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In an attempt to meet the global demand, renewable energy systems (RES) have gained an interest in it due to the availability of the resources, especially solar photovoltaic system that has been an importance since many years because of per watt cost reduction, improvement in efficiency, and abundant availability. Photovoltaic system in remote and rural areas is very useful where a grid supply is unavailable. In this scenario, power electronic converters are an integral part of the renewable energy systems particularly for electronic devices which are operated from renewable energy sources and energy storage system (fuel cell and batteries). In this article, a new topology of charge pump capacitor (CPC) which is based on high voltage gain technique DC-DC boost converter (DCBC) with dynamic modeling is proposed. To testify the efficacy of the introduced topology, a prototype has been developed in a laboratory, where input was given 10VDC and 80VDC output voltage achieved at the load side. Furthermore, the experimental result shows that the voltage stress of MOSFET switches is very less in comparison with the conventional boost converter with the same parameters as the proposed converter. [ABSTRACT FROM AUTHOR]

In developing countries, the power production is properly less than the request of power or load, and sustaining a system stability of power production is a trouble quietly. Sometimes, there is a necessary development to the correct quantity of load demand to retain a system of power production steadily. Thus, Small Hydropower Plant (SHP) includes a Kaplan turbine was verified to explore its applicability. This paper concentrates on applying on Artificial Neural Networks (ANNs) by approaching of Feed-Forward, Back-Propagation to make performance predictions of the hydropower plant at the Himreen lake dam-Diyala in terms of net turbine head, flow rate of water and power production that data gathered during a research over a 10 year period. The model studies the uncertainties of inputs and output operation and there's a designing to network structure and then trained by means of the entire of 3570 experimental and observed data. Furthermore, ANN offers an analyzing and diagnosing instrument effectively to model performance of the nonlinear plant. The study suggests that the ANN may predict the performance of the plant with a correlation coefficient ( R ) between the variables of predicted and observed output that would be higher than 0.96. [ABSTRACT FROM AUTHOR]

An increase in demand for renewable energy resources, energy storage technologies, and electric vehicles requires high-power level DC-DC converters. The DC-DC converter that is suitable for high-power conversion applications (i.e., resonant, full-bridge or the dual-active bridge) requires magnetic transformer coupling between input and output stage. However, transformer design in these converters remains a challenging problem, with several non-linear scaling issues that need to be simultaneously optimized to reduce losses and maintain acceptable performance. In this paper, a new transformer-less high step-up boost converter with a charge pump capacitorand capacitor-inductor-diode CLD cell is proposed using dynamic modeling. The experimental and simulation results of the proposed converter are carried out in a laboratory and through Matlab Simulink, where 10 V is given as an input voltage, and at the output, 100 V achieved in the proposed converter. A comparative analysis of the proposed converter has also been done with a conventional quadratic converter that has similar parameters. The results suggest that the proposed converter can obtain high voltage gain without operating at the maximum duty cycle and is more efficient than the conventional converter. [ABSTRACT FROM AUTHOR]

Power and freshwater are two energy-intensive products, which consume a huge amount of fossil fuels. It is important to supply the aforementioned products using renewable energy sources due to the depletion of fossil fuel resources and environmental issues. This paper investigates the exergy and exergy-economic analysis of water and power production using a small-scale combined cycle encompasses the concentrated photovoltaic thermal (CPVT) solar collectors, a Kalina cycle (KC), and a humidification-dehumidification (HDH) desalination unit. An exergo-economic parametric analysis was first investigated to determine the influence of some pertinent parameters on the exergy efficiency, and specific unit cost of the products. In the second stage, two intelligent forecasting approaches based on the artificial neural network (ANN) and improved particle swarm optimization (PSO) algorithms were utilized for predicting the performance metrics of the studied system. The system was supposed to work at half of the year's hour. The results demonstrated that the shares of CPVT, generator, humidifier, dehumidifier, and condenser in exergy destruction are 84 %, 6 %, 3 %, 2.5 %, and 2 %, respectively. Moreover, the exergy efficiency, and specific unit cost of the products, unit cost of electricity, and unit cost of the fresh water at the design condition were obtained as 23.23 %, 0.0806 $/kWh, 5.44 $/m3, and 31.15 $/GJ, respectively. Besides, the most effective parameter on the exergy efficiency and the specific unit cost of the products was the solar beam radiation, the increment in which from 300 W/m2 to 1000 W/m2 improved the exergy efficiency by 15.21 % and reduced the specific unit cost of the products by 63.16 %. In addition, the increase in the condenser pressure from 15 bar to 22 bar and the generator pinch point temperature difference from 5 °C to 15 °C reduced the exergy efficiency by 8.13 % and 4.03 %, respectively, leading to increasing the specific unit cost of the products by 1.30 % and 4.30 %. The results of modeling showed that hybrid ANN-IPSO models provide the most accurate prediction, highest tendency, and agreement to observation as compared to ANN in terms of (R2| exergy efficiency = 0.9903 and R2| specific unit cost of products = 0.9948) and (RMSE| exergy efficiency = 0.0010 and RMSE| specific unit cost of products = 0.9684). [ABSTRACT FROM AUTHOR]