New Coordinated Tuning of SVC and PSSs in Multimachine Power System Using Coyote Optimization Algorithm

Guesmi, Tawfik; Alshammari, Badr M.; Almalaq, Yasser; Alateeq, Ayoob; Alqunun, Khalid

doi:10.3390/su13063131

Cited by 28 publications

(23 citation statements)

References 30 publications

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“…Most of this research has been focused on the coordinated design of SVC and PSS controllers. For coordinated design of power system controllers, a large number of such algorithms have recently been offered, including Teaching-Learning Algorithm (TLA) [15], Bacterial Foraging Optimization (BFO) [16], Brainstorm Optimization Algorithm (BOA) [17], Coyote Optimization Algorithm (COA) [18], ant colony optimization (ACO) [19], bat algorithm (BAT) [20], bee colony algorithm (BCA) [11], Genetic Algorithm (GA) [21], particle swarm optimization (PSO) [22], flower pollination algorithm (FPA) [23], gravitational search algorithm (GSA) [24,25], sine-cosine algorithm (SCA) [26], grey wolf optimizer (GWO) [27], firefly algorithm (FA) [28], Differential Evolution (DE) [29], Biogeography-Based Optimization (BBO) [30], Cuckoo Search (CS) algorithm [31], Harmony Search (HS) [32], Seeker Optimization Algorithm (SOA) [33], Imperialist Competitive Algorithm (ICA) [34], Harris Hawk Optimization (HHO) [35], Sperm Swarm Optimization (SSO) [36], Tabu Search (TS) [37], Simulated Annealing [38], Multi-Verse Optimizer (MVO) [39], Moth-Flame Optimization (MFO) [40], and collective decision optimization (CDO) [41]. Although metaheuristic algorithms could provide relatively satisfactory results, no algorithm could provide superior performance than others in solving all optimizing problems.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Rat Swarm Optimization for Optimum Tuning of SVC and PSS in a Power System

Toolabi

Aghahadi

Eslami

et al. 2022

International Transactions on Electrical Energy Systems

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This paper presents a new approach for the coordinated design of a power system stabilizer- (PSS-) and static VAR compensator- (SVC-) based stabilizer. For this purpose, the design problem is considered as an optimization problem, while the decision variables are the controllers’ parameters. This paper proposes an effective optimization algorithm based on a rat swarm optimizer, namely, adaptive rat swarm optimization (ARSO), for solving complex optimization problems as well as coordinated design of controllers. In the proposed ARSO, instead of a random initial population, the algorithm starts the search process with fitter solutions using the concept of the opposite number. In addition, in each iteration of the optimization, the new algorithm replaces the worst solution with its opposite or a random part of the best solution to avoid getting trapped in local optima and increase the global search ability of the algorithm. The performance of the new ARSO is investigated using a set of benchmark test functions, and the results are compared with those of the standard RSO and some other methods from the literature. In addition, a case study from the literature is considered to evaluate the efficiency of the proposed ARSO for coordinated design of controllers in a power system. PSSs and additional SVC controllers are being considered to demonstrate the feasibility of the new technique. The numerical investigations show that the new approach may provide better optimal damping and outperform previous methods.

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Section: Introductionmentioning

confidence: 99%

Adaptive Rat Swarm Optimization for Optimum Tuning of SVC and PSS in a Power System

Toolabi

Aghahadi

Eslami

et al. 2022

International Transactions on Electrical Energy Systems

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“…As per literature, recently the use of metaheuristic algorithms for the optimal tuning of lead–lag‐based PSS and coordination with a different type of FACTS is noteworthy. Numerous different metaheuristic algorithms are used like, particle swarm optimization, 5–7 genetic algorithms, 8,9 backtracking search algorithm, 10 Jaya algorithm, 11 gray wolf optimizer, 12 a hybridization of bat algorithm, gravitational search algorithm and particle swarm optimization are used in Reference 13, farmland fertility algorithm, 14 salp swarm algorithm, 15 kidney‐inspired algorithm, 16 whale optimization algorithm, 17 cuckoo search, 18 Henry gas solubility optimization, 19 collective decision optimization algorithm, 20 slime mold algorithm, 21 coyote optimization algorithm 22 …”

Section: Introductionmentioning

confidence: 99%

Optimal tuning of fractional‐order proportional, integral, derivative and tilt‐integral‐derivative based power system stabilizers using Runge Kutta optimizer

El‐Dabah

Kamel

Abido

et al. 2021

Engineering Reports

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Low-frequency power system oscillation is of great concern as it may lead to power system instability. Moreover, this action will lead to the abate capability of electric power transfer. By introducing a stabilizing signal into the excitation system, it was possible to improve the damping in the system. The power system stabilizer (PSS) provides this signal. This manuscript aims to find the optimal tuning of three different PSS controllers using a recent optimization algorithm called Runge Kutta optimizer (RUN). Based on the obtained results, the RUN shows superiority and fast convergence over competitive algorithms. In addition, the fractional-order proportional, integral, derivative (FOPID) and tilt-integral-derivative (TID) based PSS shows great damping capability over the lead-lag, PI, and FOPID-based PSS. Also, the TID-based PSS achieves better performance indices in terms of the lowest maximum overshoot and minimum settling time. Hence, it is recommended for PSS based controller.

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“…From the literature review, it was found that several ideas and methods have been suggested for the optimal setting of PSS parameters. The most used PSS design methods are summarized in [9][10] and are divided into three main categories which are adaptive control [11][12], linear approximation [13], and nonlinear models [14]. In [12], Model Reference Adaptive System-based PSS (MRAS-PSS) design has been addressed.…”

Section: Introductionmentioning

confidence: 99%

Local Search-based Non-dominated Sorting Genetic Algorithm for Optimal Design of Multimachine Power System Stabilizers

Alshammari

Guesmi

et al. 2021

Eng. Technol. Appl. Sci. Res.

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This study presents a metaheuristic method for the optimum design of multimachine Power System Stabilizers (PSSs). In the proposed method, referred to as Local Search-based Non-dominated Sorting Genetic Algorithm (LSNSGA), a local search mechanism is incorporated at the end of the second version of the non-dominated sorting genetic algorithm in order to improve its convergence rate and avoid the convergence to local optima. The parameters of PSSs are tuned using LSNSGA over a wide range of operating conditions, in order to provide the best damping of critical electromechanical oscillations. Eigenvalue-based objective functions are employed in the PSS design process. Simulation results based on eigenvalue analysis and nonlinear time-domain simulation proved that the proposed controller provided competitive results compared to other metaheuristic techniques.

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New Coordinated Tuning of SVC and PSSs in Multimachine Power System Using Coyote Optimization Algorithm

Cited by 28 publications

References 30 publications

Adaptive Rat Swarm Optimization for Optimum Tuning of SVC and PSS in a Power System

Adaptive Rat Swarm Optimization for Optimum Tuning of SVC and PSS in a Power System

Optimal tuning of fractional‐order proportional, integral, derivative and tilt‐integral‐derivative based power system stabilizers using Runge Kutta optimizer

Local Search-based Non-dominated Sorting Genetic Algorithm for Optimal Design of Multimachine Power System Stabilizers

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