Design of Velocity PID-Fuzzy Power System Stabilizer Using Particle Swarm Optimization

Elmenfy, Tawfiq H.

doi:10.37394/23202.2021.20.2

Cited by 7 publications

(3 citation statements)

References 12 publications

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“…Several studies have presented findings regarding this matter. In decades, computational approaches have been presented in tuning PSS parameters such as atomic search optimization (ASO) [7], moth search algorithm (MSO) [8], whale optimization algorithm (WOA) [9]- [12], Henry gas solubility optimization (HSO) [13], particle swarm optimization (PSO) [14]- [17], grey wolf optimization (GWO) [18]- [21] and JAYA algorithm (JA) [22]- [24]. This article presents the power system stabilizer tuning method using the modified dandelion optimizer (DO) method.…”

Section: Introductionmentioning

confidence: 99%

A novel modified dandelion optimizer with application in power system stabilizer

Aribowo

Suprianto

Prapanca

2023

IJ-AI

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<p>This article presents a newly developed modification of the dandelion optimizer (DO). The proposed method is a chaotic algorithmic integrity and modification of the original dandelion optimizer. Dandelion is one of the plants that rely on wind for seed propagation. This article presents the tuning of the power system stabilizer with the method proposed in a case study of a single machine system. The validation of the proposed method uses the benchmark function and performance on a single engine system against transient response. The method used as a comparison in this article is the whale optimization algorithm (WOA), grasshopper optimization algorithm (GOA) and the original dandelion optimizer (DO). The simulation results show that the proposed method, which is a modified dandelion optimizer, provides promising performance.</p>

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Section: Introductionmentioning

confidence: 99%

A novel modified dandelion optimizer with application in power system stabilizer

Aribowo

Suprianto

Prapanca

2023

IJ-AI

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“…In recent years, the PSS control method has been integrated with several optimization methods. Several optimization methods that are better known as metaheuristics have been widely presented, such as the Tunicate Swarm Algorithm (TSA) [57][58], Gorilla Troops Optimizer (GTO) [59], Atom Search Optimization (ASO) [60], Salp Swarm Algorithm (SSA) [61][62], Particle Swarm Optimization (PSO) [63][64][65], and Antlion Algorithm [66].…”

Section: Introductionmentioning

confidence: 99%

A Novel Hybrid Prairie Dog Optimization Algorithm - Marine Predator Algorithm for Tuning Parameters Power System Stabilizer

Aribowo,

Rohman,

Baskoro

et al. 2023

Journal of Robotics and Control

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The article presents the parameter tuning of the Power System Stabilizer (PSS) using the hybrid method. The hybrid methods proposed in this article are Praire Dog Optimization (PDO) and Marine Predator Algorithm (MPA). The proposed method can be called PDOMPA. In the PDOMPA method, the marine predator algorithm (MPA) is able to search around optimal individuals when updating population positions. MPA is used to make the exploration and exploitation stages of PDO more valid and accurate. PDO is an algorithm inspired by the life of prairie dogs. Prairie dogs are adapted to colonizing in burrows underground. Prairie dogs have daily habits of eating, observing for predators, establishing fresh burrows, or preserving existing ones. Meanwhile, MPA is a duplication of marine predator life which is modeled mathematically. In order to validate the performance of the PDOMPA method, this article presents a comparative simulation of the objective function and the transient response of PSS. This research uses validation by comparing with conventional methods, Whale Optimization Algorithm (WOA), Grasshopper Optimization Algorithm (GOA), Marine Predator Algorithm (MPA), and Praire Dog Optimization (PDO). Based on the simulation results, PDOMPA presents fast convergence in some cases and shows optimal results compared to competitive algorithms. From the simulation results using load variations, it was found that the proposed method has the ability to reduce the average undershoot and overshoot of speed by 42.2% and 85.37% compared to the PSS-Lead Lag method. Meanwhile the average settling time value of speed is 50.7%.

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“…Aljarbouh et al [15] has proposed the design of PSS using time-delay neural network for improvement of dynamic stability of the system. In work [16] presented a PID-fuzzy power system stabilizer (PID-FPSS) technique was developed on the basis of a velocity PID controller, and its gains are tuned using the particle swarm optimization technique. The speed deviation, derivative speed deviation, and output power are used as inputs to the fuzzy controller in this stabilizer.…”

mentioning

confidence: 99%

Particle swarm optimization and Taguchi algorithm-based power system stabilizer-effect of light loading condition

Kalegowda

Srinivasan²,

Chinnamadha³

2022

IJECE

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A robust design of particle swarm optimization (PSO) and Taguchi algorithm-based power system stabilizer (PSS) is presented in this paper. It incorporates a novel concept in which Taguchi and PSO techniques are integrated for stabilization of single machine infinite bus (SMIB). The system tolerates uncertainty and imprecision to a maximum extent. The proposed controller's effectiveness is proved through experiments covering light load condition using MATLAB/Simulink platform. The performance of the system is compared without PSS and with a conventional PSS. The settling time of the optimal PSS is decreased by more than 75% to conventional PSS. The study reveals that the proposed hybrid controller offers enhanced performance with respect to settling time as well as peak overshoot of the system.

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Design of Velocity PID-Fuzzy Power System Stabilizer Using Particle Swarm Optimization

Cited by 7 publications

References 12 publications

A novel modified dandelion optimizer with application in power system stabilizer

A novel modified dandelion optimizer with application in power system stabilizer

A Novel Hybrid Prairie Dog Optimization Algorithm - Marine Predator Algorithm for Tuning Parameters Power System Stabilizer

Particle swarm optimization and Taguchi algorithm-based power system stabilizer-effect of light loading condition

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