FOTOELEKTRIK ENERGIYA TIZIMINI NOANIQ MANTIQLI  BOSHQARUV ORQALI MODELLASHTIRISH

Begmatova Muxlisaxon Mutalibjon qizi

Ushbu maqolada ikki tomonlama DC-DC konvertor asosida
fotoelektrik tizimlar samaradorligini oshirish masalasi ko‘rib chiqilgan. Unda
noaniq mantiq va neyron tarmoq tizimiga asoslangan ANFIS algoritmi yordamida
fotoelektrik tizimni boshqarish usuli taklif etilgan. Tadqiqotning asosiy maqsadi
energiya oqimini optimallashtirish, tizimning kuchlanish barqarorligini oshirish
va tok tebranishlarini kamaytirish orqali batareyaning zaryadlanish jarayonini
samarali boshqarishdan iborat. Modelda ikkita muhim omil – vaqt va atrof-
muhit harorati hisobga olinadi, chunki ushbu parametrlar quyosh panellari
samaradorligiga bevosita ta’sir ko‘rsatadi. Tadqiqot MATLAB/Simulink muhitida
simulyatsiya asosida amalga oshirildi. Taklif etilgan boshqaruv algoritmi
samaradorligi an’anaviy usullar bilan solishtirildi va natijada tizimning dinamik
barqarorligi sezilarli darajada yaxshilandi. ANFIS algoritmi fotoelektrik tizimda
energiya oqimini boshqarishni avtomatlashtirishga yordam beradi, bu esa
ishlab chiqarilgan elektr energiyasi sifatini oshirishga xizmat qiladi. Olingan
natijalar qayta tiklanuvchi energiya tizimlarida, ayniqsa, past kuchlanishli elektr
tarmoqlariga ulangan quyosh fotoelektrik tizimlarini boshqarishda qo‘llanishi
maqsadga muvofiqdir. Tadqiqot natijalari ikki tomonlama DC-DC konvertor
bilan ta’minlangan fotoelektrik tizimlar samaradorligini oshirishda innovatsion
yondashuv sifatida e’tirof etilishi mumkin.

Name of journalИЛМ-ФАН ВА ИННОВАЦИОН РИВОЖЛАНИШ
Number of editionИлм-фан ва инновацион ривожланиш илмий журнали 2025 йил 3-сон
View count 37

Web Address

DOI

Date of creation in the UzSCI system 12-06-2025

Read count 37

Date of publication 02-06-2025

Main LanguageO'zbek

Pages71-80

Tags

yuklama

neyron tarmoq

Quyosh panellari

Noaniq mantiq

akkumulyator batareyasi

past kuchlanishli elektr tarmog‘i

Ўзбек

Ushbu maqolada ikki tomonlama DC-DC konvertor asosida
fotoelektrik tizimlar samaradorligini oshirish masalasi ko‘rib chiqilgan. Unda
noaniq mantiq va neyron tarmoq tizimiga asoslangan ANFIS algoritmi yordamida
fotoelektrik tizimni boshqarish usuli taklif etilgan. Tadqiqotning asosiy maqsadi
energiya oqimini optimallashtirish, tizimning kuchlanish barqarorligini oshirish
va tok tebranishlarini kamaytirish orqali batareyaning zaryadlanish jarayonini
samarali boshqarishdan iborat. Modelda ikkita muhim omil – vaqt va atrof-
muhit harorati hisobga olinadi, chunki ushbu parametrlar quyosh panellari
samaradorligiga bevosita ta’sir ko‘rsatadi. Tadqiqot MATLAB/Simulink muhitida
simulyatsiya asosida amalga oshirildi. Taklif etilgan boshqaruv algoritmi
samaradorligi an’anaviy usullar bilan solishtirildi va natijada tizimning dinamik
barqarorligi sezilarli darajada yaxshilandi. ANFIS algoritmi fotoelektrik tizimda
energiya oqimini boshqarishni avtomatlashtirishga yordam beradi, bu esa
ishlab chiqarilgan elektr energiyasi sifatini oshirishga xizmat qiladi. Olingan
natijalar qayta tiklanuvchi energiya tizimlarida, ayniqsa, past kuchlanishli elektr
tarmoqlariga ulangan quyosh fotoelektrik tizimlarini boshqarishda qo‘llanishi
maqsadga muvofiqdir. Tadqiqot natijalari ikki tomonlama DC-DC konvertor
bilan ta’minlangan fotoelektrik tizimlar samaradorligini oshirishda innovatsion
yondashuv sifatida e’tirof etilishi mumkin.

Tags

yuklama

neyron tarmoq

Quyosh panellari

Noaniq mantiq

akkumulyator batareyasi

past kuchlanishli elektr tarmog‘i

Русский

В данной статье рассматривается вопрос повышения
эффективности фотоэлектрических систем на основе двухстороннего
преобразователя DC-DC. Предлагается метод управления фотоэлектри-
ческой системой с использованием алгоритма ANFIS, основанного на
нечёткой логике и нейронных сетях. Основной целью исследования является
эффективное управление процессом зарядки аккумуляторной батареи за
счёт оптимизации потока энергии, повышения стабильности напряжения
и снижения пульсаций тока. В предложенной модели учитываются
два ключевых параметра – время и температура окружающей среды,
поскольку они оказывают прямое влияние на эффективность солнечных
панелей. Исследование реализовано в среде MATLAB/Simulink на основе
численного моделирования. Эффективность предложенного алгоритма
управления сопоставлена с традиционными методами, при этом
достигнуто существенное улучшение динамической устойчивости системы.
Алгоритм ANFIS способствует автоматизации управления потоками
энергии в фотоэлектрической системе, что в свою очередь повышает
качество вырабатываемой электроэнергии. Полученные результаты
целесообразно использовать в системах возобновляемой энергетики,
особенно при управлении солнечными фотоэлектрическими установками, подключёнными к низковольтным электросетям. Результаты исследо-
вания могут рассматриваться как инновационный подход к повышению
эффективности фотоэлектрических систем с двухсторонним преобразо-
вателем DC-DC.

Tags

нечеткая логика

нейронная сеть

нагрузка

аккумуляторная батарея

солнечные панели

низковольтная электросеть

English

This article discusses the issue of improving the efficiency of photovoltaic
systems based on a two-way DC-DC converter. It proposed a method for controlling
a photovoltaic system using the ANFIS algorithm based on fuzzy logic and a system
of neural networks. The main purpose of the study is to effectively control the
battery charging process by optimizing the energy flow, increasing the stability
of the system voltage, and reducing current fluctuations. The model takes into
account two important factors – time and ambient temperature – since these
parameters directly affect the efficiency of solar panels. The MATLAB/Simulink
environment served as the simulation platform for the study. The effectiveness of
the proposed control algorithm was compared with traditional methods, and its
results showed a significant improvement in the dynamic stability of the system.
The ANFIS algorithm automates control of the energy flow in a photovoltaic
system, which helps improve the quality of the generated electricity. It is advisable
to apply the results obtained in renewable energy systems, especially in the
management of solar photovoltaic systems connected to low-voltage power grids.
The results of the study may be an innovative approach to improving the efficiency
of photovoltaic systems with a dual DC-DC converter.

Tags

fuzzy logic

neural network

load

battery

solar panels

low-voltage electrical network

№ Author name position Name of organisation

1 Begmatova M.M. tayanch doktorant Farg‘ona politexnika instituti

№ Name of reference

1 Abete, A., Barbisio, E., Cane, F., & Demartini, P. (1990). Analysis of photovoltaic modules with protection diodes in presence of mismatching. In Proceedings of the 21st IEEE Photovoltaic Specialists Conference (Vol. 2, pp. 1005–1010). IEEE (MDPI).

2 Agageldiyeva, G., Khydyrguliyeva, A., & Babakhanova, K. (2024). Effektivnoye ispol’zovaniye solnechnoy energii: printsipy, metody i perspektivy [Effective use of solar energy: principles, methods, and prospects]. (In Russian). Vestnik nauki – Bulletin of Science, (11), 1456–1459.

3 Allaev, K. R., Saleem, A., Kholiddinov, I. K., & Iqbal, A. (2023). Evaluation of additional electricity losses in electric networks using a meter. Indonesian Journal of Electrical Engineering and Computer Science, 31(2), 617–625. https://doi.org/10.11591/ijeecs.v31.i2.pp617-625

4 Allaev, K., Kholiddinov, I., & Kholiddinova, M. (2025). Metodologiya otsenki effektivnosti elektricheskikh setey s ispol’zovaniyem nechetkoy logiki [Methodology for assessing the efficiency of electrical networks using fuzzy logic]. (In Russian). Elektrichestvo, (2), 4–14.

5 Boxodirov, I. (2023). Analiz metodov proverki napryazheniya v elektricheskikh setyakh nizkogo napryazheniya [Analysis of voltage testing methods in low-voltage electrical networks]. (In Russian). Mashinasozlik ilmiy-texnika jurnali – Journal of Mechanical Engineering: Scientific and Technical Review, (4), 5–10.

6 da Silva, R. M., & Fernandes, J. L. M. (2010). Hybrid photovoltaic/thermal (PV/T) solar systems simulation with Simulink/Matlab. Solar Energy, 84(12), 1985–1996. https://doi.org/10.1016/j. solener.2010.10.004(Universidade de Lisboa)

7 Jing, L., Xiaobin, Y., & Peiyun, F. (2011). Improved small signal modeling and analysis of the PI controlled boost converter. In 2011 International Conference on Electronics, Communications and Control (pp. 3763–3767). IEEE.(grafiati.com)

8 Kholiddinov, I. (2023). On the method of calculating the coefficient of asymmetry in the reverse sequence. AIP Conference Proceedings, 2789(1), 040057. https://doi.org/10.1063/5.0145463(etr1880.mpei.ru)

9 Kholiddinov, I., & Begmatova, M. (2024). Povysheniye integratsii vozobnovlyaemykh istochnikov energii [Enhancing integration of renewable energy sources]. (In Russian). Educational Research in Universal Sciences, 3(13), 93–103.

10 Kholiddinov, I., & Kholiddinova, M. (2024). Primeneniye neironnykh setey dlya otsenki pokazateley nadezhnosti elektricheskikh setey [Application of neural networks for assessing the reliability indicators of electrical networks]. (In Russian). Energiya va resurslarni tejash muammolari – Problems of Energy and Resource Saving, (87), 301–309.

11 Kholiddinov, I., & Sharobiddinov, M. (2024). Noaniq mantiq bo’yicha tarqatish tarmoqlarida kuchlanishni boshqarish tizimlaridan foydalanish [Application of voltage control systems in distribution networks based on fuzzy logic]. (In Uzbek). Energiya va resurslarni tejash muammolari – Problems of Energy and Resource Saving, (87), 159–168.

12 Kholiddinov, I., Eraliyev, A., Sharobiddinov, M., Tukhtashev, A., Qodirov, A., & Khaqiqov, A. (2024). Estimation the state of power quality in distribution networks using fuzzy logic. E3S Web of Conferences, 538, 1–11.

13 Kholiddinov, I., Sharobiddinov, M., Kholiddinova, M., Komolddinov, S., Qodirov, A., & Tukhtasinov, S. (2025). The methodology for reactive power control to ensure voltage quality using fuzzy logic. EPJ Web of Conferences, 318, 05011. https://doi.org/10.1051/epjconf/202531805011(ResearchGate)

14 Kumar, S. S., & Balakrishna, K. (2024). A new wide input voltage DC–DC converter for solar PV systems with hybrid MPPT controller. Scientific Reports, 14(1), 10639. https://doi.org/10.1038/ s41598-024-61367-x(AZoCleantech)

15 Norenko, I., & Gribanov, A. (2024). Effektivnost’ solnechnykh paneley novogo pokoleniya: tekhnologii i perspektivy [Ef�iciency of new-generation solar panels: technologies and prospects]. (In Russian). Vestnik nauki – Bulletin of Science, (12), 1967–1971.

16 Peftitsis, D., Adamidis, G., & Balouktsis, A. (2008). An investigation of new control method for MPPT in PV array using DC/DC buck-boost converter. In Proceedings of the 2nd WSEAS/IASME International Conference on Renewable Energy Sources (pp. 1–6).(easychair.org)

17 Shi, J., Lee, W.-J., Liu, Y., Yang, Y., & Wang, P. (2011). Forecasting power output of photovoltaic system based on weather classification and support vector machine. In 2011 IEEE Industry Applications Society Annual Meeting (pp. 1–6). IEEE.

18 Xiong, X., Tse, C. K., & Ruan, X. (2013). Bifurcation analysis of standalone photovoltaic-battery hybrid power system. IEEE Transactions on Circuits and Systems I: Regular Papers, 60(5), 1354–1365. https://doi.org/10.1109/TCSI.2013.2239140(CoLab)

19 Xiong, X., Tse, C. K., & Ruan, X. (2015). Bifurcation analysis and experimental study of a multi- operating-mode photovoltaic-battery hybrid power system. IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 5(3), 316–325.

20 Yang, L.-S., & Liang, T.-J. (2012). Analysis and implementation of a novel bidirectional DC–DC converter. IEEE Transactions on Industrial Electronics, 59(1), 422–434.

Waiting

№	Name of reference
1	Abete, A., Barbisio, E., Cane, F., & Demartini, P. (1990). Analysis of photovoltaic modules with protection diodes in presence of mismatching. In Proceedings of the 21st IEEE Photovoltaic Specialists Conference (Vol. 2, pp. 1005–1010). IEEE (MDPI).
2	Agageldiyeva, G., Khydyrguliyeva, A., & Babakhanova, K. (2024). Effektivnoye ispol’zovaniye solnechnoy energii: printsipy, metody i perspektivy [Effective use of solar energy: principles, methods, and prospects]. (In Russian). Vestnik nauki – Bulletin of Science, (11), 1456–1459.
3	Allaev, K. R., Saleem, A., Kholiddinov, I. K., & Iqbal, A. (2023). Evaluation of additional electricity losses in electric networks using a meter. Indonesian Journal of Electrical Engineering and Computer Science, 31(2), 617–625. https://doi.org/10.11591/ijeecs.v31.i2.pp617-625
4	Allaev, K., Kholiddinov, I., & Kholiddinova, M. (2025). Metodologiya otsenki effektivnosti elektricheskikh setey s ispol’zovaniyem nechetkoy logiki [Methodology for assessing the efficiency of electrical networks using fuzzy logic]. (In Russian). Elektrichestvo, (2), 4–14.
5	Boxodirov, I. (2023). Analiz metodov proverki napryazheniya v elektricheskikh setyakh nizkogo napryazheniya [Analysis of voltage testing methods in low-voltage electrical networks]. (In Russian). Mashinasozlik ilmiy-texnika jurnali – Journal of Mechanical Engineering: Scientific and Technical Review, (4), 5–10.
6	da Silva, R. M., & Fernandes, J. L. M. (2010). Hybrid photovoltaic/thermal (PV/T) solar systems simulation with Simulink/Matlab. Solar Energy, 84(12), 1985–1996. https://doi.org/10.1016/j. solener.2010.10.004(Universidade de Lisboa)
7	Jing, L., Xiaobin, Y., & Peiyun, F. (2011). Improved small signal modeling and analysis of the PI controlled boost converter. In 2011 International Conference on Electronics, Communications and Control (pp. 3763–3767). IEEE.(grafiati.com)
8	Kholiddinov, I. (2023). On the method of calculating the coefficient of asymmetry in the reverse sequence. AIP Conference Proceedings, 2789(1), 040057. https://doi.org/10.1063/5.0145463(etr1880.mpei.ru)
9	Kholiddinov, I., & Begmatova, M. (2024). Povysheniye integratsii vozobnovlyaemykh istochnikov energii [Enhancing integration of renewable energy sources]. (In Russian). Educational Research in Universal Sciences, 3(13), 93–103.
10	Kholiddinov, I., & Kholiddinova, M. (2024). Primeneniye neironnykh setey dlya otsenki pokazateley nadezhnosti elektricheskikh setey [Application of neural networks for assessing the reliability indicators of electrical networks]. (In Russian). Energiya va resurslarni tejash muammolari – Problems of Energy and Resource Saving, (87), 301–309.
11	Kholiddinov, I., & Sharobiddinov, M. (2024). Noaniq mantiq bo’yicha tarqatish tarmoqlarida kuchlanishni boshqarish tizimlaridan foydalanish [Application of voltage control systems in distribution networks based on fuzzy logic]. (In Uzbek). Energiya va resurslarni tejash muammolari – Problems of Energy and Resource Saving, (87), 159–168.
12	Kholiddinov, I., Eraliyev, A., Sharobiddinov, M., Tukhtashev, A., Qodirov, A., & Khaqiqov, A. (2024). Estimation the state of power quality in distribution networks using fuzzy logic. E3S Web of Conferences, 538, 1–11.
13	Kholiddinov, I., Sharobiddinov, M., Kholiddinova, M., Komolddinov, S., Qodirov, A., & Tukhtasinov, S. (2025). The methodology for reactive power control to ensure voltage quality using fuzzy logic. EPJ Web of Conferences, 318, 05011. https://doi.org/10.1051/epjconf/202531805011(ResearchGate)
14	Kumar, S. S., & Balakrishna, K. (2024). A new wide input voltage DC–DC converter for solar PV systems with hybrid MPPT controller. Scientific Reports, 14(1), 10639. https://doi.org/10.1038/ s41598-024-61367-x(AZoCleantech)
15	Norenko, I., & Gribanov, A. (2024). Effektivnost’ solnechnykh paneley novogo pokoleniya: tekhnologii i perspektivy [Ef�iciency of new-generation solar panels: technologies and prospects]. (In Russian). Vestnik nauki – Bulletin of Science, (12), 1967–1971.
16	Peftitsis, D., Adamidis, G., & Balouktsis, A. (2008). An investigation of new control method for MPPT in PV array using DC/DC buck-boost converter. In Proceedings of the 2nd WSEAS/IASME International Conference on Renewable Energy Sources (pp. 1–6).(easychair.org)
17	Shi, J., Lee, W.-J., Liu, Y., Yang, Y., & Wang, P. (2011). Forecasting power output of photovoltaic system based on weather classification and support vector machine. In 2011 IEEE Industry Applications Society Annual Meeting (pp. 1–6). IEEE.
18	Xiong, X., Tse, C. K., & Ruan, X. (2013). Bifurcation analysis of standalone photovoltaic-battery hybrid power system. IEEE Transactions on Circuits and Systems I: Regular Papers, 60(5), 1354–1365. https://doi.org/10.1109/TCSI.2013.2239140(CoLab)
19	Xiong, X., Tse, C. K., & Ruan, X. (2015). Bifurcation analysis and experimental study of a multi- operating-mode photovoltaic-battery hybrid power system. IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 5(3), 316–325.
20	Yang, L.-S., & Liang, T.-J. (2012). Analysis and implementation of a novel bidirectional DC–DC converter. IEEE Transactions on Industrial Electronics, 59(1), 422–434.