Optimal coordinated design of UPFC and PSS for improving power system performance by using multi-objective water cycle algorithm

Khodabakhshian, Amin; Esmaili, Mohammad Reza; Bornapour, Mosayeb

doi:10.1016/j.ijepes.2016.03.052

Cited by 50 publications

(17 citation statements)

References 23 publications

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“…N i is for a krill individual defined in Equations 18 and 19. (18) where, 19) and N max is the maximum induced speed, ω n is the inertia weight of the motion induced in the range [0, 1], N i old is the last motion induced, α i local is the local effect provided by the neighbors, and α i target is the target direction effect provided by the best krill individuals. The foraging motion is formulated in terms of 2 main effective parameters.…”

Section: Orcidmentioning

confidence: 99%

“…16,17 Coordinated tuning of several PSSs in an extensive large power system is very difficult. Also, coordinated design of PSS and FACTs devices is reported [18][19][20] with improved damping of LFO in power systems. The optimal tuning of FACTS damping controller is vital for achieving the desirable dynamic response characteristics of them in LFO enhancement.…”

Section: Introductionmentioning

confidence: 99%

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Application of reinforcement learning for generating optimal control signal to the IPFC for damping of low-frequency oscillations

Younesi

Shayeghi

Moradzadeh

2017

Int Trans Electr Energ Syst

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Summary In this paper, an adaptive design of interline power flow controller (IPFC) using a reinforcement learning (RL) approach is utilized for damping of the low‐frequency oscillations (LFOs) in power system. This Q‐learning‐based adaptive damping controller is applied to a single‐machine and multi‐machine power system. The main advantages of the Q‐learning based controller are its robustness and adaptive behavior to change in the operation condition; also, it does not need any knowledge about the control system, making the control strategy suitable for the realistic power systems with high nonlinearities. In order to demonstrate the performance of the proposed RL‐based damping controller in both single‐machine and multi‐machine power system, nonlinear simulations are carried out using MATLAB/Simulink. Three cases of simulations are performed; the system equipped with (1) only optimal classical power system stabilizer (PSS), (2) coordinated designed of PSS and IPFC, and (3) RL‐based optimized IPFC. Krill herd optimization algorithm is used for coordination design of PSS and IPFC in both single machine and multi‐machine power systems. Suitable time‐domain performance indices in multiple operating conditions and different fault types are calculated for all 3 cases of simulations and are compared with one another. Simulation results demonstrate the effectiveness of the proposed control strategy in damping of the LFOs in the power systems.

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Section: Orcidmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Application of reinforcement learning for generating optimal control signal to the IPFC for damping of low-frequency oscillations

Younesi

Shayeghi

Moradzadeh

2017

Int Trans Electr Energ Syst

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“…Since all physical plants operate in bounded regions, we study the control problem of system (9) whose state belongs to a compact subset Ω ⊂ . Let the desired smooth signal * = * , the tracking error , and augmented item be defined as…”

Section: Upfc Rbf Neurosliding Mode Controller Designmentioning

confidence: 99%

“…Over the past several decades, linear and nonlinear control techniques have been successfully proposed and applied in the literature for the control of UPFC based on modern and classical control theories [2][3][4][5][6][7][8][9][10]. However, the main drawback of such techniques is that their application requires the development of mathematical models which are difficult to obtain.…”

Section: Introductionmentioning

confidence: 99%

A New Hybrid UPFC Controller for Power Flow Control and Voltage Regulation Based on RBF Neurosliding Mode Technique

Kenné

Kuate

Fombu

et al. 2017

Advances in Electrical Engineering

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This paper presents a new technique to design a Unified Power Flow Controller (UPFC) for power flow control and DC voltage regulation of an electric power transmission system which is based on a hybrid technique which combines a Radial Basis Function (RBF) neural network (online training) with the sliding mode technique to take advantage of their common features. The proposed controller does not need the knowledge of the perturbation bounds nor the full state of the nonlinear system. Hence, it is robust and produces an optimal response in the presence of system parameter uncertainty and disturbances. The performance of the proposed controller is evaluated through numerical simulations on a Kundur power system and compared with a classical PI controller. Simulation results confirm the effectiveness, robustness, and superiority of the proposed controller.

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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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Optimal coordinated design of UPFC and PSS for improving power system performance by using multi-objective water cycle algorithm

Cited by 50 publications

References 23 publications

Application of reinforcement learning for generating optimal control signal to the IPFC for damping of low-frequency oscillations

Application of reinforcement learning for generating optimal control signal to the IPFC for damping of low-frequency oscillations

A New Hybrid UPFC Controller for Power Flow Control and Voltage Regulation Based on RBF Neurosliding Mode Technique

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

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