INCREASING THE SPEED OF THE CONTROL SYSTEM OF THE THYRISTOR COMPENSATOR OF REACTIVE POWER OF ELECTRIC ARC FURNACES
Abstract
DOI: https://doi.org/10.26565/2079-1747-2025-35-03
Electric arc furnaces (EDP) contain furnace transformers with a power factor of 0.3, which randomly consume reactive power in the form of jumps when non-sinusoidal currents are present. Thyristor reactive power compensators (TRPC) are used to compensate for the latter. When reactive power jumps occur, the known TCRP control system provides a regulation time of 0.02 s, since it determines the control angle of the thyristors for the last period of the mains voltage. At the same time, during the transient processes of TCRP regulation, the reactive power of the EAF is not compensated.
The aim of the work is to increase the speed of the TPRC control system in order to reduce uncompensated reactive power in transient control processes. The goal is achieved by the fact that, unlike the known control system, the control angle is determined every half-period by solving the equation of minimizing the functional of the root mean square error of the equality of the reactor current and the transformer current with a capacitor at the moments of the amplitude value of the mains voltage. As a result, the speed of the control system increases to 0.01 s.
The scientific novelty of the work lies in the further development of the expression of the functional for determining the control angle of the TCRP at each half-period of voltage. Practical significance: doubling the speed of the TCRP control system reduces reactive power consumption in transient control processes by half and increases the resulting power factor to unity. The performance of the proposed TCRP control system has been verified on a mathematical model
In cites: Kovalov V., Khomiak E., Miroshnyk Ye., Tymofieiev О., Krutko V., Shevchenko V., (2025). Statistical methods for quality control of small-batch machining. Engineering, (35), 26-35.
https://doi.org/10.26565/2079-1747-2025-35-03 ( in Ukraine)
Downloads
References
Danylchenko, DO & Kuznetsov, DS 2024, ‘Vykorystannia prystroiv kompensatsii reaktyvnoi potuzhnosti pry vprovadzhenni rozpodilenoi heneratsii’ [Use of reactive power compensation devices when implementing distributed generation. ], Visnyk Natsionalnoho tekhnichnoho universytetu "KhPI". Seriia: Enerhetyka: nadiinist ta enerhoefektyvnist, no 1(8), Pp. 24–31. DOI: https://doi.org/10.20998/2224-0349.2024.01.08. ( in Ukraine)
Chyzhenko, OI & Trach, IV 2020, ‘Analiz vplyvu parametriv merezhi na efektyvnist tyrystornoho kompensatora reaktyvnoi potuzhnosti’ [Analysis of the influence of network parameters on the efficiency of the thyristor reactive power compensator. ], Elektrotekhnika i elektromekhanika, Iss. 6, Pp. 45–50. doi: 10.20998/2074-272X.2020.6.07. ( in Ukraine)
Hovorov, FP & Perepechenyi, VA 2022, ‘Pytannia kompensatsii reaktyvnoi potuzhnosti v miskykh elektromerezhakh’ [The issue of reactive power compensation in urban power grids.], Enerhetyka ta elektryfikatsiia, № 3(47), |Pp. 50–56. doi: 10.31548/energiya2022.03.050. ( in Ukraine)
Domanskyi, IV 2021, ‘Rezhymy roboty system elektrozhyvlennia z prystroiamy kompensatsii reaktyvnoi potuzhnosti [Operating modes of the power supply system with reactive power compensation devices.], Elektrotekhnika i elektromekhanika, № 3, Pp. 59–66. doi: 10.20998/2074-272X.2021.3.09. ( in Ukraine)
Kalinchyk, VV & Pobihailo, VA 2021, ‘Upravlinnia rezhymom reaktyvnoi potuzhnosti v systemakh elektropostachannia’ [Reactive power control in power supply systems.], Visnyk Natsionalnoho tekhnichnoho universytetu «KhPI». Seriia: Problemy udoskonaliuvannia elektrychnykh mashyn i aparativ. Teoriia i praktyka, № 2(6), Pp. 36–39. doi: 10.20998/2079-3944.2021.2.07. ( in Ukraine)
Pylypenko, OV & Shevchenko, VV 2022, ‘Analiz efektyvnosti tyrystornykh rehuliatoriv u systemakh elektroduhovykh pechei’ [Analysis of the efficiency of thyristor regulators in electric oven systems.], Elektrotekhnika i elektromekhanika, № 4, Pp. 38–44. doi: 10.20998/2074-272X.2022.4.06. ( in Ukraine)
Minenerho Ukrainy 2020, Metodyka obchmsdennia platy za peretikannia reaktyvnoi elektroenerhii : nakaz Minenerho Ukrainy vid 30.11.2020 № 764 [Methodology for limiting the payment for the flow of reactive electricity: Order of the Ministry of Energy of Ukraine dated 11/30/2020 No. 764], viewed June 23, 2025, https://zakon.rada.gov.ua/laws/show/z0109-21#Text ( in Ukraine)
Takata, S, Kirnura, F, F.J.A.M. van Houten, Westkamper, E, Shpitalni, M, Ceglarek, D & Lee, J 2004, ‘Maintenance: Changing Role in Life Cycle Management’, CIRP Annals, № 53 (2), P. 643–655. DOI: https://doi.org/10.1016/s0007-8506(07)60033-x.
Kupriyanov, O, Trishch, R, Dichev, D & Bondarenko, T 2022, ‘Mathematic Model of the General Approach to Tolerance Control in Quality Assessment’, Advanced Manufacturing Processes III. InterPartner 2021. Lecture Notes in Mechanical Engineering, P. 415–423. DOI: https://doi.org/10.1007/978-3-030-91327-4_41
Dichev, D, Diakov, D, Zhelezarov, Y, Nikolova, H, Kupriyanov, O & Dicheva, R 2022, ‘Accuracy evaluation of flat surfaces measurements in conditions of external influences’, Proceedings of 2022 XXXII international scientific symposium “Metrology and Metrology assurance” (MMA). (September 2022), P. 1-7. DOI: 10.1109/MMA55579.2022.9992334
Oztemel, E & Gursev, S 2020, ‘Literature review of Industry 4.0 and related technologies’, Journal of Intelligent Manufacturing, № 31, P. 127–182. DOI: https://doi.org/10.1007/s10845-018-1433-8
Dhobale, N, Mulik, S, Jegadeeshwaran, R & Patange, A 2021, ‘Supervision of Milling Tool Inserts using Conventional and Artificial Intelligence Approach: A Review’, Sound & Vibration, № 55(2), P. 87–116. DOI: https://doi.org/10.32604/sv.2021.014224
Nasir, V & Sassani, F 2021, ‘A review on deep learning in machining and tool monitoring: Methods, opportunities, and challenges’, The International Journal of Advanced Manufacturing Technology, № 115, P. 2683–2709. DOI: https://doi.org/10.1007/s00170-021-07325-7
Ko, JH & Yin, C 2025, ‘A review of artificial intelligence application for machining surface quality prediction: From key factors to model development’, Journal of Intelligent Manufacturing. DOI: https://doi.org/10.1007/s10845-025-02571-y
Kasiviswanathan, S, Gnanasekaran, S, Thangamuthu, M & Rakkiyannan, J 2024, ‘Machine-Learning- and Internet- of-Things-Driven Techniques for Monitoring Tool Wear in Machining Process: A Comprehensive Review’, Journal of Sensor and Actuator Networks, № 13(5):53. DOI: https://doi.org/10.3390/jsan13050053.
Mohanraj, T, Shankar, S, Rajasekar, R, Sakthivel, NR & Pramanik, A 2020, ‘Tool condition monitoring techniques in milling process-a review’, Journal of Materials Research and Technology, № 9(1), P. 1032–1042. DOI: https://doi.org/10.1016/j.jmrt.2019.10.031
Rong, K, Ding, H, Kong, X, Huang, R & Tang. J 2021, ‘Digital twin modeling for loaded contact pattern-based grinding of spiral bevel gears’, Advanced Engineering Informatics, № 49. 101305. DOI: https://doi.org/10.1016/j.aei.2021.101305
Topchii, NV 2024, ‘Metody ta suchasni zasoby kontroliu mekhanichnykh detalei na vyrobnytstvi’, Vcheni zapysky TNU imeni V.I. Vernadskoho. Seriia: Tekhnichni nauky, Vol. 35 (74), № 6, Pp. 30-34. DOI: https://doi.org/10.32782/2663-5941/2024.6.1/06 ( in Ukraine)
Kalchenko, V, Venzheha, V, Pasov, H, Kolohoida, A, Kuzhelnyi, Ya & Bohoslavskyi, V 2024, ‘Pidvyshchennia yakosti kontroliu parametriv detalei pry vyhotovlenni ta remonti avtomobiliv’, Tekhnichni nauky ta tekhnolohii, № 1(35), Pp. 9-17. DOI: https://doi.org/10.25140/2411-5363-2024-1(35)-9-17 ( in Ukraine)
Vasilevskyi, O 2021, ‘Assessing the level of confidence for expressing extended uncertainty through control errors on the example of a model of a means of measuring ion activity’, Acta IMEKO, № 10 (2), P. 199-203. DOI: https://doi.org/10.21014/acta_imeko.v10i2.810
Trishch, RM, Cherniak, OM, Artiukh, SM, Burdeina, VM & Hrinchenko, HS 2021, ‘Implementatsiia vymoh mizhnarodnykh standartiv shchodo nevyznachenosti vymiriuvan v metrolohichnu diialnist pidpryiemstv’ [Implementation of the requirements of international standards regarding measurement uncertainty in the metrological activities of enterprises. ], Mashynobuduvannia, № 27, Pp. 117–124. DOI: https://doi.org/10.32820/2079-1747-2021-27-117-124 ( in Ukraine)
Sankhye, S & Hu, G 2020, ‘Machine Learning Methods for Quality Prediction in Production’, Logistics, № 4(4), Rr. 35. DOI: https://doi.org/10.3390/logistics4040035
Haq, A & Munir, W 2018, ‘Improved CUSUM charts for monitoring process mean’, Journal of Statistical Computation and Simulation, № 88 (9), P. 1684–1701. DOI: https://doi.org/10.1080/00949655.2018.1444040
Imran, M, Sun, J, Zaidi, FS, Abbas, Z & Nazir, HZ n.d., Multivariate cumulative sum control chart for compositional data with known and estimated process parameters, viewed June 23, 2025, https://onlinelibrary.wiley.com/doi/abs/10.1002/qre.3099 ( in Ukraine)
Montanari, G, & Loggini, M, Cavallini, A et al 1994, ‘Arc-Furnace Model for the Study of Flicker Compensation in Electrical Networks’, IEEE Transactions on Power Delivery, Vol. 9, № 4, P. 2026-2033.
Chyzhenko, OY & Trach, YV 2017, ‘Sposob povishenyia kachestva toka vsysteme «set – upravliaemii mostovoi tyrystornyi kompensator reaktyvnoi moshchnosty» [A method for improving current quality in the system "set - control of a bridge thyristor reactive power compensator". ], Pratsi IED NANU, Iss. 46, Pp. 22-30. ( in Ukraine)
Litkovets, SP & Pietukhov, MV 2014, ‘Sposib pidvyshchennia enerhetychnoi efektyvnosti statychnykh tyrystornykh kompensatoriv reaktyvnoi potuzhnosti z prymusovoiu komutatsiieiu’ [Method for increasing the energy efficiency of static thyristor reactive power compensators with forced switching. ], Elektromekhanichni i enerhozberihaiuchi systemy, Iss. 2, Pp. 56-62.
Fusco, G, Losi, A & Russo, M 2001, ‘Adaptive Voltage Regulator Design for Static VAR Systems’, Control Engineering practice, No. 9.
