Boost converters and multilevel inverters (MLI) are frequently included in low-voltage solar photovoltaic (PV) systems for grid integration. However, the use of an inductor-based boost converter makes the system bulky and increases control complexity. Therefore, the switched-capacitor-based MLI emerges as an efficient DC/AC voltage convertor with boosting capability. To make classical topologies more efficient and cost-effective for sustainable power generation, newer topologies and control techniques are continually evolving. This paper proposes a reduced-component-count five-level inverter design for generating stable AC voltages for sustainable grid-integrated solar photovoltaic applications. The proposed topology uses seven switching devices of lower total standing voltage (TSV), three diodes, and two DC-link capacitors to generate five-level outputs. By charging and discharging cycles, the DC capacitor voltages are automatically balanced. Thus, no additional sensors or control circuitry is required. It has inherent voltage-boosting capability without any input boost converter. A low-frequency-based half-height (HH) modulation technique is employed in the standalone system for better voltage quality. Extensive simulations are performed in a MATLAB/Simulink environment to estimate the performance of the proposed topology, and 17.58% THDs are obtained in the phase voltages. Using a small inductor in series or an inductive load, the current THD reduces to 8.23%. Better dynamic performance is also observed with different loading conditions. A miniature five-level single-phase laboratory prototype is developed to verify the accuracy of the simulation results and the viability of the proposed topology.

The use of multilevel inverters has gained popularity due to their ability to generate high-quality output voltage with reduced harmonic distortion. The performance of these multilevel inverters can be improved by using a low switching frequency modulation scheme as it enables precise amplitude and harmonic control in the output voltage. Determination of optimum switching angles for minimum total harmonic distortion generally requires complex algorithms with large number of iterations. This work presents a novel approach for calculating optimum switching angles at low frequencies that produces low total harmonic distortion in the output voltage. The calculation process uses only one controlling parameter that allows easy computation of optimum switching angles with a lesser number of iterations. The effectiveness of the calculated switching angles in producing lower total harmonic distortion is verified through simulation in MATLAB/Simulink platform as well as experimentally by developing a small-scale lab prototype of symmetric five-level inverter systems. For generalization, the proposed switching angle calculation technique is examined through different levels of inverter for minimum total harmonic distortions. The superiority of the proposed switching technique is verified through an extensive comparison with other popular low-frequency strategies which revealed that the proposed technique produces lower total harmonic distortions and higher fundamental voltage than those of the Equal Phase, Feed Forward, and Half Height methods. This feature of producing higher-quality fundamental voltage would make it a preferred choice for high power multi-level inverters.

The use of solar energy is growing in popularity across the globe as a clean and sustainable energy source. Nevertheless, integrating solar power into the grid and guaranteeing a steady supply of electricity is made more difficult by the weather patterns’ tendency to cause unpredictability in solar power generation. One potential solution to these issues is the use of machine learning (ML) techniques for short term solar photovoltaic (PV) power forecasting. This research looks into how well various machine learning algorithms work for short-term PV power forecasting. It also offers a summary of the variables that affect time-series data performance and suggests high-performance methods. Long Short-term Memory (LSTM), Support Vector Machines (SVM), and Artificial Neural Networks (ANN) were tested on an 88,494-item dataset with 20 features at a 15-minute resolution. As accuracy metrics, root mean square error (RMSE) and mean square error (MSE) show that ANNs and LSTM perform better than SVM. The superiority of the suggested methods is demonstrated by a careful comparison with recently published results. The reason for the effectiveness of ANNs and LSTMs is their capacity to represent the complex and non-linear relationships found in the data. Short-term solar power forecasting is complex, and SVMs are better equipped to handle linear relationships. These findings will be a useful guidance for those who intend to work in the field of PV power forecasting, even though further research is required to generalize this observation. GUB JOURNAL OF SCIENCE AND ENGINEERING, Vol 9(1), 2022 P 82-95

This work presents optimized decision tree-based ensemble machine learning models for predicting and quantifying the effects of greenhouse gas (GHG) emissions in Bangladesh. It aims to identify policy implications in response to significant environmental changes. The models analyze the emissions of CO₂, N₂O, and CH₄ from sectors including energy, industry, agriculture, and waste. We consider many parameters, including energy consumption, population, urbanization, gross domestic products, foreign direct investment, and per capita income. The data covers the period from 1971 to 2019. The model is trained using 80% of the dataset and validated using the remaining 20%. The hyperparameters, such as the number of estimators, maximum samples, maximum depth, learning rate, and minimum samples leaf, were optimized via particle swarm optimization. The models were tested, and their forecasts were extended till 2041. An examination of feature importance has identified energy consumption as a critical factor in greenhouse gas emissions, acknowledging the positive effects of clean energy in accordance with the clean development mechanism. The results demonstrate a robust model performance, with an R² score of approximately 0.90 for both the training and testing datasets. The bagging decision tree model showed the lowest mean squared error of 151.3453 and the lowest mean absolute percentage error of 0.1686. The findings of this study will help decision-makers understand the complex connections between socioeconomic conditions and the elements that contribute to greenhouse gas emissions. The discoveries will enable more precise monitoring of national greenhouse gas (GHG) inventories, allowing for focused efforts to mitigate climate change in Bangladesh.

Due to their high efficiency and low environmental impact, fuel cell power generation systems have emerged as promising alternatives to traditional energy sources. However, the integration of fuel cells into practical applications necessitates the development of efficient power conversion systems. dc-dc converters play a crucial role in optimizing the power flow between the fuel cell stack and the load. In this paper, a push-pull type high gain dc-dc converter is proposed which consists of a push-pull inverter, a rectifier circuit, and C-filter. The push pull inverter produces high frequency square wave output while the full bridge rectifier and C-filter convert this square wave output into desired dc voltage. The converter boosts the 12V dc input supply to 326 V, representing a gain factor of more than 27. The isolated coupling inductor used in push pull inverter produces high gain and provides electrical isolation. Besides, the proposed topology eliminates the use of bulky grid-connected power transformers that decreases the system size and cost. To evaluate the performance of the proposed topology, a number of simulations under varying loads were carried out in the MATLAB Simulink framework. Further-more, a scaled-down prototype of 160W at 12/326 V was developed to confirm the simulation results. The findings indicate that the suggested converter can be a viable option for the hydrogen fuel cell-based power conversion system. GUB JOURNAL OF SCIENCE AND ENGINEERING, Vol 10(1), 2023 P 37-52