Optimal techno-economic assessment of isolated microgrid integrated with fast charging stations using radial basis deep learning.

The global transportation electrification commerce sector is now booming. Stakeholders are paying an increased attention to the integration of electric vehicles and electric buses into  the transportation networks. As a result, there is an urgent need to invest in public charging infrastructure, particularly for fast charging facilities.

Maximizing solar radiations of PV panels using artificial gorilla troops reinforced by experimental investigations.

This article's main objective is to maximize solar radiations (SRs) through the use of the gorilla troop algorithm (GTA) for identifying the optimal tilt angle (OTA) for photovoltaic (PV) panels. This is done in conjunction with an experimental work that consists of three 100 W PV panels tilted at three different tilt angles (TAs). The 28°, 30°, and 50° are the three TAs.

Consolidation of LVFRT capabilities of microgrids using energy storage devices.

The performance and dependability of distribution networks may be enhanced by the incorporation of microgrids (MGs). However, it is necessary to enhance low voltage fault-ride-through (LVFRT), which has the capacity to prevent abrupt grid disconnections during LV occurrences under problematic circumstances. In this study, a control strategy for energy storage elements (ESDs) which includes batteries and supercapacitors is proposed to enhance LVFRT under balanced and unbalanced faults.

Accurate emulation of steady-state and dynamic performances of PEM fuel cells using simplified models.

The current effort addresses a novel attempt to extract the seven ungiven parameters of PEMFCs stack. The sum of squared deviations (SSDs) among the measured and the relevant model-based calculated datasets is adopted to define the cost function. A Kepler Optimization Algorithm (KOA) is employed to decide the best values of these parameters within viable ranges.

Estimation of electrical transformer parameters with reference to saturation behavior using artificial hummingbird optimizer.

This paper offers an efficient tool to define the unknown parameters of electrical transformers. The proposed methodology is developed based on artificial hummingbird optimizer (AHO) to generate the best values of the transformer's unknown parameters. At initial stage, the parameters' extraction of the transformer electrical equivalent is adapted as an optimization function along with the associated operating inequality constraints.