Fractional‐order lead‐lag compensator‐based multi‐band power system stabiliser design using a hybrid dynamic GA‐PSO algorithm

Abdulkhader, Haseena Kuttomparambil; Jacob, Jeevamma; Mathew, Abraham T.

doi:10.1049/iet-gtd.2017.1087

Cited by 68 publications

(9 citation statements)

References 33 publications

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“…This can happen because there are 𝑎 𝑖 , 𝑏 𝑖 and 𝛽 𝑖 . Hence the equation can be described using (13).…”

Section: Extreme Learning Machinementioning

confidence: 99%

“…∑ 𝛽 𝑖 𝐺(𝑎 𝑖 , 𝑏 𝑖 , 𝑥 𝑗 ) = 𝑜 𝑗 , 𝑗 = 1, … , 𝑁 𝐿 𝑖=1 (13) We can further simplify (13) as described in ( 14) to (17). H is the matrix output hidden layer from SLFNs, column i -th from H is the output from hidden node i -th that connected with the input 𝑥 1 , 𝑥 2 , 𝑥 3 , … , 𝑥 𝑁 .…”

Section: Extreme Learning Machinementioning

confidence: 99%

“…The simulation results show that manta ray foraging optimization can be used to optimally design a virtual inertia controller. In [13], [14], the use of particle swarm optimization (PSO) and the firefly algorithm (FA) to optimize parameters was described. It is clear that PSO and FA can provide better parameters than traditional optimization methods.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive virtual inertia controller based on machine learning for superconducting magnetic energy storage for dynamic response enhanced

Setiadi

Abdillah

Afif

et al. 2023

IJECE

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The goal of this paper was to create an adaptive virtual inertia controller (VIC) for superconducting magnetic energy storage (SMES). An adaptive virtual inertia controller is designed using an extreme learning machine (ELM). The test system is a 25-bus interconnected Java Indonesian power grid. Time domain simulation is used to evaluate the effectiveness of the proposed controller method. To simulate the case study, the MATLAB/Simulink environment is used. According to the simulation results, an extreme learning machine can be used to make the virtual inertia controller adaptable to system variation. It has also been discovered that designing virtual inertia based on an extreme learning machine not only makes the VIC adaptive to any change in the system but also provides better dynamics performance when compared to other scenarios (the overshoot value of adaptive VIC is less than -5×10-5).

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“…This can happen because there are 𝑎 𝑖 , 𝑏 𝑖 and 𝛽 𝑖 . Hence the equation can be described using (13).…”

Section: Extreme Learning Machinementioning

confidence: 99%

Section: Extreme Learning Machinementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Adaptive virtual inertia controller based on machine learning for superconducting magnetic energy storage for dynamic response enhanced

Setiadi

Abdillah

Afif

et al. 2023

IJECE

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“…Multi-band PODCs have the ability to mitigate oscillations at different frequencies. Although, in recent years, some studies have designed PSS-based multi-band controllers [108][109][110] and fractional-order LLC-based multi-band controller [111], no study has been done on multi-band FOPID controllers.…”

Section: Multi-band Fopid-podcmentioning

confidence: 99%

A Survey on FOPID Controllers for LFO Damping in Power Systems Using Synchronous Generators, FACTS Devices and Inverter-Based Power Plants

et al. 2021

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In recent decades, various types of control techniques have been proposed for use in power systems. Among them, the use of a proportional–integral–derivative (PID) controller is widely recognized as an effective technique. The generalized type of this controller is the fractional-order PID (FOPID) controller. This type of controller provides a wider range of stability area due to the fractional orders of integrals and derivatives. These types of controllers have been significantly considered as a new approach in power engineering that can enhance the operation and stability of power systems. This paper represents a comprehensive overview of the FOPID controller and its applications in modern power systems for enhancing low-frequency oscillation (LFO) damping. In addition, the performance of this type of controller has been evaluated in a benchmark test system. It can be a driver for the development of FOPID controller applications in modern power systems. Investigation of different pieces of research shows that FOPID controllers, as robust controllers, can play an efficient role in modern power systems.

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“…GA can also be used to find the best parameter of multi-band power system stabilizer (PSS) as reported in [13]. It is found that small signal stability of power system can be enhanced by introducting multi-band PSS based on GA.…”

Section: Introductionmentioning

confidence: 99%

Design feedback controller of six pulse three phase rectifier based on differential evolution algorithm

Setiadi

Swandaru

Nugroho

2021

IJEECS

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<span id="docs-internal-guid-b9128c38-7fff-a778-f2e1-7959fee15cb3"><span>The application of power electronics devices is increasing significantly in this past few year. This condition could happen due to the development of semiconductor technology. The application of power electronics is varied from power plant application to vehicles application. One of the power electronics devices that becoming popular is rectifier. Rectifier can be used to transform AC electricity to DC. This paper presents fully controlled six pulse three phase rectifier which designed to stabilize the DC voltage output on a certain loads variation. PID controller is used as the close loop controller system of the rectifier. To get optimal coordination differential evolution algorithm is used to tune the parameter of PID controller. simulation is carried out in MATLAB/SIMULINK environment. From the simulation results, it is found that the performance of six pulse three phase rectifier can be enhanced significantly by using PID controller based on differential evolution algorithm.</span></span>

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Fractional‐order lead‐lag compensator‐based multi‐band power system stabiliser design using a hybrid dynamic GA‐PSO algorithm

Cited by 68 publications

References 33 publications

Adaptive virtual inertia controller based on machine learning for superconducting magnetic energy storage for dynamic response enhanced

Adaptive virtual inertia controller based on machine learning for superconducting magnetic energy storage for dynamic response enhanced

A Survey on FOPID Controllers for LFO Damping in Power Systems Using Synchronous Generators, FACTS Devices and Inverter-Based Power Plants

Design feedback controller of six pulse three phase rectifier based on differential evolution algorithm

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