Fractional Order PID Control of Hybrid Power System with Renewable Generation Using Genetic Algorithm

Regad, M.; Helaimi, M.; Taleb, Rachid; Othman, Mohamed I. A.

doi:10.1109/sege.2019.8859970

Cited by 19 publications

(28 citation statements)

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“…It can supply high power in a short time. The transfer functions of the BESS and FESS can be presented by a first-order transfer function as given next [1,5,8]…”

Section: Modeling Of Different Generation Componentsmentioning

confidence: 99%

“…where  is the stochastic component of the power, P represents the wind or solar and load powers,  presents the mean value of the power,  is a constant normalizes the generated or demand powers (X) constant to correspondence per unit (p.u.) level, and  is timedependent switching signal with a gain causes sudden fluctuation of the average value for stochastic power [5,8]. For the wind power generation the parameters of (7) are…”

Section: Modeling Of Different Generation Componentsmentioning

confidence: 99%

“…The transfer function of FOPID controller in Laplace domain is given as presented in Fig. 2 [8,12]. At Fig.…”

Section: Modeling Of Different Generation Componentsmentioning

confidence: 99%

“…Many kinds of research investigate to study of power generation system based renewable generation systems such as wind and thermal solar in a hybrid system with a storage system and diesel generator [4,5]. The importance of storage energy systems such as batteries, aqua electrolyser, fuel cell, ultracapacitor, and super magnetic energy storage (SMES) lead to analysis and control of various hybrid system configurations as in [6][7][8][9]. However, the use of conventional sources such as diesel generator and energy storage devices control the mismatches in frequency and power deviations and assure the operation of the hybrid energy system in isolated areas independently of the main grid.…”

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confidence: 99%

“…In this paper, the fractional PID controller is used to control the frequency and power deviations for its robustness shown by the previous studies as in [8]. Many evolutionary optimization methods have been employed for tuning the controller parameters and given more improvement to the control strategies.…”

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Optimal Frequency Control in Microgrid System Using Fractional Order Pid Controller Using Krill Herd Algorithm

Regad

Helaimi

Taleb

et al. 2020

Elektroteh. elektromeh.

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This paper investigates the use of fractional order Proportional, Integral and Derivative (FOPID) controllers for the frequency and power regulation in a microgrid power system. The proposed microgrid system composes of renewable energy resources such as solar and wind generators, diesel engine generators as a secondary source to support the principle generators, and along with different energy storage devices like fuel cell, battery and flywheel. Due to the intermittent nature of integrated renewable energy like wind turbine and photovoltaic generators, which depend on the weather conditions and climate change this affects the microgrid stability by considered fluctuation in frequency and power deviations which can be improved using the selected controller. The fractional-order controller has five parameters in comparison with the classical PID controller, and that makes it more flexible and robust against the microgrid perturbation. The Fractional Order PID controller parameters are optimized using a new optimization technique called Krill Herd which selected as a suitable optimization method in comparison with other techniques like Particle Swarm Optimization. The results show better performance of this system using the fractional order PID controller-based Krill Herd algorithm by eliminates the fluctuations in frequency and power deviation in comparisonwith the classical PID controller. The obtained results are compared with the fractional order PID controller optimized using Particle Swarm Optimization. The proposed system is simulated under nominal conditions and using the disconnecting of storage devices like battery and Flywheel system in order to test the robustness of the proposed methods and the obtained results are compared. References 18, figures 8. енергії, таких як паливна батарея, акумулятор і маховик. Через переривчасту природу інтегрованої відновлювальної енергії, наприклад, вітрогенераторів та фотоелектричних генераторів, які залежать від погодних умов та зміни клімату, це впливає на стабільність мікромережі, враховуючи коливання частоти та відхилення потужності, які можна поліпшити за допомогою вибраного контролера. Контролер дробового порядку має п'ять параметрів порівняно з класичним PID-контролером, що робить його більш гнучким та надійним щодо збурень мікромережі. Параметри PID-контролера дробового порядку оптимізовані за допомогою нової методики оптимізації під назвою «зграя криля», яка обрана як підходящий метод оптимізації порівняно з іншими методами, такими як оптимізація методом рою частинок. Результати показують кращі показники роботи цієї системи за допомогою алгоритму «зграя криля», заснованого на PID-контролері дробового порядку, виключаючи коливання частоти та відхилення потужності порівняно з класичним PID-контролером. Отримані результати порівнюються з PID-контролером дробового порядку, оптимізованим за допомогою оптимізації методом рою частинок. Запропонована система моделюється в номінальному режимі роботи та використовує відключення накопичувальних пристроїв, таких як ак...

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“…It can supply high power in a short time. The transfer functions of the BESS and FESS can be presented by a first-order transfer function as given next [1,5,8]…”

Section: Modeling Of Different Generation Componentsmentioning

confidence: 99%

Section: Modeling Of Different Generation Componentsmentioning

confidence: 99%

“…The transfer function of FOPID controller in Laplace domain is given as presented in Fig. 2 [8,12]. At Fig.…”

Section: Modeling Of Different Generation Componentsmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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