Green Energy Generation Using Renewable Energy Technologies

Baidya, Sanghita; Nandi, Champa

doi:10.1007/978-981-15-4246-6_16

Cited by 15 publications

(2 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The existing safety devices are not capable of effectively differentiating the various types of faults by discovering, categorizing, and localizing them that appear in the microgrids because of their dynamic nature, especially when the grid is operating in the islanded mode. There are numerous difficulties in research in the area of microgrid protection for the dynamic nature of the microgrid [4], [13], [15]. A cluster of decentralized/distributed energy systems that is a set of non-conventional energy resources (like wind, hydro and solar), storage the energy and supply it in the demand side (local customers) based on the need, is known as a microgrid [1].…”

Section: Introductionmentioning

confidence: 99%

“…[2], [3], [39]. Usually, the microgrid protection designs rely upon some vital factors such as the type of microgrid, topology of the microgrid, category of DG resources, communication type, details about the faults, relay model, data preprocessing and grounding method [2], [4], [5]. This makes a requirement for a reasonable and intelligent microgrid protection scheme that must be appropriate to integrate with the traditional protection units [10].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Data Processing Method for Artificial Neural Network ANN Based Micro grid Protection Model

Baidya

Nandi

2023

EAE

View full text Add to dashboard Cite

Effective fault detection and isolation technologies are very necessary for uninterrupted power supply and for making a flexible protection scheme. Almost all protection schemes in the power system are based on data exchange among protection units through a strong communication structure. Thus, it is important to deal with a large amount of data. Artificial Intelligence (AI) is one of the key factors in this regard. AI has several sections and Artificial Neural Network (ANN) is one of them. It is suggested to implement the ANN-based models while working with big data. The existing protection models are facing difficulties while trying to deal with big data. Thus ANN-based approaches have come into the front line in advanced power system networks. The performance of the ANN model is depending on the training of the data set. Hence in this work, we are focusing on preparing the data to provide input in the ANN model. The principal component analysis (PCA) method is applied here for reduced the dimension of a large number of data sets. The new data set is used to run the k-means clustering algorithm. It is shown that the clustering is more accurate with the processed data set by PCA. Therefore, the prepared data set is used to run the ANN model that has a smaller size with higher information and minimum computational time. This study shows the data preparation part to train the ANN model.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Data Processing Method for Artificial Neural Network ANN Based Micro grid Protection Model

Baidya

Nandi

2023

EAE

View full text Add to dashboard Cite

show abstract

Importance of Hybrid Energy System in Reducing Greenhouse Emissions

Das¹,

Bhattacharjee²,

Das³

2022

Renewable Energy Systems

View full text Add to dashboard Cite

Improving durability and efficiency in passive direct ethanol fuel cells using a biodegradable polyvinyl alcohol/epoxidized natural rubber membrane

Wani,

Zakaria,

Othman

et al. 2024

J of Applied Polymer Sci

View full text Add to dashboard Cite

In this study, a novel biodegradable polymer electrolyte membrane with reduced fuel crossover, high selectivity, and high conductivity compared with Nafion 117 membrane in direct ethanol fuel cells (DEFCs) has been reported. Herein, polyvinyl alcohol/epoxidized natural rubber (PVA/ENR) blend membranes were synthesized via a simple solution casting method and applied in DEFCs. The structural and physicochemical features of the PVA/ENR blend membranes were examined using FESEM, FTIR, XRD, water absorption, ethanol uptake, swelling ratio and oxidative stability. ENR enhances the chemical, structural, and mechanical characteristics of PVA, making it a valuable material in fuel cell applications. The incorporation of ENR into the PVA matrix results in a compact morphology, excessive multifunctional groups, low fuel crossover, and high selectivity. The optimum membrane thickness achieves the highest selectivity, reaching up to 12.32 × 104 S s cm−3 at 30°C. Additionally, the maximum power density achieved is 19.52 mW cm−2, surpassing that of the Nafion membrane, which is only 14.55 mW cm−2 at 90°C. Furthermore, this biodegradable membrane can sustain operation for 1000 h at 90°C, owing to its ability to maintain hydration for an extended period. This study represents the first attempt to combine PVA and ENR in fuel cells.

show abstract

Green Energy Generation Using Renewable Energy Technologies

Cited by 15 publications

References 21 publications

Data Processing Method for Artificial Neural Network ANN Based Micro grid Protection Model

Data Processing Method for Artificial Neural Network ANN Based Micro grid Protection Model

Importance of Hybrid Energy System in Reducing Greenhouse Emissions

Improving durability and efficiency in passive direct ethanol fuel cells using a biodegradable polyvinyl alcohol/epoxidized natural rubber membrane

Contact Info

Product

Resources

About