This study addresses the optimal power flow (OPF) problem incorporating renewable energy sources (RES) and flexible alternating current transmission systems (FACTS) using the Chaos Game Optimization (cgo) algorithm. F...
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This study addresses the optimal power flow (OPF) problem incorporating renewable energy sources (RES) and flexible alternating current transmission systems (FACTS) using the Chaos Game Optimization (cgo) algorithm. Five objective functions are considered, which include minimizing generation costs, emissions, active power loss, voltage deviation, and enhancing voltage profiles. The OPF formulation considers the anticipated electricity production from wind turbines (WT) and photovoltaic (PV) units as dependent variables, while the voltage magnitude at WT and PV buses is treated as a control variable. Probabilistic models based on wind speed and solar irradiance are used to forecast the electrical output of WT and PV units. The proposed OPF methodology and solution method are validated on the IEEE 30-bus test network. This paper introduces and applies four optimization techniques inspired by biological and natural phenomena, namely cgo, Osprey Optimization algorithm (OOA), RIME algorithm, and Slime Mould algorithm (SMA), to address both single-OPF and multi-OPF objective problems in electric power networks. The suggested optimization approaches are tested under different operational scenarios, considering various combinations of FACTS, renewable energy sources (solar PV and wind), and their locations in the network. To predict wind and solar PV power generation, Weibull and lognormal probability density functions are utilized, respectively. The objective function accounts for reserve cost due to overestimation and penalty cost due to underestimation of intermittent solar and wind power. The results demonstrate that the cgo technique is more efficient than other methods in solving OPF instances.
This paper presents a novel application of the chaos game optimization (cgo) algorithm to optimally design PI controllers for power electronic interface circuits of offshore wind farms (OWF) consisting of a permanent ...
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This paper presents a novel application of the chaos game optimization (cgo) algorithm to optimally design PI controllers for power electronic interface circuits of offshore wind farms (OWF) consisting of a permanent magnet synchronous generator powered by a variable-speed wind turbine. The OWF is linked to the network via a high-voltage direct current (HVDC) transmission system. The cgo metaheuristic method is employed to finetune voltage source converter (VSC)-based HVDC transmission systems' proportional-integral controller gains. The study explores multiple strategies to extract the highest power from the system while ensuring stability under symmetrical and unsymmetrical fault conditions. The cgo algorithm consistently yields superior results to other algorithms, improving system regaining and stability post disturbances. Consequently, the technique enhances the dynamic and transient stability of the OWF. The detailed study is implemented using MATLAB/ Simulink.
In this paper, a new method for designing three-zone optical pupil filter is presented. The phase-only optical pupil filter and the amplitude-only optical pupil filters were designed. The first kind of pupil for optic...
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In this paper, a new method for designing three-zone optical pupil filter is presented. The phase-only optical pupil filter and the amplitude-only optical pupil filters were designed. The first kind of pupil for optical data storage can increase the transverse resolution. The second kind of pupil filter can increase the axial and transverse resolution at the same time, which is applicable in three-dimension imaging in confocal microscopy. (C) 2007 Elsevier GmbH. All rights reserved.
In this paper, it is applied the constrained global optimization (cgo) algorithm to design superresolution optical pupil filter. The cgo method is discussed in detail. Design consideration and solutions for applicatio...
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In this paper, it is applied the constrained global optimization (cgo) algorithm to design superresolution optical pupil filter. The cgo method is discussed in detail. Design consideration and solutions for applications such as confocal microscope and optical data storage are presented. The results show that the cgo algorithm is feasible and effective.
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