Biogeography based tuning of PID controllers for Load Frequency Control in microgrid

Kumar, Rakesh; Ushakumari, S.

doi:10.1109/iccpct.2014.7054992

Cited by 28 publications

(17 citation statements)

References 15 publications

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“…On the assumption that simplified models can result to reasonable simulation results in case of the consideration of small disturbances [4, 11, 12, 14, 16, 23, 38–42]. As a result, in this article, a simple ﬁrst‐order lag transfer functions ignoring all the non‐linearities are proposed.…”

Section: Configuration and Modelling Of The Hμgs Under Studymentioning

confidence: 99%

“…As examples for conventional methods, conventional PI controller [3, 5], Ziegler–Nichols–based LFC [6], and H‐∞‐based controllers [7, 8] are presented to achieve the same. In addition, artificial intelligence‐based methods and swarm‐based approaches are exploited to improve the system dynamic responses such as artificial neural network [9], fuzzy logic controllers [10, 11], genetic algorithm (GA) [12], particle swarm optimiser [13], biogeography‐based optimisation technique [14], quasi‐oppositional harmony search algorithm [11, 15] and so on [16–21]. In addition, comprehensive and wide technical reviews are deliberated and presented in the literatures [22–25].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Efficient frequency controllers for autonomous two‐area hybrid microgrid system using social‐spider optimiser

El‐Fergany

El‐Hameed

2017

IET Generation, Transmission & Distribution

179

125

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Section: Configuration and Modelling Of The Hμgs Under Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Efficient frequency controllers for autonomous two‐area hybrid microgrid system using social‐spider optimiser

El‐Fergany

El‐Hameed

2017

IET Generation, Transmission & Distribution

179

125

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“…So far, various control methods have been proposed to regulate the microgrid frequency to the nominal frequency. Methods based on traditional and artificial intelligence controllers for the LFC problem can be found in the articles (Das et al, 2012;Dhanalakshmi & Palaniswami, 2011;Goya et al, 2011;Kumar & Ushakumari, 2014;Lal & Barisal, 2017;Mallesham et al, 2011;Ray et al, 2011;Veronica & Kumar, 2017;Vidyanandan & Senroy, 2016). Dhanalakshmi and Palaniswami (2011), Ray et al (2011), Vidyanandan andSenroy (2016) have suggested a technique based on PI controller, Mallesham et al have proposed a Ziegler-Nichols-based method to adjust the PID controller parameters (Mallesham et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Frequency control of an island microgrid using reset control method in the presence of renewable sources and parametric uncertainty

Banki

Faghihi

Soleymani

2020

Systems Science & Control Engineering

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In recent years, several powerful control methods have been proposed. One of these methods is the reset control, simple nonlinear control technique very effective for plants subjected to fundamental design limitations, and this paper suggests its implementation for secondary control of the frequency of an island microgrid. The studied microgrid includes various elements such as renewable energy sources and various factors such as the load changes, power fluctuations from renewable energy sources, dynamic disturbances as well as parametric uncertainties affecting the frequency of the grid and the complexity of its control. To overcome the above effects and stabilize the network frequency, this paper uses the reset control method to regulate the network operating frequency. Using the proposed controller, the behaviour of the network under parametric changes and different disturbances has been investigated and compared with conventional and optimized PID control methods. High speed frequency regulatory and low undershot/overshot are some of the advantages of the proposed method. The simulation results show the desired performance of the proposed control method in stabilizing the network frequency under different scenarios.

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“…A method for determining the optimal size of a BESS for primary frequency control of an MG was presented in Aghamohammadi and Abdolahinia (2014). The PID control has been considered as an applicable solution for the LFC because of its ease of use and also its robustness (Kumar and Ushakumari, 2014; Yan et al , 2014). A new method for tuning PID controllers for LFC in an autonomous MG using Biogeography-Based Optimization was reported in Kumar and Ushakumari (2014).…”

Section: Introductionmentioning

confidence: 99%

“…The PID control has been considered as an applicable solution for the LFC because of its ease of use and also its robustness (Kumar and Ushakumari, 2014; Yan et al , 2014). A new method for tuning PID controllers for LFC in an autonomous MG using Biogeography-Based Optimization was reported in Kumar and Ushakumari (2014). Yan et al (2014) propose using fractional-order PI controller, which increases an adjustable parameter (the order of integral), to stabilize EV battery-incorporated MG system.…”

Section: Introductionmentioning

confidence: 99%

Reinforcement learning based PID controller design for LFC in a microgrid

Esmaeili

Shayeghi

Nejad

et al. 2017

COMPEL

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Purpose This paper aims to propose an improved reinforcement learning-based fuzzy-PID controller for load frequency control (LFC) of an island microgrid. Design/methodology/approach To evaluate the performance of the proposed controller, three different types of controllers including optimal proportional-integral-derivative (PID) controller, optimal fuzzy PID controller and the proposed reinforcement learning-based fuzzy-PID controller are compared. Optimal PID controller and classic fuzzy-PID controller parameters are tuned using Non-dominated Sorting Genetic Algorithm-II algorithm to minimize overshoot, settling time and integral square error over a wide range of load variations. The simulations are carried out using MATLAB/SIMULINK package. Findings Simulation results indicated the superiority of the proposed reinforcement learning-based controller over fuzzy-PID and optimal-PID controllers in the same operational conditions. Originality/value In this paper, an improved reinforcement learning-based fuzzy-PID controller is proposed for LFC of an island microgrid. The main advantage of the reinforcement learning-based controllers is their hardiness behavior along with uncertainties and parameters variations. Also, they do not need any knowledge about the system under control; thus, they can control any large system with high nonlinearities.

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Biogeography based tuning of PID controllers for Load Frequency Control in microgrid

Cited by 28 publications

References 15 publications

Efficient frequency controllers for autonomous two‐area hybrid microgrid system using social‐spider optimiser

Efficient frequency controllers for autonomous two‐area hybrid microgrid system using social‐spider optimiser

Frequency control of an island microgrid using reset control method in the presence of renewable sources and parametric uncertainty

Reinforcement learning based PID controller design for LFC in a microgrid

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