Probabilistic power system stabilizer design with consideration of optimal siting using recursive Genetic Algorithm

Wang, Zhiguo; Chung, C.Y.; Wong, K.P.; Gan, Deqiang; Xue, Yali

doi:10.1002/etep.508

Cited by 7 publications

(6 citation statements)

References 31 publications

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“…GAs are nature-inspired algorithms inspired by natural selection and genetics [23][24][25]. The following 4 operators are essential in the GA to create the fittest individuals: selection, crossover, mutation, and replacement.…”

Section: An Overview Of the Gamentioning

confidence: 99%

“…In this paper, the damping performances of the CS algorithm-based and MCS algorithm-based controller designs are compared with the GA-based controller design. GAs are nature-inspired algorithms inspired by natural selection and genetics [23][24][25]. The following 4 operators are essential in the GA to create the fittest individuals: selection, crossover, mutation, and replacement.…”

Section: An Overview Of the Gamentioning

confidence: 99%

See 1 more Smart Citation

Stability analysis of multimachine thermal power systems using the nature-inspired modified cuckoo search algorithm

Rangasamy¹,

Manickam²

2014

Turk J Elec Eng & Comp Sci

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Abstract:The stability of modern interconnected thermal power systems is greatly affected by the presence of lowfrequency inertial oscillations in the system, due to various forms of disturbances experienced. This paper provides an efficient damping solution to these oscillations based on nature-inspired modified cuckoo search algorithm-based controller design. The proposed controller design is formulated as a parameter optimization problem based on damping ratio and time-domain error deviations. The effectiveness of the proposed damping controller design is illustrated by performing the nonlinear time domain simulations of the test multimachine power systems under various operating conditions and disturbances. Moreover, an exhaustive comparative stability analysis is performed based on the damping performance of the modified cuckoo search controller design over the genetic algorithm-based and cuckoo search algorithm-based controller designs.

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Section: An Overview Of the Gamentioning

confidence: 99%

Section: An Overview Of the Gamentioning

confidence: 99%

Stability analysis of multimachine thermal power systems using the nature-inspired modified cuckoo search algorithm

Rangasamy¹,

Manickam²

2014

Turk J Elec Eng & Comp Sci

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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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“…These techniques include robust control [6], optimization methods [7] and artificial intelligence approaches such as fuzzy logic [8], fuzzy sliding mode control techniques [9 ],neuro-fuzzy [10]. Genetic Algorithm (GA) [11] and Particle Swarm Optimization (PSO) [12]. In this paper, a robust controller was developed that uses H∞/μ control of the (PSS) to dampen vibrations in the (SMIB) system.…”

Section: Introductionmentioning

confidence: 99%

Robust Power System Stabilizer Design Based on H∞/μ

Aldurze¹

2020

(AREJ)

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In this article, H∞ /μ controller is relied on to control the power system stabilizer(PSS) using state space approach for a single machine infinite bus (SMIB) system. Design a robust feedback controller for the system using the H∞/μ technology supported by the Matlab / Simulink. The H∞/μ design method leads to a robust controller with a fixed structure and fixed parameters. The uncertainties of the model are taken into account when specifying the weights. The controller demeanor obtained was analyzed through the input represented by the step response and the output response of the power system (PS) in the case of normal operation and then the system with changed parameters. The suggested controller proved its effectiveness by maintaining the stability of the system with acceptable limits of disturbances.

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Probabilistic power system stabilizer design with consideration of optimal siting using recursive Genetic Algorithm

Cited by 7 publications

References 31 publications

Stability analysis of multimachine thermal power systems using the nature-inspired modified cuckoo search algorithm

Stability analysis of multimachine thermal power systems using the nature-inspired modified cuckoo search algorithm

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

Robust Power System Stabilizer Design Based on H∞/μ

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