Analysis of a wind-PV battery hybrid renewable energy system for a dc microgrid

Doshi, Karan; Harish, V. S. K. V.

doi:10.1016/j.matpr.2020.09.194

Cited by 20 publications

(8 citation statements)

References 16 publications

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“…The state of charge(SOC) can be defined by Equation (10) [ 48 ]:

Where P B is the charging or discharging power,

is the efficiency of charging or discharging.…”

Section: Methodsmentioning

confidence: 99%

Optimisation of a standalone photovoltaic electric vehicle charging station using the loss of power supply probability

Chen,

Ghosh,

Lopez

2023

Heliyon

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“…The state of charge(SOC) can be defined by Equation (10) [ 48 ]:

Where P B is the charging or discharging power,

is the efficiency of charging or discharging.…”

Section: Methodsmentioning

confidence: 99%

Optimisation of a standalone photovoltaic electric vehicle charging station using the loss of power supply probability

Chen,

Ghosh,

Lopez

2023

Heliyon

View full text Add to dashboard Cite

“…60,61 Wind energy is produced by harnessing the power of wind turbines, which gather and transform the kinetic energy of the wind. 62,63 These turbines are often built in regions with strong winds, such as mountain peaks or offshore in the ocean. The most prominent benefit of wind energy is that it can be used on a large scale.…”

Section: Wind Energymentioning

confidence: 99%

“…Wind energy is a RE source that is growing quickly and could meet a large proportion of current energy needs. It is a renewable and clean resource, and its use can cut GHG emissions, reduce reliance on fossil-based fuels, and generate employment and economic prosperity. , Wind energy is produced by harnessing the power of wind turbines, which gather and transform the kinetic energy of the wind. , These turbines are often built in regions with strong winds, such as mountain peaks or offshore in the ocean. The most prominent benefit of wind energy is that it can be used on a large scale.…”

Section: Renewable Energy Technologiesmentioning

confidence: 99%

Potential of Explainable Artificial Intelligence in Advancing Renewable Energy: Challenges and Prospects

Nguyen,

Tarełko,

Sharma

et al. 2024

Energy Fuels

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Modern machine learning (ML) techniques are making inroads in every aspect of renewable energy for optimization and model prediction. The effective utilization of ML techniques for the development and scaling up of renewable energy systems needs a high degree of accountability. However, most of the ML approaches currently in use are termed black box since their work is difficult to comprehend. Explainable artificial intelligence (XAI) is an attractive option to solve the issue of poor interoperability in black-box methods. This review investigates the relationship between renewable energy (RE) and XAI. It emphasizes the potential advantages of XAI in improving the performance and efficacy of RE systems. It is realized that although the integration of XAI with RE has enormous potential to alter how energy is produced and consumed, possible hazards and barriers remain to be overcome, particularly concerning transparency, accountability, and fairness. Thus, extensive research is required to address the societal and ethical implications of using XAI in RE and to create standardized data sets and evaluation metrics. In summary, this paper shows the potential, perspectives, opportunities, and challenges of XAI application to RE system management and operation aiming to target the efficient energy-use goals for a more sustainable and trustworthy future.

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“…The invertor's data acquisition system is used to record the data. Downloading data is handled by software that employs OPC HDA technology, and OPC uses fault detection algorithms for day-to-day review [17].…”

Section: Online/remote Supervision Of Pv Panelmentioning

confidence: 99%

An Effective Evaluation on Fault Detection in Solar Panels

et al. 2021

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The world’s energy consumption is outpacing supply due to population growth and technological advancements. For future energy demands, it is critical to progress toward a dependable, cost-effective, and sustainable renewable energy source. Solar energy, along with all other alternative energy sources, is a potential renewable resource to manage these enduring challenges in the energy crisis. Solar power generation is expanding globally as a result of growing energy demands and depleting fossil fuel reserves, which are presently the primary sources of power generation. In the realm of solar power generation, photovoltaic (PV) panels are used to convert solar radiation into energy. They are subjected to the constantly changing state of the environment, resulting in a wide range of defects. These defects should be discovered and remedied as soon as possible so that PV panels efficiency, endurance, and durability are not compromised. This paper focuses on five aspects, namely, (i) the various possible faults that occur in PV panels, (ii) the online/remote supervision of PV panels, (iii) the role of machine learning techniques in the fault diagnosis of PV panels, (iv) the various sensors used for different fault detections in PV panels, and (v) the benefits of fault identification in PV panels. Based on the investigated studies, recommendations for future research directions are suggested.

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Analysis of a wind-PV battery hybrid renewable energy system for a dc microgrid

Cited by 20 publications

References 16 publications

Optimisation of a standalone photovoltaic electric vehicle charging station using the loss of power supply probability

Optimisation of a standalone photovoltaic electric vehicle charging station using the loss of power supply probability

Potential of Explainable Artificial Intelligence in Advancing Renewable Energy: Challenges and Prospects

An Effective Evaluation on Fault Detection in Solar Panels

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