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This paper presents a simple strategy for controlling an interleaved boost converter that is used to reduce the current fluctuations in proton exchange membrane fuel cells, with high impact on the fuel cell lifetime. To keep the output voltage at the desired reference value under the strong fluctuations of the fuel flow rate, fuel supply pressure, and temperature, a neural network controller is developed and implemented using Matlab-Simulink (R2012b, MathWorks limited, London, UK). The advantage of this controller resides in its simplicity, where limited number of tests are carried out using Matlab-Simulink to construct it. To investigate the robustness of the proposed converter and the neural network controller, strong variations of the fuel flow rate, fuel supply pressure, temperature and air supply pressure are applied to both the fuel cell and the neural network controller of the converter. The simulation results show the effectiveness and the robustness of the both the proposed controller and converter to control the load voltage and minimize the current and voltage ripples. As a result of that, fuel cell current oscillations are considerably reduced on the one hand, while on the other hand, the load voltage is stabilized during transient variations of the fuel cell inputs. [ABSTRACT FROM AUTHOR]

One of the main goals of the designers of the power systems is the reduction of extra systems costs. The main part of these costs is related to energy loss, and the methods for loss reduction significantly lead to a cost reduction. The voltage regulators (VRs) can be used along the distribution feeders and the improvement of the network voltage profile results in the loss reduction of the system. However, the important problem is the installation location of this equipment to have the most profitability with the lowest cost. In this paper, the optimal placement of the VRs along the distribution systems is performed using intersect mutation differential evolution algorithm. The method aims to reduce loss and the primary investment and maintenance of the VRs. To this aim, the studies are performed on two scenarios for two standard distribution systems with 33 and 69 buses. A genetic algorithm (GA) is also utilized for the VRs placement to validate the results. The results show that the VRs reduce the loss to about 15% of the maximum system load. Moreover, the improved differential evolution algorithm performs better than the GA and makes the objective function in the minimum value. [ABSTRACT FROM AUTHOR]