistics, 2007, 35(6):2769-2794.
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