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This paper presents a method for determining the optimum number, impedance, and locations of fault current limiter (FCL) to reduce the negative impact of distributed generations presence in the power systems such as increasing the short circuit current levels and improve the system reliability. In the considered method, we have modeled the FCL placement as an optimization problem while the objectives are; bus fault current difference, reliability, the number and impedance of FCLs. Moreover, to solve the proposed problem, a new multi-objective optimization algorithm based on firework algorithm namely multi-objective firework algorithm has been implemented. In the new algorithm, several iterations have been considered, and the non-dominated solutions are extracted and stored in an external repository in the iterations. Finally, a fuzzy clustering technique is used to control the size of the repository during the algorithm evolution. The proposed approach is tested on two different systems, namely, Roy Billinton Test System (RBTS) 2 and RBTS 4 test system. The obtained results demonstrate the effectiveness and feasibility of the new method. [ABSTRACT FROM PUBLISHER]

Metaheuristic algorithms are particularly useful for maximum power point tracking (MPPT) applications, because they can adapt to changes in operating conditions and effectively handle partial shading conditions. However, metaheuristic algorithms also have some limitations that need to be addressed to make them suitable for MPPT applications. The problems associated with metaheuristic algorithm-based MPPT applications include being trapped in local optima, slow convergence speed, shading condition variability, computational complexity and robustness. These problems lead to reduced efficiency in MPPT applications. In the literature, the solution of the aforementioned problems is partially addressed and some of them are solved via an additional irradiation sensor. The motivation of this study is to develop a control algorithm that covers all problems that have been partially solved in the literature and includes an original re-initialization modeling method in accordance with visual programing concept, without using any additional radiation sensor. The proposed control algorithm has the flexibility to be easily adapted to other metaheuristic algorithms and does not require any radiation sensors. The re-initialization model created via Matlab/Simulink and "Embedded Coder Support Package for TI C2000 Processors" allows easy tracking of the global maximum power point (GMPP) by detecting variable radiation conditions. The proposed model was implemented on the Cuckoo Search Algorithm (CSA) and verified through experimental studies carried out with a TI-TMS320f28069 microcontroller and PV emulator. The experimental results confirm that issue 1 is solved with 100%, issue 2 is solved with 99.5%, issue 3 is solved with 99.84%, and issue 4 is solved with 100% MPPT efficiency. [ABSTRACT FROM AUTHOR]

This article explores the capability of a differential evolution technique applied with a concept of random localization for solving the reactive power dispatch problem. Being a strong optimization technique, differential evolution is still suffering from the problem of slow convergence at the global minima region. Reactive power dispatch, on the other hand, is an active power loss minimization problem where bus voltages are to be kept within a system-stable margin. In this article, modified differential evolution is chosen as an optimization tool for studying few parameters to solve the reactive power dispatch problem in case of IEEE 14- and 30-bus systems. The applied modified differential evolution technique shows much improved results compared to basic differential evolution technique. The obtained results are also compared with different soft-computing techniques, including differential evolution and another modified differential evolution to substantiate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Motivated by the challenge of efficiently reconfiguring distribution networks for power loss reduction, this study presents an approach for finding a minimum loss radial configuration for a power network using ordinal optimization. Ordinal optimization relies on order comparison and goal softening to make the problem solution easier and the computation more efficient. The successful application of ordinal optimization to such a complex optimization problem required the investigation of several algorithmic parameters. The solution algorithm was implemented in a software package, where an acceptable solution is considered good enough if it is in the top m% of the solutions with a probability P. Testing it on 33- and 136-bus systems, minimal power loss results were obtained on the 33-bus system that are in the top 0.03% of the search space. Comparing the experimental results with other recently published methods showed the effectiveness of ordinal optimization for minimum loss calculations and motivated further studies in smart-grid-like scenarios, where the results obtained for different load levels were in the top 0.13% of the search space. [ABSTRACT FROM AUTHOR]