Determination of the technical condition of auxiliary transformers when building a monitoring system for a digital traction substation
Keywords:
тяговая подстанция, трансформатор собственных нужд, техническое состояние трансформатора, прогнозный ресурс работы, диагностические параметры, нормативные требования, цифровая тяговая подстанцияAbstract
The article discusses aspects of determining the technical condition of auxiliary transformers when creating a system of monitoring the technical condition of digital traction substation equipment. The technical condition of power equipment is influenced by climatic, operational and technological factors. Modern systems for monitoring the technical condition of power equipment based on measurements on operating equipment ensure its stable operation, signal critical deviations in performance indicators, and evaluate the predicted service life. While making diagnostic measurements of auxiliary transformers, it is proposed to measure the following performance indicators and parameters: load and voltage level (on the HV side); temperature of the upper oil layers; oil level; tank noise and vibration; oil moisture content; the appearance of the main components (integrity check). Analysis of measurement data in determining the technical condition of auxiliary transformers includes a retrospective analysis of changes in test data during maintenance, measurement data in operating mode, analysis of the level and speed of changes of diagnostic data.Technical condition of transformers is assessed and forecasted within the framework of the proposed expert system, which takes into account the data of tests and measurements of diagnostic data and electrical quantities. The considered system of monitoring the technical condition of auxiliary transformers of traction substations is a part of a single equipment condition monitoring system, the construction of which is associated with the project of a digital traction substation. The proposed solutions correspond to the concept of switching to a maintenance system based on the current state, improving the reliability of equipment operation and preventing emergency events.
References
Коробко Г.И., Лебедев В.В. Автоматизированные электроприводы объектов водного транспорта. Нижний 1. Application of HFCT and UHF Sensors in On-Line Partial Discharge Measurements for Insulation Diagnosis of High Voltage Equipment / F. Álvarez, F.Garnacho, J.Ortego et al. // Sensors. 2015. 15(4), 7360–7387. DOI: 10.3390/s150407360.
Bakar N., Abu-Siada A., Islam S. A review of dissolved gas analysis measurement and interpretation techniques // IEEE Electrical Insulation Magazine. 2014. 30(3). Р. 39–49. DOI:10.1109/mei.2014.6804740.
Experience with capacitive on-line sensors for moisture evaluation in transformer insulation / I. Atanasova-Höhlein, M. Končan-Gradnik, T. Gradnik et al. // IEEE Electrical Insulation Magazine. 2019. 35(2). Р. 18–26. DOI:10.1109/mei.2019.8636102.
Fundamental concepts of using water activity probes to assess transformer insulation water content / D. Martin, T. Saha, C. Perkasa // IEEE Electrical In-sulation Magazine. 2016. 32(3). Р. 9–16. DOI: 10.1109/mei.2016.7527120.
Modeling of the Winding Hot-Spot Temperature in Power Transformers / М. Kunicki, S. Borucki, А. Cichoń et al. // Case Study of the Low-Loaded Fleet. Energies. 2019. 12(18). 3561. DOI: 10.3390/en12183561.
Vibration characteristic investigation on distribution transformer influenced by DC magnetic bias based on motion transmission model / X. Liu, Y. Yang, Y. Huang et al. // International Journal of Electrical Power & Energy Systems. 2018. № 98. 389–398. DOI:10.1016/j.ijepes.2017.12.032.
Design and implementation of the monitoring and control systems for dis-tribution transformer by using GSM network / M. Jalilian, H. Sariri, F. Parandin et al. // International Journal of Electrical Power & Energy Systems. 2016. № 74. 36–41. DOI:10.1016/j.ijepes.2015.07.022.
Transformer fault diagnosis method using IoT based monitoring system and ensemble machine learning / C. Zhang, Y. He, B. Du et al. // Future Generation Computer Systems. 2020. DOI:10.1016/j.future.2020.03.008.
A Primer on 3GPP Narrowband Internet of Things / Y.-P. Wang E., X. Lin, A. Adhikary et al. // IEEE Communications Magazine. 2017. 55(3). 117–123. DOI:10.1109/mcom.2017.1600510cm.
A Smart IoT Based System for Monitoring and Controlling the Sub-Station Equipment / M.S. Hossain, M. Rahman, M.T. Sarker et al. // Internet of Things. 2019. 100085. DOI:10.1016/j.iot.2019.100085.
Al Mhdawi A.K., Al-Raweshidy H.S. A Smart Optimization of Fault Diagnosis in Electrical Grid Using Distributed Software-Defined IoT Sys-tem // IEEE Systems Journal. 2019. 1–11. DOI:10.1109/jsyst.2019.2921867.
A Probabilis-tic Approach for Forecasting the Allowable Current of Oil-Immersed Trans-formers / A. Bracale, G. Carpinelli, M. Pagano et al. // IEEE Transactions on Power Delivery. 2018. 33(4). 1825–1834. DOI:10.1109/tpwrd.2018.2791181.
Sun C., Ohodnicki P.R., Stewart E.M. Chemical Sensing Strat-egies for Real-Time Monitoring of Transformer Oil : а Review //IEEE Sensors Journal. 2017. 17(18). 5786–5806. DOI:10.1109/jsen.2017.2735193.
Stating Diagnosis of Current State of Electric Furnace Transformer on the Basis of Analysis of Partial Discharges / O.I. Karandaeva, I.A. Yakimov, A.A. Filimonova et al. // Machines. 2019. 7(4). 77. DOI: 10.3390/machines7040077.
Hekmati A., Hekmati R. Optimum acoustic sensor placement for partial discharge allocation in transformers // IET Science, Measurement & Technology. 2017. 11(5). 581–589. DOI:10.1049/iet-smt.2016.0417.
Diagnostic Measurements for Power Transformers / S. Tenbohlen, S. Coenen, M. Djamali // Energies. 2016. 9(5). 347. DOI:10.3390/en9050347.
Rahman M. S. A., Lewin P.L., Rapisarda P. Autonomous localization of partial discharge sources within large transformer windings // IEEE Transactions on Dielectrics and Electrical Insulation. 2016. 23(2), 1088–1098. DOI:10.1109/tdei.2015.005070.
Djamali M., Tenbohlen S. A validated online algorithm for detec-tion of fan failures in oil-immersed power transformers. International Journal of Thermal Sciences. 2017. 116. 224–233. DOI:10.1016/j.ijthermalsci.2017.02.012.
Hernandez M.P.C., Labib A. Selecting a condition monitor-ing system for enhancing effectiveness of power transformer maintenance // Journal of Quality in Maintenance Engineering. 2017. 23(4). 400–414. DOI: 10.1108/jqme-07-2015-0027.
In-creased operation reliability of HV apparatus through PD monitoring / W. Koltunowicz, L.-V. Badicu, U. Broniecki et al. // IEEE Transactions on Dielectrics and Electrical Insulation. 2016. 23(3). 1347–1354. DOI:10.1109/tdei.2015.005579.
