Load Frequency Controller Design via Artificial Cuckoo Search Algorithm

Abdelaziz, Almoataz Y.; Ali, E. S.

doi:10.1080/15325008.2015.1090502

Cited by 51 publications

(16 citation statements)

References 31 publications

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“…The first one is the parallel tied modules, the second one is the series tied modules and the last one is the cross-tied modules. Out of three connection styles series tied modules are highly affected under PSCs as discussed in the papers [9,16,17]. For this reason, in this article parallel-tied modules are considered for extracting the electrical characteristics of the PVGS.…”

Section: Operation Of Pvgs Under Pscmentioning

confidence: 99%

“…CSA was developed based on the reproduction parasitism of cuckoo, the optimization function was discussed in the ref. [16]. In this algorithm, by using (3) and (4) the levy flights are used to update the D in terms of gamma function (γ) [8].…”

Section: Cuckoo Search Algorithmmentioning

confidence: 99%

“…Although the above evolutionary algorithms (EA) work satisfactorily regarding MPPT, there is still a need to develop modern EAs. Recently, CSA has been used to solve the load frequency issues, and it shows very satisfactory results compared to the genetic algorithm and PSOA [14][15][16]. This encourages the authors to develop a modern EA based on CSA to solve MPPT issues also.…”

Section: Introductionmentioning

confidence: 97%

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Comparative Analysis of Photovoltaic Generating Systems Using Particle Swarm Optimization and Cuckoo Search Algorithms under Partial Shading Conditions

Kotla¹,

Yarlagadda²

2021

JESA

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The energy demand on the electricity grids increased rapidly due to that non-conventional energy sources (NCES) like PV, wind power plants are encouraged to establish and operate with the grid. Out of the available NCES, Photovoltaic generating systems (PVGS) are widely penetrated to the grids. As the output power extracted from the PVGS is non-linear, it becomes fluctuating depending on the available Irradiance (G), Temperature (T), and partial shading conditions (PSC). So, there is a need for the development of maximum power point tracking (MPPT) algorithms in the PVGS for maximizing the output power and minimizing the fluctuations. In this article, a comparative analysis of two advanced MPPT algorithms namely particle swarm optimization algorithm (PSOA) and cuckoo search algorithm (CSA) is presented. These two algorithms are used to control the duty cycle of the boost converter to maximize the PVGS output power. The proposed design is modeled using Matlab/Simulink software and the results were obtained and analyzed.

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Section: Operation Of Pvgs Under Pscmentioning

confidence: 99%

Section: Cuckoo Search Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 97%

See 1 more Smart Citation

Comparative Analysis of Photovoltaic Generating Systems Using Particle Swarm Optimization and Cuckoo Search Algorithms under Partial Shading Conditions

Kotla¹,

Yarlagadda²

2021

JESA

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“…Different optimization approaches were introduced to design the PID controllers for the LFC objectives [23]. According to the main optimization rule "No free launch", no optimization algorithm can be globalizing, and the Three different optimization algorithms ware used in this paper which are (GA), (PSO), and GWO.…”

Section: Optimization Techniquesmentioning

confidence: 99%

Load Frequency Control Using Optimized Control Techniques

Hemeida

Mohamed

Mahmoud

2020

JES. Journal of Engineering Sciences

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The current study addresses the impact of the optimized controller to load frequency control (LFC) problem. The proportional-integral-derivative (PID) parameters is determined for both single area and two area control system using genetic algorithms (GA), Particle swarm optimization (PSO), and grey wolf optimization techniques (GWO). The LFC is a stochastic problem due to the load variations and changing the system operating conditions. This results in failing the conventional controller to adapt the LFC in case of implementing the conventional PID. So that, implementing optimized, the PID controller parameters using GA, PSO, and GWO. The technique is used for single area system as well two-area system. The results show the accuracy and robustness of implementing the optimized controller parameters. MATLAB/Simulink is used to solve the system equations. The suggested optimized controller shows fast response with better control quality in compared with conventional controller.

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“…Cuckoo search algorithm is one of the latest meta-heuristic techniques, developed by Yang and Deb [9,10]. Nowadays, implementations of cuckoo search has proved to be effective in engineering optimization problems [11][12][13], for example, optimal power system stabilizers [14], load frequency controller design [15], optimal power system [16], synthesis of analog controller [17], etc. Cuckoo search achieved better results than available methods in most cases which appeared in the literature.…”

Section: Introductionmentioning

confidence: 99%

Design of the Second-Order Controller by Time-Domain Objective Functions Using Cuckoo Search

Horng¹

2019

Swarm Intelligence - Recent Advances, New Perspectives and Applications

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The proportional-integral-derivative (PID) controllers are widely used in many industrial control applications. However, the lead-lag controllers are more practical. Traditionally, time-domain or frequency-domain methods have been used to design a lead-lag controller in order to meet the design specifications. This chapter will focus on the design of controller by optimizing the time-domain objective function. The proposed objective function includes the first peak time, maximum peak time, rise time, maximum overshoot, maximum undershoot, setting time, and steadystate error. In the study, cuckoo search algorithm is adopted to search the optimal controller parameters. Cuckoo search is a recently developed meta-heuristic optimization method, which is a population-based algorithm inspired by the behavior of some cuckoo species in combination with the Lévy flight behavior. A numerical example is simulated to illustrate the use of the proposed method.

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Load Frequency Controller Design via Artificial Cuckoo Search Algorithm

Cited by 51 publications

References 31 publications

Comparative Analysis of Photovoltaic Generating Systems Using Particle Swarm Optimization and Cuckoo Search Algorithms under Partial Shading Conditions

Comparative Analysis of Photovoltaic Generating Systems Using Particle Swarm Optimization and Cuckoo Search Algorithms under Partial Shading Conditions

Load Frequency Control Using Optimized Control Techniques

Design of the Second-Order Controller by Time-Domain Objective Functions Using Cuckoo Search

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