Gradient based hybrid metaheuristics for robust tuning of power system stabilizers

Peres, Wesley; Júnior, Ivo. C. Silva; Filho, João Alberto Passos

doi:10.1016/j.ijepes.2017.08.014

Cited by 63 publications

(55 citation statements)

References 44 publications

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“…These algorithms require a minimum time to optimize the design parameters of any complex engineering problem when compared with the conventional optimization techniques. Simulated annealing, tabu search, genetic algorithm, PSO, ant‐directed hybrid differential evolution, bacteria foraging algorithm, honey‐bee colony algorithm, harmony search algorithm, cuckoo algorithm, chaotic–teaching‐learning methods, grey wolf algorithm, bat algorithm, water cycle algorithm, backtracking search algorithm, gradient‐based metaheuristic algorithm, whale optimization algorithm, gravitational search agorithm, and minimax polynomial approximation using PSO‐based PSS design techniques are developed by many researchers from the last few years. Though several methods are developed, optimal design of PSS for the highly nonlinear multi‐machine interconnected power system operating at different loading conditions is still essential for robust operation.…”

Section: Introductionmentioning

confidence: 99%

An improved whale optimization algorithm for the design of multi‐machine power system stabilizer

Butti¹,

Mangipudi²,

Rayapudi

2020

Int Trans Electr Energ Syst

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Summary An improved whale optimization algorithm (IWOA) for the design of a robust power system stabilizer (PSS) for the multi‐machine power system is developed in this paper. Tuning of PSS parameters using the proposed IWOA is carried out by minimizing a multi‐objective function comprising the damping ratio and damping factor of lightly damped oscillating modes of all the generators. The advantage of considering the objective function is that the lightly damped and undamped oscillating modes of all the generators can shift to a prespecified zone of the s‐plane. The performance of the proposed IWOA is tested on standard benchmark test functions and compared with familiar optimization algorithms. The efficacy of the proposed design technique is tested on three benchmark test systems: three‐generator nine‐bus system, two‐area four‐machine interconnected system, and the New England 10‐generator 39‐bus system working on various operating conditions under several typical disturbances. The potential of the proposed method is demonstrated through eigenvalue analysis. The proposed IWOA‐based PSS is compared with the other stabilizers to show its efficacy.

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Section: Introductionmentioning

confidence: 99%

An improved whale optimization algorithm for the design of multi‐machine power system stabilizer

Butti¹,

Mangipudi²,

Rayapudi

2020

Int Trans Electr Energ Syst

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“…There is also research on fitting work based on the behavior described in nature and other tools that have been systematically developed over time. References [21,22], based on the bat echolocation behavior, use theBAT search algorithm for the optimal design of the PSS power system stabilizers of the multi-machine electric systems.…”

Section: Introductionmentioning

confidence: 99%

Implementation of Particle Swarm Optimization (PSO) Algorithm for Tuning of Power System Stabilizers in Multimachine Electric Power Systems

et al. 2020

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The application of artificial intelligence-based techniques has covered a wide range of applications related to electric power systems (EPS). Particularly, a metaheuristic technique known as Particle Swarm Optimization (PSO) has been chosen for the tuning of parameters for Power System Stabilizers (PSS) with success for relatively small systems. This article proposes a tuning methodology for PSSs based on the use of PSO that works for systems with ten or even more machines. Our new methodology was implemented using the source language of the commercial simulation software DigSilent PowerFactory. Therefore, it can be translated into current practice directly. Our methodology was applied to different test systems showing the effectiveness and potential of the proposed technique.

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“…In general, the CPSS strongly damps the local mode easily in some cases, but needs enhancements to generally damp LFOs types. This enhancement classified into three sets [6,7].…”

Section: Introductionmentioning

confidence: 99%

“…So, through the parameter valuation step, the state can escape to infinity in finite time. That makes it a very complicated challenge for the adaptive system [6,8].…”

Section: Introductionmentioning

confidence: 99%

Performance Assessment of Bacterial Foraging based Power System Stabilizer in Multi-Machine Power System

Ibrahim¹,

Elnaghi²,

Ibrahim³

et al. 2019

IJISA

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Gradient based hybrid metaheuristics for robust tuning of power system stabilizers

Cited by 63 publications

References 44 publications

An improved whale optimization algorithm for the design of multi‐machine power system stabilizer

An improved whale optimization algorithm for the design of multi‐machine power system stabilizer

Implementation of Particle Swarm Optimization (PSO) Algorithm for Tuning of Power System Stabilizers in Multimachine Electric Power Systems

Performance Assessment of Bacterial Foraging based Power System Stabilizer in Multi-Machine Power System

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