Abstract
Nowadays, the power demand is increasing day by day due to the growth of the population and industries. The conventional power plant alone is incompetent to meet the consumer demand due to environmental concerns. In this present situation, the essential thing is to be find an alternate way to meet the consumer demand. In present days most of the developed countries concentrate to develop alternative resources and invest huge money for its research and development activities. Most renewable energy sources are naturally friendly sources such as wind, solar, fuel cell, and hydro/water sources. The results of power generation using renewable energy sources only depend on the availability of the resources. The availability of renewable energy sources throughout the day is variable due to fluctuations in the natural resources. This research work discusses two major renewable energy power generating sources: photovoltaic (PV) cell and fuel cell. Both of them provide foundations for power generation, so they are very popular because of their impressive performance mechanisms. The mentioned renewable energy-based power generating systems are static devices, so the power losses are generally ignorable as compared to line losses in the main grid. The PV and fuel cell (FC) power systems need a controller for maximum power generation during fluctuations in the input resources. Based on the investigation report, an algorithm is proposed for an advanced maximum power point tracking (MPPT) controller. This paper proposes a deep neural network- (DNN-) based MPPT algorithm, which has been simulated using MATLAB both for PV and for FC. The main purpose behind this paper has been to develop the latest DNN controller for improving the output power quality that is generated using a hybrid PV and fuel cell system. After developing and simulating the proposed system, we performed the analysis in different possible operating conditions. Finally, we evaluated the simulation outcomes based on IEEE 1547 and 519 standards to prove the system’s effectiveness.
Highlights
Power generation and transmission require substantially large quantities of fossil fuels
This paper has been written after detailed investigation and development of an advanced Deep Neural Network (DNN) controller-based maximum power point tracking (MPPT) controller algorithm for PV, which has been analyzed in various atmospheric conditions
The PEM cell system has been developed to manage the fuel flow pressure by DNN, which is primarily based on the algorithm to enhance the fuel cell efficiency
Summary
Power generation and transmission require substantially large quantities of fossil fuels. The fossil fuel-based power generation systems cannot meet the consumer demand in future due to limited availability of fuel. Environmental considerations will limit their usage as they emit more greenhouse gases This leads to warning levels and climate change, and the availability of fossil fuels is limited. For understanding the energy demand in the peak periods, such as at night, a hybrid system consisting of a fuel cell and a PV was developed without power storage. It has a few drawbacks such as the issue of integrating a microgrid, and besides, such a system is difficult to operate under
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