Optimal Design of Lead Compensator Using Nature-Inspired Algorithms

Abo-Hammour, Zaer S.; Saaideh, Mohammad Al; Alkayyali, Malek; Khasawneh, Hussam J.

doi:10.1109/jeeit.2019.8717416

Cited by 10 publications

(8 citation statements)

References 28 publications

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“…In [34,35], the PSO was successfully employed in electrical power system applications to retrieve the voltage balance, reduce the voltage sags, and estimate power transformer parameters. In [36], the authors compared between GA and PSO for tuning lead controllers which have three parameters to be optimized. It was concluded that the PSO has a larger mean ftness value along with the generation number compared to GA, especially when the generation continues.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Te global best particle (g best ) of the smallest ftness is found by comparing the ftness values of all personal best particles. In each iteration, the velocity and position of the particles are updated according to equations ( 4) and (5), respectively [36]. PSO algorithm continues searching in the d-dimensional space until one termination condition is achieved.…”

Section: Particle Swarm Optimization (Pso) Algorithmmentioning

confidence: 99%

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Intelligent Integrated Approach for Voltage Balancing Using Particle Swarm Optimization and Predictive Models

Ghaeb,

Al-Naimi,

Alkayyali

2023

Journal of Electrical and Computer Engineering

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In this paper, an intelligent integrated approach is proposed to control the reactive power and restore the voltage balance in a three-phase power system using particle swarm optimization (PSO), Gaussian process regression (GPR), and support vector machine (SVM). The PSO algorithm is used in offline mode to determine the optimal set of firing angles for the thyristor-controlled-reactor (TCR) compensator according to the smallest fitness value required for voltage balancing. The optimum firing angles are then used to train the GPR and SVM regression models. The GPR and SVM models are finally used as a real-time controller to retrieve the voltage balance in online mode. A simulation model and experimental setup of the electrical power system are built. The modeled system consists of a 500 km long transmission line. The line is divided into three-pi sections to guarantee a real system response. Several simulation and practical case studies have been conducted to test and validate the capability of the proposed integrated approach in solving the voltage unbalance problem. The results have revealed the supreme ability of the proposed integrated approach to restore the voltage balance quickly (within 20 ms) and for a wide range of voltage unbalance factors (VUFs) (3.90–8.42%).

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Section: Literature Reviewmentioning

confidence: 99%

Section: Particle Swarm Optimization (Pso) Algorithmmentioning

confidence: 99%

Intelligent Integrated Approach for Voltage Balancing Using Particle Swarm Optimization and Predictive Models

Ghaeb,

Al-Naimi,

Alkayyali

2023

Journal of Electrical and Computer Engineering

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“…According to the work in [6], PSO performs the same as the genetic algorithm (GA) in solving the inverse kinematics problem of robotic arm manipulators but PSO converges to the solution in smaller time. Moreover, the statistical results in [7] showed that PSO has better performance than real‐coded GA and binary‐coded GA in terms of fitness value for designing lead compensators.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The fitness of every particle in the swarm is initially calculated, then the particle with the best fitness is assumed to be the personal best particle

()(, P_{best})

and the global best particle

()(, g_{best})

. In the following iterations, the velocity and position of the particles are updated in each iteration according to (12) and (13), respectively [7]. The fitness of the new personal best particle is compared with that of the previous global best particle to find the new global best position, which has the best fitness so far.…”

Section: Hybrid Pso–ann Algorithmmentioning

confidence: 99%

Hybrid PSO–ANN algorithm to control TCR for voltage balancing

Alkayyali

Ghaeb

2020

IET Generation, Transmission & Distribution

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“…ατ and τ, are the time constants of the finite zero and pole, respectively [3]. Recent literature shows that there have been numerous attempts to directly design lead and lag compensators [4][5][6][7][8][9] and fractional lead and lag compensators [10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Archives of Electrical Engineering

Dewantoro,

Rizky,

Murtianta

2020

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With the rapid advancement of digital processors, filters have been commonly implemented using microcomputers. In this study, a low-cost and compact Arduino Uno development board was used to realize digital lead and lag compensators. Arduino boards are very affordable. Consequently, they were investigated to see if they were capable of preserving the frequency response of continuous-time compensators. The experiments required a set of equipment including a function generator, an Arduino Uno development board, a PC-based oscilloscope, and a laptop. The signal frequency was varied from 0 to 500 Hz. Two discretization methods were employed, namely bilinear transformation and matched pole-zero mapping. The results showed that an Arduino Uno board can be utilized to implement lead and lag compensators to some extent. The discrete-time compensator preserved the capability of filtering out certain frequencies. The change in DC gain was negligible, however, there was a significant difference in the cut-off frequency and transient slope. For both discretization methods, the frequency responses at high frequency experienced a rippling profile.

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Optimal Design of Lead Compensator Using Nature-Inspired Algorithms

Cited by 10 publications

References 28 publications

Intelligent Integrated Approach for Voltage Balancing Using Particle Swarm Optimization and Predictive Models

Intelligent Integrated Approach for Voltage Balancing Using Particle Swarm Optimization and Predictive Models

Hybrid PSO–ANN algorithm to control TCR for voltage balancing

Archives of Electrical Engineering

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