Research Publications
Browse peer-reviewed journal articles, conference papers, and scholarly publications. Use the filters below to refine results by type, author, year, or research area.
1.
Kumar, P. H.; Alluraiah, N. C.; Gopi, P.; Bajaj, M.; P., Sunil; Kalyan, C. N. S.; Blazek, V.
In: Results in Engineering, vol. 25, 2025, ISBN: 25901230 (ISSN), (0).
@article{344,
title = {Techno-economic optimization and sensitivity analysis of off-grid hybrid renewable energy systems: A case study for sustainable energy solutions in rural India},
author = {P. H. Kumar and N. C. Alluraiah and P. Gopi and M. Bajaj and Sunil P. and C. N. S. Kalyan and V. Blazek},
url = {https://www.sciencedirect.com/science/article/pii/S2590123024019170?via%3Dihub},
doi = {10.1016/j.rineng.2024.103674},
isbn = {25901230 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {Results in Engineering},
volume = {25},
publisher = {Elsevier B.V.},
abstract = {In the twenty-first century, global energy consumption is rapidly increasing, particularly in emerging nations, hastening the depletion of fossil fuel reserves and emphasizing the vital need for sustainable and renewable energy sources. This study aims to analyze hybrid renewable energy systems (HRESs) that use solid waste to generate power, focusing on difficulties linked to intermittent renewable sources using a techno-economic framework. Employing the HOMER Pro software, prefeasibility analysis is performed to meet the energy needs of an Indian community. System architecture optimization depends on factors like minimizing net present cost (NPC), achieving the lowest cost of energy (COE), and maximizing renewable source utilization. This study evaluates the technical, economic, and environmental feasibility of a hybrid renewable energy system (HRES) comprising a 400-kW solar photovoltaic (PV) array, a 100-kW wind turbine (WT), a 100-kW electrolyzer, 918 number of 12V batteries, a 200-kW converter, a 200-kW reformer, and a 15-kg hydrogen tank (H-tank). This optimal configuration has the lowest NPC of $26.8 million and COE of $4.32 per kilowatt-hour, and a Renewable Fraction (RF) of 100 %. It can provide a dependable power supply and satisfy 94 % of the daily onsite load demand, which is 1080 kilowatt-hours per day. The required electricity is sourced to load demand entirely from renewable energy at the given location. Additionally, the study highlights the benefits of HRES in solid waste management, considering technological advancements and regulatory frameworks. Furthermore, sensitivity analysis is conducted to measure economic factors that influence HRES, accounting for fluctuations in load demand, project lifespan, diesel fuel costs and interest rates. Installing an HRES custom-made to the local environmental conditions would provide a long-lasting, reliable, and cost-effective energy source. The results show that the optimal HRES system performs well and is a viable option for sustainable electrification in rural communities.},
note = {0},
keywords = {EE},
pubstate = {published},
tppubtype = {article}
}
In the twenty-first century, global energy consumption is rapidly increasing, particularly in emerging nations, hastening the depletion of fossil fuel reserves and emphasizing the vital need for sustainable and renewable energy sources. This study aims to analyze hybrid renewable energy systems (HRESs) that use solid waste to generate power, focusing on difficulties linked to intermittent renewable sources using a techno-economic framework. Employing the HOMER Pro software, prefeasibility analysis is performed to meet the energy needs of an Indian community. System architecture optimization depends on factors like minimizing net present cost (NPC), achieving the lowest cost of energy (COE), and maximizing renewable source utilization. This study evaluates the technical, economic, and environmental feasibility of a hybrid renewable energy system (HRES) comprising a 400-kW solar photovoltaic (PV) array, a 100-kW wind turbine (WT), a 100-kW electrolyzer, 918 number of 12V batteries, a 200-kW converter, a 200-kW reformer, and a 15-kg hydrogen tank (H-tank). This optimal configuration has the lowest NPC of $26.8 million and COE of $4.32 per kilowatt-hour, and a Renewable Fraction (RF) of 100 %. It can provide a dependable power supply and satisfy 94 % of the daily onsite load demand, which is 1080 kilowatt-hours per day. The required electricity is sourced to load demand entirely from renewable energy at the given location. Additionally, the study highlights the benefits of HRES in solid waste management, considering technological advancements and regulatory frameworks. Furthermore, sensitivity analysis is conducted to measure economic factors that influence HRES, accounting for fluctuations in load demand, project lifespan, diesel fuel costs and interest rates. Installing an HRES custom-made to the local environmental conditions would provide a long-lasting, reliable, and cost-effective energy source. The results show that the optimal HRES system performs well and is a viable option for sustainable electrification in rural communities.
2.
Gupta, J.; Gupta, V. K.
Versatility of various tungsten oxide nanostructures towards fostering electrochromic state of the art: a review Journal Article
In: Transition Metal Chemistry, 2025, ISBN: 03404285 (ISSN), (0).
@article{379,
title = {Versatility of various tungsten oxide nanostructures towards fostering electrochromic state of the art: a review},
author = {J. Gupta and V. K. Gupta},
doi = {10.1007/s11243-024-00628-0},
isbn = {03404285 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {Transition Metal Chemistry},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {Electrochromism is the process of changing a material’s optical glaze from coloured to bleached and vice versa by applying a reversible voltage. It is worth noting that across all transition metal oxides, tungsten oxide (WO3) has acquired a prime focus owing to its versatile electrochromic properties. High coloration efficiency, high diffusion coefficient (D), high cyclic stability, high optical modulation, and fast switching time are few properties which makes WO3, a versatile electrochromic material. Over the years, many scientists and researchers have been encouraged to extravagant their work on WO3, to realise its hidden electrochromic persona. Various nanostructured forms of WO3 have been studied till now. Every form of WO3 film defines a different electrochromic capability. In this review we try to portray versatility of various nanostructured forms of WO3 such as nanowires, nanorods, nanotrees, nanoflowers, nanoflakes, and nanoporous films towards acquiring electrochromic state of the art.},
note = {0},
keywords = {EE},
pubstate = {published},
tppubtype = {article}
}
Electrochromism is the process of changing a material’s optical glaze from coloured to bleached and vice versa by applying a reversible voltage. It is worth noting that across all transition metal oxides, tungsten oxide (WO3) has acquired a prime focus owing to its versatile electrochromic properties. High coloration efficiency, high diffusion coefficient (D), high cyclic stability, high optical modulation, and fast switching time are few properties which makes WO3, a versatile electrochromic material. Over the years, many scientists and researchers have been encouraged to extravagant their work on WO3, to realise its hidden electrochromic persona. Various nanostructured forms of WO3 have been studied till now. Every form of WO3 film defines a different electrochromic capability. In this review we try to portray versatility of various nanostructured forms of WO3 such as nanowires, nanorods, nanotrees, nanoflowers, nanoflakes, and nanoporous films towards acquiring electrochromic state of the art.
3.
Sravanthi, C.; Sekhar, J.; Alluraiah, Chinna; Dhanamjayulu, C.; Pujari, Harish; Khan, Baseem
An Overview of Remaining Useful Life Prediction of Battery Using Deep Learning and Ensemble Learning Algorithms on Data-Dependent Models Journal Article
In: International Transactions on Electrical Energy Systems, vol. 2025, pp. 2242749+, 2025.
@article{399,
title = {An Overview of Remaining Useful Life Prediction of Battery Using Deep Learning and Ensemble Learning Algorithms on Data-Dependent Models},
author = {C. Sravanthi and J. Sekhar and Chinna Alluraiah and C. Dhanamjayulu and Harish Pujari and Baseem Khan},
url = {https://doi.org/10.1155/etep/2242749},
doi = {10.1155/etep/2242749},
year = {2025},
date = {2025-01-01},
journal = {International Transactions on Electrical Energy Systems},
volume = {2025},
pages = {2242749+},
publisher = {John Wiley & Sons, Ltd},
abstract = {There has been expeditious development and significant advancements accomplished in the electrified transportation system recently. The primary core component meant for power backup is a lithium-ion battery. One of the keys to assuring the vehicle?s safety and dependability is an accurate remaining useful life (RUL) forecast. Hence, the exact prediction of RUL plays a vital part in the management of battery conditions. However, because of its complex working characteristics and intricate deterioration mechanism inside the battery, predicting battery life by evaluating exterior factors is exceedingly difficult. As a result, developing improved battery health management technology successfully is a massive effort. Because of the complexity of ageing mechanisms, a single model is unable to describe the complex deterioration mechanisms. As a result, this paper review is organised into three sections. First is to study about the battery degradation mechanism, the second is about battery data collections using mercantile and openly accessible Li-ion battery data sets and third is the estimation of battery RUL. The important performance parameters of distinct RUL forecast and estimation are categorised, analysed and reviewed. In the end, a brief explanation is given of the various performance error indices. This article classifies and summarises the RUL prediction by data-dependent models using machine learning (ML), deep learning (DL) and ensemble learning (EL) algorithms suggested in a last few years. The goal of this work in this context is to present an overview of all recent advancements in RUL prediction utilising all three data-driven models. This article is also followed by a categorisation of several types of ML, DL and EL algorithms for RUL prediction. Finally, this review-based study includes the pros and cons of the models.},
keywords = {EE},
pubstate = {published},
tppubtype = {article}
}
There has been expeditious development and significant advancements accomplished in the electrified transportation system recently. The primary core component meant for power backup is a lithium-ion battery. One of the keys to assuring the vehicle?s safety and dependability is an accurate remaining useful life (RUL) forecast. Hence, the exact prediction of RUL plays a vital part in the management of battery conditions. However, because of its complex working characteristics and intricate deterioration mechanism inside the battery, predicting battery life by evaluating exterior factors is exceedingly difficult. As a result, developing improved battery health management technology successfully is a massive effort. Because of the complexity of ageing mechanisms, a single model is unable to describe the complex deterioration mechanisms. As a result, this paper review is organised into three sections. First is to study about the battery degradation mechanism, the second is about battery data collections using mercantile and openly accessible Li-ion battery data sets and third is the estimation of battery RUL. The important performance parameters of distinct RUL forecast and estimation are categorised, analysed and reviewed. In the end, a brief explanation is given of the various performance error indices. This article classifies and summarises the RUL prediction by data-dependent models using machine learning (ML), deep learning (DL) and ensemble learning (EL) algorithms suggested in a last few years. The goal of this work in this context is to present an overview of all recent advancements in RUL prediction utilising all three data-driven models. This article is also followed by a categorisation of several types of ML, DL and EL algorithms for RUL prediction. Finally, this review-based study includes the pros and cons of the models.
4.
Gupta, J.; Shaik, H.; Gupta, V. K.; Sattar, S. A.
Perspective of Electrochromic Double Layer Towards Enrichment of Electrochromism: A Review Journal Article
In: Brazilian Journal of Physics, vol. 54, pp. 89+, 2024, ISBN: 01039733 (ISSN), (5).
@article{22,
title = {Perspective of Electrochromic Double Layer Towards Enrichment of Electrochromism: A Review},
author = {J. Gupta and H. Shaik and V. K. Gupta and S. A. Sattar},
doi = {10.1007/s13538-024-01463-5},
isbn = {01039733 (ISSN)},
year = {2024},
date = {2024-01-01},
journal = {Brazilian Journal of Physics},
volume = {54},
pages = {89+},
publisher = {Springer},
abstract = {Electrochromism is the exhibition of reversible optical property changes by certain materials upon administration of voltage across it. Tungsten oxide (WO3) finds a diverse range of applications because of its exceptional electrochromism. Amidst all applications, an electrochromic device (ECD) can be considered the most prominent application due to energy saving perspective. Although, WO3 itself has been noticed as an efficient electrochromic layer for EDCs; however, there exists a lot of space and ideas to enhance the electrochromism and hence the efficiency of an ECD. Recently, scientists are paying close attention to hybrid or composite films such as TiO2/WO3 and TiO2/V2O5. Such hybrid films are known as electrochromic double layer (ECDL). This review article strives to deepen our understanding of ECDL and assess their feasibility in the enrichment of electrochromism in ECDs by replacing a single electrochromic layer with an ECDL toward an energy-saving regime. Graphical Abstract: (Figure presented.). © The Author(s) under exclusive licence to Sociedade Brasileira de Física 2024.},
note = {5},
keywords = {EE},
pubstate = {published},
tppubtype = {article}
}
Electrochromism is the exhibition of reversible optical property changes by certain materials upon administration of voltage across it. Tungsten oxide (WO3) finds a diverse range of applications because of its exceptional electrochromism. Amidst all applications, an electrochromic device (ECD) can be considered the most prominent application due to energy saving perspective. Although, WO3 itself has been noticed as an efficient electrochromic layer for EDCs; however, there exists a lot of space and ideas to enhance the electrochromism and hence the efficiency of an ECD. Recently, scientists are paying close attention to hybrid or composite films such as TiO2/WO3 and TiO2/V2O5. Such hybrid films are known as electrochromic double layer (ECDL). This review article strives to deepen our understanding of ECDL and assess their feasibility in the enrichment of electrochromism in ECDs by replacing a single electrochromic layer with an ECDL toward an energy-saving regime. Graphical Abstract: (Figure presented.). © The Author(s) under exclusive licence to Sociedade Brasileira de Física 2024.
5.
Nagaraja, K. G.; Ramesh, H. R.
In: Electrical Engineering, vol. 106, pp. 5543-5556,, 2024, ISBN: 09487921 (ISSN), (0).
@article{30,
title = {Circulating current mitigation for renewable-based modular seven-level converter using deep learning-optimized fractional-order proportional resonant controller},
author = {K. G. Nagaraja and H. R. Ramesh},
doi = {10.1007/s00202-024-02275-1},
isbn = {09487921 (ISSN)},
year = {2024},
date = {2024-01-01},
journal = {Electrical Engineering},
volume = {106},
pages = {5543-5556,},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {Modular multi-level converters (MMCs) are often used for high and medium voltage applications. However, to reduce losses and costs, many researchers prefer a half-bridge converter. In addition, the half-bridge-based MMC is vulnerable in the event of an error, so the full-bridge MMC is used here to work with faulty network states. The losses and harmonics in the system could be reduced by using an appropriate arm voltage and circulating current control model. In order to operate the MMC in a grid-tied renewable system, both outer and inner loop control were performed. In order to realize outer-loop control, a fractional-order proportional–integral–derivative controller using a deep learning technique is proposed. An active power filter-based fractional-order proportional resonant controller with improved pulse width modulation achieves arm balancing with harmonic mitigated circulating current regulation. The simulation shows that the proposed method reduced the current and voltage harmonics to 71.56% and 10.42% through an improved control strategy based on pulse width modulation. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.},
note = {0},
keywords = {EE},
pubstate = {published},
tppubtype = {article}
}
Modular multi-level converters (MMCs) are often used for high and medium voltage applications. However, to reduce losses and costs, many researchers prefer a half-bridge converter. In addition, the half-bridge-based MMC is vulnerable in the event of an error, so the full-bridge MMC is used here to work with faulty network states. The losses and harmonics in the system could be reduced by using an appropriate arm voltage and circulating current control model. In order to operate the MMC in a grid-tied renewable system, both outer and inner loop control were performed. In order to realize outer-loop control, a fractional-order proportional–integral–derivative controller using a deep learning technique is proposed. An active power filter-based fractional-order proportional resonant controller with improved pulse width modulation achieves arm balancing with harmonic mitigated circulating current regulation. The simulation shows that the proposed method reduced the current and voltage harmonics to 71.56% and 10.42% through an improved control strategy based on pulse width modulation. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.
6.
Shivanaganna, N.; Shivamurthy, K. P.; Boddapati, V.
Optimal strategy for transition into nearly zero energy residential buildings: A case study Journal Article
In: Energy, vol. 307, pp. 132742+, 2024, ISBN: 03605442 (ISSN), (0).
@article{57,
title = {Optimal strategy for transition into nearly zero energy residential buildings: A case study},
author = {N. Shivanaganna and K. P. Shivamurthy and V. Boddapati},
doi = {10.1016/j.energy.2024.132742},
isbn = {03605442 (ISSN)},
year = {2024},
date = {2024-01-01},
journal = {Energy},
volume = {307},
pages = {132742+},
publisher = {Elsevier Ltd},
abstract = {This study suggests a customized design for turning an existing residential structure into a nearly zero energy building, along with a peak-power limiting strategy. The proposed work focuses on curtailing peak power demand of residential consumers through shifting the appliances connection time. The load scheduling is modeled as an optimization problem and solved using whale optimization algorithm and fuzzy logic. This is followed by reviewing the required size of the rooftop solar photovoltaic system for various types of residential consumers in a community using self-consumption and self-sufficiency-based indices. The proposed optimization algorithm effectively reduces the peak power drawn by 27 % and the peak-to-average ratio by 30.69 % on average in the residential community. Further, the rooftop PV size assessed substantiates that the energy drawn from the grid is nearly zero, attaining energy independence. © 2024 Elsevier Ltd},
note = {0},
keywords = {EE},
pubstate = {published},
tppubtype = {article}
}
This study suggests a customized design for turning an existing residential structure into a nearly zero energy building, along with a peak-power limiting strategy. The proposed work focuses on curtailing peak power demand of residential consumers through shifting the appliances connection time. The load scheduling is modeled as an optimization problem and solved using whale optimization algorithm and fuzzy logic. This is followed by reviewing the required size of the rooftop solar photovoltaic system for various types of residential consumers in a community using self-consumption and self-sufficiency-based indices. The proposed optimization algorithm effectively reduces the peak power drawn by 27 % and the peak-to-average ratio by 30.69 % on average in the residential community. Further, the rooftop PV size assessed substantiates that the energy drawn from the grid is nearly zero, attaining energy independence. © 2024 Elsevier Ltd
7.
Kumar, P. H.; Gopi, R. R.; Rajarajan, R.; Vaishali, N. B.; Vasavi, K.; P, Kumar
Prefeasibility techno-economic analysis of hybrid renewable energy system Journal Article
In: e-Prime - Advances in Electrical Engineering, Electronics and Energy, vol. 7, pp. 100443+, 2024, ISBN: 27726711 (ISSN), (14).
@article{75,
title = {Prefeasibility techno-economic analysis of hybrid renewable energy system},
author = {P. H. Kumar and R. R. Gopi and R. Rajarajan and N. B. Vaishali and K. Vasavi and Kumar P},
doi = {10.1016/j.prime.2024.100443},
isbn = {27726711 (ISSN)},
year = {2024},
date = {2024-01-01},
journal = {e-Prime - Advances in Electrical Engineering, Electronics and Energy},
volume = {7},
pages = {100443+},
publisher = {Elsevier Ltd},
abstract = {The feasibility of a hybrid renewable energy system for long-term rural electrification in Billerahalli village, Karnataka, India, is investigated in this paper. This paper presents a systematic methodology for planning and designing of an efficient hybrid system that incorporates techno-economic optimization analysis. Using HOMER PRO software, several hybridization situations are modelled and analyzed. In this design, electrolyzer and a hydrogen tank are included to reduce the need for batteries. The results shown that the optimal configuration system is a wind turbine, electrolyzer, hydrogen tank, converter, and LL1500-W battery (4 units of 800 kW/ 100 kW/ 100 kg/ 734 kW/ 500 units), with a low net present cost, cost of energy, a significant return on investment, and reductions in carbon emissions. This system offers a steady power supply by utilizing 100 % renewable sources, with a net present cost of $5.21 million and a relatively low energy cost of $0.25 per kilowatt-hour. The optimized hybrid system outperforms other system configurations in terms of cost-effectiveness and environmental performance. Concurrently, the sensitivity analysis emphasizes the system costs majorly depends on the unpredictability of solar radiation and wind speed, as well as changes in interest rates impacting future investment decisions. Finally, the study expects to provide the awareness of renewable energy potential usage in Billerahalli village and significant information for the efficient design and exploitation of hybrid renewable energy systems within the larger energy sector. © 2024 The Author(s)},
note = {14},
keywords = {EE},
pubstate = {published},
tppubtype = {article}
}
The feasibility of a hybrid renewable energy system for long-term rural electrification in Billerahalli village, Karnataka, India, is investigated in this paper. This paper presents a systematic methodology for planning and designing of an efficient hybrid system that incorporates techno-economic optimization analysis. Using HOMER PRO software, several hybridization situations are modelled and analyzed. In this design, electrolyzer and a hydrogen tank are included to reduce the need for batteries. The results shown that the optimal configuration system is a wind turbine, electrolyzer, hydrogen tank, converter, and LL1500-W battery (4 units of 800 kW/ 100 kW/ 100 kg/ 734 kW/ 500 units), with a low net present cost, cost of energy, a significant return on investment, and reductions in carbon emissions. This system offers a steady power supply by utilizing 100 % renewable sources, with a net present cost of $5.21 million and a relatively low energy cost of $0.25 per kilowatt-hour. The optimized hybrid system outperforms other system configurations in terms of cost-effectiveness and environmental performance. Concurrently, the sensitivity analysis emphasizes the system costs majorly depends on the unpredictability of solar radiation and wind speed, as well as changes in interest rates impacting future investment decisions. Finally, the study expects to provide the awareness of renewable energy potential usage in Billerahalli village and significant information for the efficient design and exploitation of hybrid renewable energy systems within the larger energy sector. © 2024 The Author(s)
8.
Ranjitha, R.; Agalya, V.; Archana, K.
Diabetes Prediction by Artificial Neural Network Proceedings
Springer Science and Business Media Deutschland GmbH, vol. 311, 2022, ISBN: 23673370 (ISSN); 978-981165528-9 (ISBN), (2).
@proceedings{113,
title = {Diabetes Prediction by Artificial Neural Network},
author = {R. Ranjitha and V. Agalya and K. Archana},
doi = {10.1007/978-981-16-5529-6_76},
isbn = {23673370 (ISSN); 978-981165528-9 (ISBN)},
year = {2022},
date = {2022-01-01},
journal = {Lecture Notes in Networks and Systems},
volume = {311},
pages = {1011-1019,},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {Diabetes is a syndrome caused by the hyperglycemia of multiple chronic combined with the variation of carbohydrate, fat and protein metabolism, which impact the improper discharge of insulin and the proper usage of insulin in the human body or both. Diabetes affects more than 463 million people globally. By 2020, 88 million people in Southeast Asia are suffering from this illness. According to a report by the International Federation (IDF), India has 77 million people out of 88 million affected individuals. Therefore, diabetes is one of the growing health concerns in India and has no persistent heal. Therefore, rapid diabetic perception is essential, and it can be done inexpensively through the computation method. The research is carried out for the detection of diabetes by artificial neural network (ANN). Here, the prediction is based on back propagation algorithm of an ANN model for diabetes analysis. For training and testing, the dataset was obtained from the UCI machine learning repository’s Pima Indian Diabetes Dataset (PIDD). The network was built with different neurons at various epochs and observed that the accuracy reaches up to 99.23%. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.},
note = {2},
keywords = {EE},
pubstate = {published},
tppubtype = {proceedings}
}
Diabetes is a syndrome caused by the hyperglycemia of multiple chronic combined with the variation of carbohydrate, fat and protein metabolism, which impact the improper discharge of insulin and the proper usage of insulin in the human body or both. Diabetes affects more than 463 million people globally. By 2020, 88 million people in Southeast Asia are suffering from this illness. According to a report by the International Federation (IDF), India has 77 million people out of 88 million affected individuals. Therefore, diabetes is one of the growing health concerns in India and has no persistent heal. Therefore, rapid diabetic perception is essential, and it can be done inexpensively through the computation method. The research is carried out for the detection of diabetes by artificial neural network (ANN). Here, the prediction is based on back propagation algorithm of an ANN model for diabetes analysis. For training and testing, the dataset was obtained from the UCI machine learning repository’s Pima Indian Diabetes Dataset (PIDD). The network was built with different neurons at various epochs and observed that the accuracy reaches up to 99.23%. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
9.
Archana, K.; Ranjitha, R.
Solar Energy Utilization for Domestic Lighting Proceedings
Institute of Electrical and Electronics Engineers Inc., 2021, ISBN: 978-172818501-9 (ISBN), (1).
@proceedings{142,
title = {Solar Energy Utilization for Domestic Lighting},
author = {K. Archana and R. Ranjitha},
doi = {10.1109/ICICT50816.2021.9358571},
isbn = {978-172818501-9 (ISBN)},
year = {2021},
date = {2021-01-01},
journal = {Proceedings of the 6th International Conference on Inventive Computation Technologies, ICICT 2021},
pages = {394-399, 9358571+},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {In the present scenario, solar energy as a source of renewable energy is used to satisfy the increasing energy demands. The Photo Voltaic (PV) panel is used as the major supply of power and the battery is used as the substitute supply of power for the suitable power converter in the present work. The proposed power converter has the potential to operate at four different states which mainly focuses on the solar power availability, battery charge level, and requirements of the load. A proposed control method uses the P O method to achieve Maximum Power Point Tracking (MPPT). A LED lamp is used as a load, which gets the power either from battery or PV or from both based on the state at which the power converter operates. The simulation of the overall system is shown through MATLAB/SIMULINK. © 2021 IEEE.},
note = {1},
keywords = {EE},
pubstate = {published},
tppubtype = {proceedings}
}
In the present scenario, solar energy as a source of renewable energy is used to satisfy the increasing energy demands. The Photo Voltaic (PV) panel is used as the major supply of power and the battery is used as the substitute supply of power for the suitable power converter in the present work. The proposed power converter has the potential to operate at four different states which mainly focuses on the solar power availability, battery charge level, and requirements of the load. A proposed control method uses the P O method to achieve Maximum Power Point Tracking (MPPT). A LED lamp is used as a load, which gets the power either from battery or PV or from both based on the state at which the power converter operates. The simulation of the overall system is shown through MATLAB/SIMULINK. © 2021 IEEE.
10.
Sujatha, B. G.; Anitha, G. S.
Enhancement of PQ in grid connected PV system using hybrid technique Journal Article
In: Ain Shams Engineering Journal, vol. 9, pp. 869-881,, 2018, ISBN: 20904479 (ISSN), (25).
@article{207,
title = {Enhancement of PQ in grid connected PV system using hybrid technique},
author = {B. G. Sujatha and G. S. Anitha},
doi = {10.1016/j.asej.2016.04.007},
isbn = {20904479 (ISSN)},
year = {2018},
date = {2018-01-01},
journal = {Ain Shams Engineering Journal},
volume = {9},
pages = {869-881,},
publisher = {Ain Shams University},
abstract = {This paper proposed a hybrid technique based on power quality (PQ) enhancement in grid connected Photovoltaic (PV) system. The hybrid technique is the combined performance of both the Radial Basis Function Neural Network (RBFNN) and Proportional Integral (PI) controller. The primary intention of the proposed method is to predict the adaptive gain parameters for both the normal and abnormal environment in the grid side. In the proposed method, the RBFNN is trained with input parameters such as grid power variations and the target gain parameters of the PI controller. During the testing time, the RBFNN predicts the gain parameters of the PI controller as per the grid side parameter variation and the PQ of the grid side has been enhanced. Then the proposed method is implemented in the MATLAB/Simulink platform and the effectiveness is examined by comparison analysis with the conventional techniques. © 2016},
note = {25},
keywords = {EE},
pubstate = {published},
tppubtype = {article}
}
This paper proposed a hybrid technique based on power quality (PQ) enhancement in grid connected Photovoltaic (PV) system. The hybrid technique is the combined performance of both the Radial Basis Function Neural Network (RBFNN) and Proportional Integral (PI) controller. The primary intention of the proposed method is to predict the adaptive gain parameters for both the normal and abnormal environment in the grid side. In the proposed method, the RBFNN is trained with input parameters such as grid power variations and the target gain parameters of the PI controller. During the testing time, the RBFNN predicts the gain parameters of the PI controller as per the grid side parameter variation and the PQ of the grid side has been enhanced. Then the proposed method is implemented in the MATLAB/Simulink platform and the effectiveness is examined by comparison analysis with the conventional techniques. © 2016
