| Русский Русский | English English |
   
Главная Архив номеров
31 | 10 | 2020
10.14489/vkit.2017.06.pp.003-010

DOI: 10.14489/vkit.2017.06.pp.003-010

Манусов В. З., Матренин П. В.
ПОСТРОЕНИЕ НЕЧЕТКОГО РЕГУЛЯТОРА ВЕТРОЭНЕРГЕТИЧЕСКОЙ УСТАНОВКИ С ИСПОЛЬЗОВАНИЕМ АЛГОРИТМА РОЯ ЧАСТИЦ
(c. 3-10)

Аннотация. Рассмотрены задача оптимального управления ветроэнергетической установкой на основе нечеткого регулятора и автоматизация формирования базы нечетких правил. Предложен метод оптимизации базы нечетких правил, составленной различными экспертами, основанный на расстановке весов у данных правил с помощью алгоритма роя частиц. На основе экспериментов показано, что предложенный подход позволяет из базы неоптимизированных правил сформировать базу, совпадающую с образцовой для рассматриваемого контура управления и нечеткой модели ветра.

Ключевые слова:  ветроэнергетическая установка; нечеткое регулирование; роевой интеллект; алгоритм роя частиц.

 

Manusov V. Z., Matrenin P. V.
CONSTRUCTING FUZZY CONTROLLER OF WIND POWER PLANT USING PARTICLE SWARM OPTIMIZATION ALGORITHM
(pp. 3-10)

Abstract. Wind power becomes more and more popular form of energy generation. At the same time, it is required to increase the efficiency of the wind plants. The problem of the optimal control of a wind power plant using fuzzy control and the problem automation of constructing the fuzzy rule base are considered. The paper describes a fuzzy model of the wind, fuzzy controller of wind power plant, implementation of swarm intelligence to the optimization of the fuzzy rule base. The fuzzy model of wind relies on Beaufort scale as membership functions of the wind velocity. Wind power plant controller considered has wind speed and direction as input values. The controller gives commands for changing nacelle turn, blade attack angle, and wind wheel blade length. Fuzzy controller operation is carried out as a process of sequential execution of steps fuzzification, fuzzy rules evaluation using Mamdani algorithms, defuzzification. Expert fuzzy rules do not always provide a maximum output power, so expert fuzzy rule bases require adjustments while parameters of wind power plant or the environment are changing. The paper proposes a method of fuzzy rules base optimization compiled by some experts. This method is based on tuning weights of fuzzy rules to improve an efficiency of fuzzy controlled using these rules. Besides, it is proposed to delete rules weight of which is lower than the limit. Fuzzy rule base optimization is the high-dimensional optimization problem with stochastic elements. For such optimization tasks, it is reasonable to apply stochastic population-based optimization methods, such as Swarm Intelligence. In this paper, Particle Swarm Optimization algorithm is implemented. Experiments confirmed that method proposed allows producing fuzzy rule base taking into account the control loop of wind power plant and fuzzy model of the wind. In particular, the algorithm applied allowed forming selecting 16 required items from the base of 200 original rules. The 200-rules base consisted 16 right elements and 184 random generated wrong rules. The research shows that swarm intelligence optimization methods can significantly automate designing fuzzy rule base.

Keywords: Wind power plant; Fuzzy control; Swarm intelligence; Particle swarm optimization.

Рус

В. З. Манусов, П. В. Матренин (Новосибирский государственный технический университет, Новосибирск, Россия) E-mail: Этот e-mail адрес защищен от спам-ботов, для его просмотра у Вас должен быть включен Javascript

Eng

V. Z. Manusov, P. V. Matrenin (Novosibirsk State Technical University, Novosibirsk, Russia) E-mail: Этот e-mail адрес защищен от спам-ботов, для его просмотра у Вас должен быть включен Javascript  

Рус

1. Grogg K. Harvesting the Wind: The Physics of Wind Turbines. Carleton College. USA: Northfield, 2005. 42 p.
2. Удалов С. Н., Манусов В. З. Моделирование ветроэнергетических установок и управление ими на основе нечеткой логики. Новосибирск: Изд-во НГТУ, 2013. 200 с.
3. Carline P. W., Laxson А. S., Muljadi E. B. The History and State of the Art of Variable-Speed Wind Turbine Technology: Technical Report NREL/TP-500-28607 – National Renewable Energy Laboratory. USA: Golden, Colorado, 2001. 68 p.
4. Schiemenz I., Stiebler M. Maximum Power Point Tracker of a Wind Energy System with a Permanent-Magnet Synchronous Generator // Proc. ICEM. 2000. Р. 1083 – 1086.
5. DeCarlo R., Zak S., Drakunov S. Variable Structure and Sliding Mode Control // The Control Handbook (W. S. Levine, ed.). CRC Press, Boca Raton, Fl. 1996. Р. 941 – 950.
6. Young K. D., Utkin V. I., Ozguner U. A Control Engineer’s Guide to Sliding Mode Control // IEEE Transactions on Control System Technology. 1999. V. 7, № 3. P. 328 – 342.
7. De Battista H., Mantz R. J. Dynamical Variable Structure Controller for Power Regulation of Wind Energy Conversion System // IEEE Transactions on Energy Conversion. 2004. V. 19, № 4. Р. 756 – 763.
8. Application of a Multivariable Feedback Linearization Scheme for Rotor Angle Stability and Voltage Regulation of Power System / O. Akhfir et al. // IEEE Transactions on Power System. 1999. V. 14, № 2. Р. 620 – 628.
9. Gain Tuning of Fuzzy PID Controllers for MIMO Systems / G. Paulo et al. // IEEE Transactions on Fuzzy Systems. 2015. V. 23, № 4. Р. 757 – 768.
10. Dernoncourt F. Introduction to Fuzzy Logic // Massachusetts Institute of Technology. 2013. January. 21 p.
11. Maximum Power Search in Wind Turbine Based on Fuzzy Logic Control / E. Adzic et al. // Acta Polytechnica Hungarica. 2009. V. 6, № 1. P. 131 – 149.
12. Ekel P. Y. Fuzzy Sets and Models of Decision Making // Computers & Mathematics with Applications. 2002. V. 44, № 7. P. 863 – 875.
13. Kennedy J., Eberhart R. Particle Swarm Optimization // IEEE Intern. Conf. on Neural Network. Piscataway, NJ. 1995. V. 5, № 3. P. 1942 – 1948.
14. Матренин П. В., Секаев В. Г. Системное описание алгоритмов роевого интеллекта // Программная инженерия. 2013. № 12. C. 39 – 45.
15. Poli R. An Analysis of Publications on Particle Swarm Optimisation Applications [Электронный ресурс] // Tech. Rep CSM-469. University of Essex, UK, 2007. 57 p. URL: http://cswww.essex.ac.uk/technical-reports/ 2007/tr-csm 469.pdf (дата обращения: 10.05.2017).
16. Матренин П. В., Манусов В. З. Адаптивный алгоритм роя частиц в задачах оперативного планирования // Вестник компьютерных и информационных технологий. 2016. № 4. С. 11 – 15. doi: 10.14489/ vkit.2016.04. pp.011-015

Eng

1. Grogg K. (2005). Harvesting the wind: The physics of wind turbines. Carleton College. USA: Northfield.
2. Udalov S. N., Manusov V. Z. (2013). Modeling and management of wind power plants based on fuzzy logic. Novosibirsk: Izdatel'stvo NGTU. [in Russian language]
3. Carline P. W., Laxson S., Muljadi E. B. (2001). The history and state of the art of variable-speed wind turbine technology. Technical Report NREL/TP-500-28607 – National Renewable Energy Laboratory. USA: Golden, Colorado.
4. Schiemenz I., Stiebler M. (2000). Maximum power point tracker of a wind energy system with a permanent-magnet synchronous generator. Proc. ICEM, pp. 1083-1086.
5. Levine W.S. (Ed.), De Carlo R. A., Zak S. H., Drakunov S. V. (2010). Variable structure and sliding-mode controller design. The Control Handbook. (pp. 941-951). New York: CRC Press Inc.
6. Young K. D., Utkin V. I., Ozguner U. (1999). A control engineer’s guide to sliding mode control. IEEE Transactions on Control System Technology, 7(3), pp. 328-342. doi: 10.1109/87.761053
7. De Battista H., Mantz R. J. (2004). Dynamical variable structure controller for power regulation of wind energy conversion system. IEEE Transactions on Energy Conversion, 19(4), pp. 756-763. doi: 10.1109/TEC.2004.827705
8. Akhfir O. et al. (1999). Application of a multivariable feedback linearization scheme for rotor angle stability and voltage regulation of power system. IEEE Transactions on Power System, 14(2), pp. 620-628. doi: 10.1109/59.761889
9. Paulo G. et al. (2015). Gain tuning of fuzzy PID controllers for MIMO systems. IEEE Transactions on Fuzzy Systems, 23(4), pp. 757-768. doi: 10.1109/TFUZZ.2014.2327990
10. Dernoncourt F. (2013). Introduction to fuzzy logic. Massachusetts Institute of Technology.
11. Adzic E. et al. (2009). Maximum power search in wind turbine based on fuzzy logic control. Acta Polytechnica Hungarica, 6(1), pp. 131-149.
12. Ekel P. Y. (2002). Fuzzy sets and models of decision making. Computers & Mathematics with Applications, 44(7), pp. 863-875. doi: 10.1016/S0898-1221(02)00199-2
13. Kennedy J., Eberhart R. (1995). Particle swarm optimization. IEEE Intern. Conf. on Neural Network, 5(3), (pp. 1942-1948). Piscataway, NJ.
14. Matrenin P. V., Sekaev V. G. (2013). System approach to swarm intelligence. Programmnaia inzheneriia, (12), pp. 39-45. [in Russian language]
15. Poli R. (2007). An analysis of publications on particle swarm optimization applications. Tech. Rep CSM-469. University of Essex, UK. Available at: http://cswww.essex.ac.uk/technical-reports/ 2007/tr-csm 469.pdf (Accessed: 10.05.2017).
16. Matrenin P. V., Manusov V. Z. (2016). Adaptive particle swarm optimization for the operational scheduling problem. Vestnik komp'iuternykh i informatsionnykh tekhnologii, (4), pp. 11-15. doi: 10.14489/ vkit.2016.04. pp.011-015. [in Russian language]

Рус

Статью можно приобрести в электронном виде (PDF формат).

Стоимость статьи 350 руб. (в том числе НДС 18%). После оформления заказа, в течение нескольких дней, на указанный вами e-mail придут счет и квитанция для оплаты в банке.

После поступления денег на счет издательства, вам будет выслан электронный вариант статьи.

Для заказа скопируйте doi статьи:

10.14489/vkit.2017.06.pp.003-010

и заполните  ФОРМУ 

Отправляя форму вы даете согласие на обработку персональных данных.

.

Eng

This article  is available in electronic format (PDF).

The cost of a single article is 350 rubles. (including VAT 18%). After you place an order within a few days, you will receive following documents to your specified e-mail: account on payment and receipt to pay in the bank.

After depositing your payment on our bank account we send you file of the article by e-mail.

To order articles please copy the article doi:

10.14489/vkit.2017.06.pp.003-010

and fill out the  FORM  

.

 

 

 
Поиск
Баннер
Баннер
Баннер
Баннер
Журнал КОНТРОЛЬ. ДИАГНОСТИКА
Rambler's Top100 Яндекс цитирования