Neural Network Hardware and Software Real Time Control of Electrical Signals Phase Shift
DOI:
https://doi.org/10.22213/2413-1172-2023-2-76-84Keywords:
relay protection, emergency modes, signal processing, microprocessor, microcontroller, artificial neural networkAbstract
Artificial neural networks are increasingly being used in the intelligent electric power industry. Smart grids are the key components of digital economy. The work purpose is to show the possibility of using a simple architecture neural network in the appropriate microprocessor engineering for improvement such microprocessor device characteristics as reducing the device reaction time, increasing the decision-making accuracy in the accident and the ability to more accurate localization of the accident site. This reduces the negative accident consequences, time of determination of the accident location and, accordingly, the time to eliminate the accident consequences, and to restore the normal power system operation. Neural network training is a long-lasting process. At the same time, neural network “deep learning” does not guarantee their default-free operation. So, it is proposed to introduce a pre-trained neural network into intelligent electronic devices when electrical signals can be described by analytical formulas and the possible variation ranges of such signal parameters are set in advance. The corresponding approach has been implemented and tested in a microprocessor device for signal phase shift in transient mode rapid estimation. It is shown that the phase difference estimation can be carried out in a time not exceeding 1 ms., which significantly exceeds conventional algorithms based on the Fourier filter capabilities. The practical application and joint use of the Fourier filter and the artificial neural network possibilities for the creation relay protection device hybrid measuring elements are discussed. The approach and the obtained results can potentially be applied in a wide range of signal processing tasks.References
Slavutskiy L.A., Ivanova N.N. (2020) Using the simplest neural network as a tool for fault location in power lines: AIP Conference Proceedings, Moscow, 2020, 01-02 April, p. 030006. DOI: 10.1063/5.007492
Пономарева О. В., Пономарев А. В., Смирнова Н. В. Алгоритмы прямого и обратного параметрического быстрого преобразования Фурье // Информационные технологии. 2022. Т. 28, № 1. С. 9-19. DOI: 10.17587/it.28.9-19
Ядарова О. Н., Славутский Л. А. Контроль воздушного потока на основе доплеровского рассеяния ультразвука // Приборы и системы. Управление, контроль, диагностика. 2013. № 3. С. 55-59.
Муравьева О. В., Брестер А. Ф., Муравьев В. В. Сравнительная чувствительность информативных параметров электромагнитно-акустического зеркально-теневого метода на многократных отражениях при контроле пруткового проката // Дефектоскопия. 2022. № 8. С. 36-51. DOI: 10.31857/S0130308222080048
Хайкин С. Нейронные сети: полный курс: пер. с англ. 2-е изд. М.: ООО "И.Д. Вильямс", 2006. - 1104 с.
Slavutskiy L.A., Lazareva N.M., Portnov M.S., Slavutskaya E.V. (2023) Neural net without deep learning: signal approximation by multilayer perceptron: 2nd International Conference on Computer Applications for Management and Sustainable Development of Production and Industry (CMSD-II-2022), Proc. SPIE, 2023, p. 125640. DOI: 10.1117/12.2669233
Rukonuzzman M. and Nakaoka M. (2002) An Advanced Three-Phase Active Filter with Adaptive Neural Network Based Harmonic Detection Scheme. Journal of Power Electronics, 2002, vol. 2, no. 1, pp. 1-10. DOI: 10.6113/JPE.2001.02.1.1
Andreev O.N., Slavutskiy A.L., Slavutskiy L.A. Neural network in a sliding window for power grids signals structural analysis. IOP Conference Series: Earth and Environmental Science, 990 012054. https://iopscience.iop.org/article/10.1088/1755-1315/990/1/012054
Андреев О. Н., Славутский А. Л., Алексеев В. В. Структурный анализ электротехнических сигналов при рекуррентном использовании многослойного персептрона // Электротехника. 2022. № 8. С. 41-44. DOI: 10.53891/00135860_2022_8_41
Shah1 B.S., Parmar S.B. (2017) Transformer protection using artificial neural network. IJNRD, 2017, vol. 2 (5), pp. 108-111.
Haque M.T., Atabak M.K. (2007) Application of Neural Networks in Power Systems; A Review.International Journal of Energy and Power Engineering, 2007: 1: 897-901.
Курбацкий В. Г., Томин Н. В. Применение новых информационных технологий в решении электроэнергетических задач // Системы. Методы. Технологии. 2009. № 1 (1). С. 113-119.
Симонов Н., Ивенев Н. Опыт и перспективы применения искусственных нейронных сетей в электроэнергетике // Электроэнергия. Передача и распределение. 2019. № S4 (15). С. 42-48.
Лоскутов А. А., Митрович М., Осокин В. Ю. Повышение распознаваемости режимов функционирования системы электроснабжения на основе методов машинного обучения // Релейная защита и автоматизация. 2020. № 4 (41). С. 26-34.
Куликов А. Л., Лоскутов А. А., Бездушный Д. И. Алгоритмы релейной защиты и автоматики электрических сетей, основанные на имитационном моделировании и методах машинного обучения // Стратегия устойчивого развития электроэнергетики, низкоуглеродные способы генерации, экология, тарифное регулирование. М.: Изд-во МЭИ, 2022. С. 101-129.
Панов М., Хмелев И., Смирнов А. Нейронные сети на службе энергетиков // Открытые системы. СУБД. 2016. № 4. С. 39-41. ISSN 1028-7493
Osowski S. (1992) Neural network for estimation of harmonic components in a power system: IEEЕ Proceedings on Generation, Transmission and Distribution, 1992, vol. 139 (2), pp. 129-135.
Dillon T.S., Niebur D. (1996) Neural Networks Application in Power Systems. CRL Ltd. Publishing, London, 1996.
Brdyś M.A., Kulawski G.J. (1999) Dynamic neural controllers for induction motor. IEEE Trans Neural Netw, 1999, vol. 10 (2), pp. 340-55. DOI: 10.1109/72.750564
Alhanjouri Mohammed (2017) Speed Control of DC Motor using Artificial Neural Network.International Journal of Science and Research (IJSR), 2017, vol. 7, pp. 2140-2148. DOI: 10.21275/ART20172035
Buettner M. A., Monzen N., Hackl C. M. (2022) Artificial Neural Network Based Optimal Feedforward Torque Control of Interior Permanent Magnet Synchronous Machines: A Feasibility Study and Comparison with the State-of-the-Art. Energies, 2022, vol. 15 (5), p. 1838. DOI: 10.3390/en15051838
Ghlib I., Messlem Y., Chedjara Z. (2019) ADALINE-Based Speed Control For Induction Motor Drive: 2019 International Conference on Advanced Electrical Engineering (ICAEE). Algiers, Algeria, 2019, pp. 1-6. DOI: 10.1109/ICAEE47123.2019.9015162
Eddahech A., Briat O., Vinassa J. (2011) Neural networks based model and voltage control for lithium polymer batteries: 8th IEEE Symposium on Diagnostics for Electrical Machines, Power Electronics & Drives: Bologna, Italy, 2011, pp. 645-650. DOI: 10.1109/DEMPED.2011.6063692
Li Xiaoou, Yu Wen. (2002) Dynamic system identification via recurrent multilayer perceptrons. Information Sciences, 2002, vol. 147 (1-4), pp. 45-63. DOI: 10.1016/S0020-0255(02)00207-4
Hornik Kurt, Stinchcombe Maxwell, White Halbert. (1990) Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networks. Neural Networks, 1990, vol. 3 (5), pp. 551-560. DOI: 10.1016/0893-6080(90)90005-6
José A.R. Vargas, Pedrycz W., Hemerly M. Elder (2019) Improved learning algorithm for two-layer neural networks for identification of nonlinear systems. Neurocomputing, 2019, vol. 329, pp. 86-96. DOI: 10.1016/j.neucom.2018.10.008
Parlos A.G., Chong K.T., Atiya A.F. (1994) Application of the recurrent multilayer perceptron in modeling complex process dynamics: IEEE Trans Neural Netw, 1994, vol. 5 (2), pp. 255-266. DOI: 10.1109/72.279189
Plett G.L. (2003) Adaptive inverse control of linear and nonlinear systems using dynamic neural networks: IEEE Trans Neural Netw, 2003, vol. 14 (2), pp. 360-376. DOI: 10.1109/TNN.2003.809412
Jaroslav Timko, Peter Girovský (2006) Nonlinear System Control Using Neural Networks. Acta Polytechnica Hungarica, 2006, vol. 3 (4), pp. 85-94.
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