Simulation Modeling of Intelligent Control Algorithms for Constructing Autonomous Power Supply Systems with Improved Energy Efficiency
| Parent link: | MATEC Web of Conferences Vol. 155 : Information and Measuring Equipment and Technologies (IME&T 2017).— 2018.— [01032, 7 p.] |
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| Summary: | Title screen The paper considers the issue of supplying autonomous robots by solar batteries. Low efficiency of modern solar batteries is a critical issue for the whole industry of renewable energy. The urgency of solving the problem of improved energy efficiency of solar batteries for supplying the robotic system is linked with the task of maximizing autonomous operation time. Several methods to improve the energy efficiency of solar batteries exist. The use of MPPT charge controller is one these methods. MPPT technology allows increasing the power generated by the solar battery by 15 - 30%. The most common MPPT algorithm is the perturbation and observation algorithm. This algorithm has several disadvantages, such as power fluctuation and the fixed time of the maximum power point tracking. These problems can be solved by using a sufficiently accurate predictive and adaptive algorithm. In order to improve the efficiency of solar batteries, autonomous power supply system was developed, which included an intelligent MPPT charge controller with the fuzzy logic-based perturbation and observation algorithm. To study the implementation of the fuzzy logic apparatus in the MPPT algorithm, in Matlab/Simulink environment, we developed a simulation model of the system, including solar battery, MPPT controller, accumulator and load. Results of the simulation modeling established that the use of MPPT technology had increased energy production by 23%; introduction of the fuzzy logic algorithm to MPPT controller had greatly increased the speed of the maximum power point tracking and neutralized the voltage fluctuations, which in turn reduced the power underproduction by 2%. |
| Language: | English |
| Published: |
2018
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| Subjects: | |
| Online Access: | https://doi.org/10.1051/matecconf/201815501032 http://earchive.tpu.ru/handle/11683/46992 |
| Format: | Electronic Book Chapter |
| KOHA link: | https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=657821 |
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| 200 | 1 | |a Simulation Modeling of Intelligent Control Algorithms for Constructing Autonomous Power Supply Systems with Improved Energy Efficiency |f R. Gimazov, S. V. Shidlovskiy | |
| 203 | |a Text |c electronic | ||
| 210 | |d 2018 | ||
| 300 | |a Title screen | ||
| 320 | |a [References: 4 tit.] | ||
| 330 | |a The paper considers the issue of supplying autonomous robots by solar batteries. Low efficiency of modern solar batteries is a critical issue for the whole industry of renewable energy. The urgency of solving the problem of improved energy efficiency of solar batteries for supplying the robotic system is linked with the task of maximizing autonomous operation time. Several methods to improve the energy efficiency of solar batteries exist. The use of MPPT charge controller is one these methods. MPPT technology allows increasing the power generated by the solar battery by 15 - 30%. The most common MPPT algorithm is the perturbation and observation algorithm. This algorithm has several disadvantages, such as power fluctuation and the fixed time of the maximum power point tracking. These problems can be solved by using a sufficiently accurate predictive and adaptive algorithm. In order to improve the efficiency of solar batteries, autonomous power supply system was developed, which included an intelligent MPPT charge controller with the fuzzy logic-based perturbation and observation algorithm. To study the implementation of the fuzzy logic apparatus in the MPPT algorithm, in Matlab/Simulink environment, we developed a simulation model of the system, including solar battery, MPPT controller, accumulator and load. Results of the simulation modeling established that the use of MPPT technology had increased energy production by 23%; introduction of the fuzzy logic algorithm to MPPT controller had greatly increased the speed of the maximum power point tracking and neutralized the voltage fluctuations, which in turn reduced the power underproduction by 2%. | ||
| 461 | 0 | |0 (RuTPU)RU\TPU\network\4526 |t MATEC Web of Conferences | |
| 463 | 0 | |0 (RuTPU)RU\TPU\network\24598 |t Vol. 155 : Information and Measuring Equipment and Technologies (IME&T 2017) |o VIII International Scientific and Practical Conference, November 22-25, 2017, Tomsk, Russia |o [proceedings] |f National Research Tomsk Polytechnic University (TPU) ; eds. E. Siemens [et al.] |v [01032, 7 p.] |d 2018 | |
| 610 | 1 | |a электронный ресурс | |
| 610 | 1 | |a труды учёных ТПУ | |
| 610 | 1 | |a моделирование | |
| 610 | 1 | |a алгоритмы | |
| 610 | 1 | |a автономные системы | |
| 610 | 1 | |a электропитание | |
| 610 | 1 | |a энергоэффективность | |
| 610 | 1 | |a автономные роботы | |
| 610 | 1 | |a солнечные батареи | |
| 610 | 1 | |a контроллеры | |
| 610 | 1 | |a имитационные модели | |
| 700 | 1 | |a Gimazov |b R. |g Ruslan | |
| 701 | 1 | |a Shidlovskiy |b S. V. |c a specialist in the field of power engineering |c Professor of Tomsk Polytechnic University, doctor of technical sciences |f 1981- |g Stanislav Viktorovich |3 (RuTPU)RU\TPU\pers\34616 |9 17978 | |
| 712 | 0 | 2 | |a Национальный исследовательский Томский политехнический университет |b Инженерная школа энергетики |b Научно-образовательный центр И. Н. Бутакова (НОЦ И. Н. Бутакова) |3 (RuTPU)RU\TPU\col\23504 |
| 801 | 2 | |a RU |b 63413507 |c 20181022 |g RCR | |
| 856 | 4 | |u https://doi.org/10.1051/matecconf/201815501032 | |
| 856 | 4 | |u http://earchive.tpu.ru/handle/11683/46992 | |
| 942 | |c CF | ||