Robust Parameters Tuning of Different Power System Stabilizers Using a Quantum Artificial Gorilla Troops Optimizer

El‐Dabah, Mahmoud A.; Hassan, Mohamed H.; Kamel, Salah; Zawbaa, Hossam M.

doi:10.1109/access.2022.3195892

Cited by 32 publications

(13 citation statements)

References 56 publications

(51 reference statements)

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“…While it has been demonstrated in this study that the artificial gorilla troops algorithm (GTO) is a competitive algorithm by testing it through the CEC2019 benchmark functions, the same results have been achieved with different functions in previous studies [7]. Real-world problem such as renewable energy source [8,9], computer science [10], engineering optimization problems [11], power system stabilizer [12], industrial product design [13], and neural networks optimized [14] was solved with GTO algorithm and improved or hybrid variants of GTO at the used previous works. In this paper, three bar truss design problem will be discussed.…”

Section: Introductionmentioning

confidence: 56%

Assessment of GTO: Performance evaluation via constrained benchmark function, and Optimized of Three Bar Truss Design Problem

Eker

2023

DÜMF MD

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The aim of this paper is to show that the artificial gorilla troops optimization (GTO) algorithm, as an optimizer, can cope with test functions such as CEC2019, and also to best optimize the three bar truss design problem as a constrained optimization problem. As a method, two statistical measures such as the best values provided by the algorithms and the standard deviation showing the distance between the values were studied. At the same time, the convergence rate of the algorithms compared by the convergence curves were examined. For this purpose, it has been competed against two other swarm-based algorithms, sine-cosine algorithm (SCA) and golden eagle optimization (GEO). The optimization of the three bar truss design problem, which is another side of the study, has been made. The GTO algorithm reached the best values in the optimization of the parameters of the problem. In addition to the convergence curve, statistical results have examined, and the advantages of GTO are revealed through box-plot figures that evaluate the relationship between median and quartiles and the distribution among all results.

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Section: Introductionmentioning

confidence: 56%

Assessment of GTO: Performance evaluation via constrained benchmark function, and Optimized of Three Bar Truss Design Problem

Eker

2023

DÜMF MD

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“…The dynamic equations of a system with n machines n PSS and n POD are given below. Here the system state matrix is expressed as, The state-space equation of the system was formed for performing small-signal stability analysis [34]. The combined differential equations after being linearized around a common operating point, and the same is expressed in matrix form as,…”

Section: State-space Matrix Of Proposed Systemmentioning

confidence: 99%

Revamped Sine Cosine Algorithm Centered Optimization of System Stabilizers and Oscillation Dampers for Wind Penetrated Power System

2023

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The mapping of damping ratios and damping factors by Sine-Cosine Algorithm (SCA) is significant in the analysis of low frequency oscillations generated in an integrated network having multiple conventional and renewable energy sources. Multiple random models with unknown search spaces and uncertain conditions and constraints, which incorporate the fluctuation of solutions towards or outwards at the initial stage for solving real-time problems can be analyzed by a revamped sine-cosine algorithm (RSCA) model. In this paper, the SCA has been revamped to explore the stability threshold for a stable system under dynamic nonlinear operating conditions. It is achieved with the infusion of suitable mathematical functions for optimizing the parameters of Power System Stabilizers (PSSs) and Doubly Fed Induction Generator (DFIG) system-based oscillation dampers to enhance the Small Signal Stability of power systems through effective damping of low-frequency oscillations (LFOs). These LFOs weaken the effective power generation and demand management cycle in conventional power systems when the system faces conditions like intermittent renewable energy sources, overloading conditions, impulsive faults etc. The small-signal and transient stability studies for various disturbances and different operating conditions are investigated, and the performance of the proposed RSCA for optimized parameter tuning of system stabilizers and oscillation dampers are analyzed on the modified benchmarking systems with wind generators using MATLAB R simulations. The efficiency and robustness of the proposed algorithm has been verified under selective critical operating conditions, line outages, and load uncertainties to prove that the low frequency modes are damped with an elevated positive damping ratio and rapid settling time with reduced oscillations. The application of system stabilizers and oscillation dampers optimized through the proposed RSCA shows a reduced settling time in the LFOs created and improved damping ratio of 0.22 for an increase in 20% loading and 50% of wind power generation in the case study of Two Area Four Machine System.INDEX TERMS Low-frequency oscillations, optimization problem, power system stabilizer (PSSs), power oscillation dampers (PODs), sine-cosine algorithm, wind energy conversion systems (WECS). I. INTRODUCTION A. BACKGROUND AND CHALLENGES

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“…There are several unique intelligent control design approaches, such as artificial neural networks [3] and fuzzy logic [4], [5], have been researched. As per literature there are so many used metaheuristic optimization algorithms for the optimum tuning of PSS like Particle Swarm Optimization [6], [7], JAYA Algorithm [8], Improved Moth Flame [9], whale optimization algorithm [10], sine cosine algorithm [11], A hybrid modified grey wolf optimization-sine cosine algorithm [12], modified Sperm Swarm Optimization [13], Improved Salp Swarm Optimization Algorithm [14], Adaptive Rat Swarm Optimization [15], improved particle swarm optimization [16], Bat Algorithm [17], Runge Kutta optimizer [18], Quantum Artificial Gorilla Troops Optimizer [19], Cuckoo Search Optimization Algorithm [20], slime mould algorithm [21], honey bee mating optimization [22], Backtracking Search Algorithm [23], kidney-inspired algorithm [24], henry gas solubility optimization algorithm [25], modified arithmetic optimization algorithm [26],Water Cycle-Moth Flame Optimization [27], modified sine cosine algorithm [28], nondominated sorting genetic algorithm [29], Improved Harris Hawk Optimizer [30], genetic algorithm-neural network techniques [31].…”

Section: B Literature Reviewmentioning

confidence: 99%

“…W denotes a parameter to be set before the optimization operation. The first strategy can be mathematically calculated as follows [19]:…”

Section: Exploitation Phasementioning

confidence: 99%

Optimal Tuning of Power System Stabilizers for a Multi-Machine Power Systems Using Hybrid Gorilla Troops and Gradient-Based Optimizers

et al. 2023

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This work discusses the production of a novel hybrid algorithm by combining the gorilla troops optimizer (GTO) with the gradient-based optimizers (GBO) approach. The novel approach is called GTO-GBO, it is offered as a useful tool for optimizing the power system stabilizer (PSS) used in the IEEE four-generator, two-area multi-machine power system subjected to a three-phase short-circuit fault. MATLAB/Simulink software was utilized to carry out the assessments. The suggested approach is initially evaluated using multiple benchmark functions with unimodal and multimodal properties. The results are then compared to other competing algorithms (artificial ecosystem optimizer, artificial rabbits optimizer, Coati Optimization Algorithm, and northern goshawk optimization). The comparisons with various algorithms reveal the developed hybrid GTO-GBO algorithm's considerable promise. This demonstrates the GTO-GBO algorithm's improved balance of global and local search stages. The proposed GTO-GBO algorithm's performance is also evaluated by developing an optimum performing PSS for further examination, allowing observation of its capabilities for difficult real-world engineering challenges. To illustrate the applicability and superior performance of the suggested hybrid algorithm for such a complicated real-world engineering problem, the PSS damping controller is formulated as an optimization problem, and the developed GTO-GBO algorithm is used to search for optimal controller parameters. The latter case's findings are compared to the competitive optimization algorithms where the GTO-GBO demonstrates the efficiency and robustness of this suggested optimization algorithm to enhance power system stability.

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Robust Parameters Tuning of Different Power System Stabilizers Using a Quantum Artificial Gorilla Troops Optimizer

Cited by 32 publications

References 56 publications

Assessment of GTO: Performance evaluation via constrained benchmark function, and Optimized of Three Bar Truss Design Problem

Assessment of GTO: Performance evaluation via constrained benchmark function, and Optimized of Three Bar Truss Design Problem

Revamped Sine Cosine Algorithm Centered Optimization of System Stabilizers and Oscillation Dampers for Wind Penetrated Power System

Optimal Tuning of Power System Stabilizers for a Multi-Machine Power Systems Using Hybrid Gorilla Troops and Gradient-Based Optimizers

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