7 research outputs found
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Comparative analysis of optimal damped and undamped passive filters using MIDACO-solver
Copyright © 2022 The Authors. Harmonic pollution is one of the challenging problems facing power networks recently due to the widespread non-linear loads and inverter-based renewables. In this regard, this work presents the optimal design of damped and undamped passive filters using a solver called Mixed-Integer Distributed Ant Colony Optimisation (MIDACO). This solver is employed to obtain an optimal design strategy for single-tuned passive power filters by investigating three primary criteria â minimisation of active power losses of the Theveninâs resistor, maximisation of the true power factor, and maximisation of the transmission efficiency. Several constrictions associated with the designed filters have been considered, in which the global maximum or minimum criterion was attained by retaining the quality factors of the designed filters within a particular range, damping harmonic resonance, achieving a permissible range of the power factor, limiting voltage harmonic distortion by complying with IEEE Std. 519â2014 restrictions. Besides, the performance limits of capacitors operating in distorted systems have been met while complying with IEEE Std. 18â2012. Further, the results obtained using the MIDACO solver in four different case studies are compared to those obtained using particle swarm optimisation and genetic algorithm. In addition, this work depicts the damping resistor of the inductance in the single-tuned filters. The benefits and drawbacks of damping over an undamped filter are discussed. Finally, the results validate the effectiveness of the MIDACO solver employed in this paper.The authors did not receive support from any organisation for the submitted work
Optimal PEM fuel cell model using a novel circle search algorithm
202309 bcvcVersion of RecordOthersKing Saud UniversityPublishe
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Optimal power flow of modern power systems comprising mixed renewable energy sources using improved chaotic hunger games search optimization algorithm
Copyright: © 2021 by the authors. This article introduces an application of the recently developed hunger games search (HGS) optimization algorithm. The HGS is combined with chaotic maps to propose a new Chaotic Hunger Games search (CHGS). It is applied to solve the optimal power flow (OPF) problem. The OPF is solved to minimize the generation costs while satisfying the systemsâ constraints. Moreover, the article presents optimal siting for mixed renewable energy sources, photovoltaics, and wind farms. Furthermore, the effect of adding renewable energy sources on the overall generation costs value is investigated. The exploration field of the optimization problem is the active output power of each generator in each studied system. The CHGS also obtains the best candidate design variables, which corresponds to the minimum possible cost function value. The robustness of the introduced CHGS algorithm is verified by performing the simulation 20 independent times for two standard IEEE systemsâIEEE 57-bus and 118-bus systems. The results obtained are presented and analyzed. The CHGS-based OPF was found to be competitive and superior to other optimization algorithms applied to solve the same optimization problem in the literature. The contribution of this article is to test the improvement done to the proposed method when applied to the OPF problem, as well as the study of the addition of renewable energy sources on the introduced objective function