Solar Power System Assessments Using ANN and Hybrid Boost Converter Based MPPT Algorithm

Low-Stress and Optimum Design of Boost Converter for Renewable Energy Systems

This paper examines the design and analysis of DC–DC converters for high-power and low-voltage applications such as renewable energy sources (RESs) and comparisons between converters based on switch stresses and efficiency. The RESs including photovoltaic arrays and fuel cell stacks must have enhanced output voltages, such as 380 V DC in the case of a full bridge inverter or 760 V DC in the case of a half bridge inverter, in order to interface with the 220 V AC grid-connected power system. One of the primary difficulties in developing renewable energy systems is enhancing DC–DC converters’ efficiency to enable high step-up voltage conversion with high efficiency and low voltage stress. In the present work, the efficiency, current, and voltage stress of switches of an isolated Flyback boost converter, simple DC–DC Boost converter, and an Interleaved boost converter, are explored and studied relatively. The most suitable and optimized options with a high efficiency and low switching stress are investigated. The more suitable topology is designed and analyzed for the switch technology based on the Silicon-Metal Oxide Semiconductor Field Effect Transistor (Si-MOSFET) and the Gallium Nitride-High Electron Mobility Transistor (GaN-HEMT). The Analytical approach is analyzed in this paper based on efficiency and switching stress. It is explored that GaN HEMT based Flyback boost converter is the best. Finally, the future direction for further improving the efficiency of the proposed boost converter is investigated.

A Data-Driven Model to Forecast Multi-Step Ahead Time Series of Turkish Daily Electricity Load

It is critical to maintain a balance between the supply and the demand for electricity because of its non-storable feature. For power-producing facilities and traders, an electrical load is a piece of fundamental and vital information to have, particularly in terms of production planning, daily operations, and unit obligations, among other things. This study offers a deep learning methodology to model and forecast multistep daily Turkish electricity loads using the data between 5 January 2015, and 26 December 2021. One major reason for the growing popularity of deep learning is the creation of new and creative deep neural network topologies and significant computational advancements. Long Short-Term Memory (LSTM), Gated Recurrent Network, and Convolutional Neural Network are trained and compared to forecast 1 day to 7 days ahead of daily electricity load. Three different performance metrics including coefficient of determination (R2), root mean squared error, and mean absolute error were used to evaluate the performance of the proposed algorithms. The forecasting results on the test set showed that the best performance is achieved by LSTM. The algorithm has an R2 of 0.94 for 1 day ahead forecast, and the metric decreases to 0.73 in 7 days ahead forecast.