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Analyzing and evaluating floating PV systems in relation to traditional PV systems

Materials Today: Proceedings 2024年 103卷 342-345页

作者： Panda, Sampurna Panda, Babita Kumar, Rakesh Sharma, Kajal Pradhan, Ritambhara Kabat, Subash Ranjan ITM University Gwalior474001 India School of Electrical Engg KIIT University India Department of Electrical Engg IGIT Dhenkanal Odisha Sarang India Department of Electrical Engg Radhakrishna Institute of Technology and Engineering Odisha Khorda India

The various benefits of floating photovoltaic technology, a relatively new kind of energy generation, have piqued the interest of many people (FPVT). FPVT technology not only helps to lessen water loss due to evaporation, but it also helps to increase energy production. Science can't progress without the analysis of mechanical and electrical systems. Many researchers have looked at FPVT systems from different angles. This article undertakes a logical investigation and provides a thorough summary of the state of the art, elaborating on the numerous features and components of FPVT systems as a means of producing electricity. The advantages and disadvantages of each type have been compared in detail. A common way of capturing solar energy for use in electrical generation is the installation of solar arrays on the roof of a structure. However, although most homes only need 5-20 kW, most enterprises require 100 kW or more. On average, floating solar panels produce 11 percent more energy than their ground-based counterparts. Approximately 40% of the water in open reservoirs is lost to evaporation, according to studies. Covering only 30% of the water's surface can reduce evaporation by 49%. At 104 GW, the solar panel market will see a compound annual growth rate of 6%. Global photovoltaic capacity climbed by 27% in 2018, reaching 512 GW. This represents about 55% of the total capacity. This study also predicts that the FPVT system will help spur a 7.38 percent increase in solar technology, which is equal to 485.4 GW of additional installed electricity around the world. Copyright © 2024 Elsevier Ltd. All rights reserved.

关键词： Photovoltaics

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Detection of Lung Cancer Using CT-Scan Image - Deep Learning Approach 20

Detection of Lung Cancer Using CT-Scan Image - Deep Learning...

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20th OITS International Conference on Information technology, OCIT 2022

作者： Pal, Jashasmita Das, Subhalaxmi Tripathy, Jogeswar Radhakrishna Institute of Technology and Engineering Computer Science and Engineering Odisha India Odisha University of Technology and Research Computer Science and Engineering Odisha India

ISBN: (纸本)9781665493482

Cancer is a disease that comes in many forms and is the largest cause of death worldwide for men and women alike. Early detection of cancer has the highest chance of saving a person's life. Some of the procedures used to diagnose cancer include CT scans, bone scans, MRIs, PET (Positron Emission Tomography), ultrasound, and X-rays. Cancers such as lung cancer are among the deadliest worldwide, killing approximately five million people every year. This chapter focuses on lung cancer detection. The diagnosis of Cancer is usually a very difficult task in the biomedical and the bioinformatics field. Now, computed tomography (CT) scans can provide useful information for lung cancer diagnosis. In recent advances, deep learning approaches have improved to outperform humans in some tasks like classifying objects in images and also predicting better accuracy. Therefore, these techniques have been utilized in this model for the treatment of cancerous conditions. We detect lung cancer nodules from a given input and classify cancer as Adenocarcinoma, Large Cell Carcinoma, or Squamous Cell Carcinoma in our research. To detect the location of lung nodules, researchers used revolutionary deep learning approaches. In this paper basically, we used three deep learning case studies to diagnose lung cancer such as VGG16, INCEPTIONV3 and RESNET50 and also, we are discussing various measures for evaluating the performance of our model to get better accuracy. SS © 2022 IEEE.

关键词： Diagnosis

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Low Voltage Ride Through Improvement Using Power System Stabilizer for DFIG based Wind Energy Conversion System 2

Low Voltage Ride Through Improvement Using Power System Stab...

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2nd Odisha International Conference on Electrical Power engineering, Communication and Computing technology, ODICON 2022

作者： Mallick, Pradyumna Sharma, Renu Kabat, Subash Ranjan Soa Deemed to Be University Iter Department of Electrical Engineering Bhubaneswar India Radhakrishna Institute of Technology Department of Electrical Engineering Bhubaneswar India

ISBN: (纸本)9781665478397

This paper mainly aims to investigate the FRT capabilities of a wind power plant connected to the grid. The study provides an overview of the various ways utilized to improve Ride-Through (FRT) fault capabilities in doubly fed induction generator(DFIGs)-based transient wind turbines. This paper provides a comprehensive review of the various strategies employed to define the competencies of DFIG wind turbine-based systems with low-voltage travel (LVRT) (WT). Because the world uses about 20-25 % of renewable wind energy, the main DFIG-based type-III WT machine is connected to the grid without a digital power interface;consequently, it cannot manage terminal voltage or a reactive electrical output. Unique LVRT approaches were therefore proposed within this document based upon the implementation of other active interface technologies. Today, many techniques to overcome this low-voltage problem by failure are being developed. This work presents a comprehensive examination of LVRT techniques for DFIG-based WECS in order to build these active methods in order to close the gap in overall adaptive performance, controller operational complexity, and economic efficiency. This paper highlights the methods to increase the capacity of LVRT by applying a power system stabilizer to configure the relationship in 3 significant areas according to its grid inclusion (PSS). In this paper, FACT device connections are also used in WECS to investigate its efficiency and advantages, including PSS. © 2022 IEEE.

关键词： Wind turbines

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Improvement of LVRT Capability Using PSS for Grid Connected DFIG Based Wind Energy Conversion System 1

Improvement of LVRT Capability Using PSS for Grid Connected ...

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1st IEEE International Conference on Industrial Electronics: Developments and Applications, ICIDeA 2022

作者： Kabat, Subash Ranjan Pahadsingh, Sunita Jena, Kasinath Institute of Technology and Engineering Department of EE Radhakrishna Bhubaneswar India OUTR University Department of EE Bhubaneswar India Bhadrak Institute of Engineering and Technology Department of EE Bhadrak India

ISBN: (数字)9781665421492

ISBN: (纸本)9781665421492

This paper mainly aims to investigate the FRT capabilities of a wind power plant connected to the grid. The study introduces an overview of different approaches used to increase Ride-Through (FRT) fault capability in transient WTs based on DFIGs. This paper provides a comprehensive review of the various strategies employed to define the competencies of DFIG WT-based systems with low-voltage travel (LVRT) (WT). The main DFIG-based type-III WT machine is connected to the grid without a digital power interface as the world uses approximately 20-25% of renewable wind energy;consequently, it cannot manage terminal voltage or a reactive electrical output. Unique LVRT approaches were therefore proposed within this document based upon the implementation of other active interface technologies. Today, many techniques to overcome this low-voltage problem by failure are being developed. This article tries to establish these active methods to reduce the gap in overall adaptive performance, controller operational complexity, and economic efficiency by presenting a complete analysis of LVRT strategies for DFIG-based WECS. It also highlights the methods to increase the capacity of LVRT by applying a PSS to configure the relationship in 3 significant areas based on its grid inclusion. FACT device connections are also used in WECS to investigate its efficiency and advantages, including PSS. © 2022 IEEE.

关键词： Wind power

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Prediction of Growth Percentage of Sintered Compacts Using Neural Network Technique 2nd

Prediction of Growth Percentage of Sintered Compacts Using N...

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2nd International Conference on Recent Advances in Mechanical engineering Research and Development, ICRAMERD 2021

作者： Khuntia, Sujit Kumar Mohanty, Soumya Darshan Pani, Bibhuti Bhusan Das, Sankar Narayan College of Engineering Bhubaneswar Odisha Bhubaneswar India Radhakrishna Institute of Technology and Engineering Odisha Khurda India Veer Surendra Sai University of Technology Odisha Sambalpur India Siksha O Anusandhan Deemed to be University Odisha Bhubaneswar India

ISBN: (纸本)9789811690563

In this investigation, percentage of growth in volume of high-carbon content rapidly solidified alloyed ferrous powder sintered compacts has been found out at compaction pressures in the range of 300–700 MPa and sintering temperatures like 720, 850, and 1000 °C. It has been found that growth percentage increases with increase in sintering temperature at a constant compaction pressure and decreases with increase in compaction pressure at a particular sintering temperature. Then, an initiative has been taken to relate the compaction pressure and sintering temperature with growth percentage of the compacts. At the first stage, experimental data obtained during investigation have been used to train the network to develop mathematical models. These models have been used subsequently for prediction of growth percentage of the final products for different combinations of compaction pressures and sintering temperatures. Therefore, a new method has been developed to avoid repetition of experiments. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Annealing Atomization Ferrous powders Green density Growth percentage Sintered density

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Optimization of process parameters of friction stir welded joints of dissimilar aluminum alloys AA3003 and AA6061 by RSM

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Materials Today: Proceedings 2022年 56卷 1675-1683页

作者： Nait Salah, A. Mehdi, Husain Mehmood, Arshad Wahab Hashmi, Abdul Malla, Chandrabhanu Kumar, Ravi Laboratory Physical Chemistry of Processes and Materials Faculty of Science and Technology Hasan First University of Settat Morocco Department of Mechanical Engineering Delhi Technological University New Delhi India Department of Mechanical Engineering College of Engineering University of Buraimi Oman Advanced Manufacturing and Mechatronics Lab Department of Mechanical Engineering MNIT Jaipur India Department of Mechanical Engineering Radhakrishna Institute of Technology and Engineering Bhubaneswar India Department of Mechanical Engineering Institute of Technology and Management Meerut India

The friction stir welding (FSW) has excellent potential compared to other welding processes for joining similar or dissimilar aluminium alloys, which operate severe plastic mixing and high temperature to enhanced the mechanical properties of aluminium alloys. In the present work, RSM was used for the mathematical modelling of the FSW input parameters to fabricate AA3003 and AA6061. The empirical correlation was developed to predict the output responses of the FSWed joints at 95% confidence interval (C.I). The developed model reveals that the tool rotational speed (TRS), and welding speed (WS) were the dominating parameters. The increase in TRS, and decrease in WS leads to increased heat input to the welded joints. The C.I reveals that the ultimate tensile strength (UTS), and microhardness increased with increasing TRS. The optimized value of UTS, % strain nad microhardness was observed as 95.8 MPa, 12.18%, and 74.47 HV, respectively, while the optimized value of TRS, WS, and tilt angle (TA) were 1172 rpm, 57.44 mm-min−1, and 1.252° respectively. © 2021

关键词： Friction stir welding

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An Innovative Method for Optimizing Photovoltaic Array Design in Partially Shaded Environments 2

An Innovative Method for Optimizing Photovoltaic Array Desig...

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2nd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2023

作者： Shanmugapriya, G. Vasant Muralidhar, Nikale Solankce, Ravindra R Ranjan Kabat, Subash Jeevalatha, R. Patil, Pandit S. Agnel Institute of Technology and Design ECE Department Goa India Dada Patil Mahavidyalaya Karjat Department of Physics Maharashtra Karjat India Dr Rajendra Gode Institute of Technology and Research Amravati Department of Electronics and Telecommunication Engineering Maharastra Amravati India Radhakrishna Institute of Technology and Engineering Department of Electrical Engineering Odisha Bhubaneswar India Panimalar Engineering College Chennai City Campus Department of Chemistry Tamilnadu Chennai India R.C.Patel Institute of Technology Shirpur Department of Mechanical Engineering Maharashtra Dhule India

ISBN: (纸本)9798350397635

Several factors contribute to the gradual decrease in energy production from Photovoltaic (PV) systems. Partial shading is a key contributing element. Clouds and the presence of structures like buildings and tall trees cast partial shadows. Shade can reduce the quantity of energy generated by a photovoltaic system. Even in shady situations, it must be propped up to live. By removing the load from the PV array and taking a look at the open-circuit and short- circuit currents and voltages partial shading may be determined in the traditional manner. However, the suggested solution uses a regular camera to identify the partial shadowing without disconnecting the PV array from the load. There is a higher degree of sensitivity to variations in system performance using this approach. Photographs of the PV array are taken using a digital camera, and then partial shading is determined using standard image processing methods. During times of partial shadowing, the suggested technology reconfigures the electrical current in order to maximize power production by using the relay circuit. Partial shading data is used to create the control signals for the relays. The suggested approach involves interfacing a camera with MATLAB and immediately processing the collected pictures to identify the error or partial shading. The PV array is reconfigured for increased output power generation after reconfiguring signals are sent from a computer using a MATLAB-Arduino connection to the switch circuits in the array. The proposed approach has been tested in a solar PV system with a power output of 80 W, with results showing a 15 percent increase in output. It works well for 1-5-kilowatt solar photovoltaic power systems. © 2023 IEEE.

关键词： Sensitivity analysis

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Optimization of process parameters on the mechanical properties of AA6061/Al2O3 nanocomposites fabricated by multi-pass friction stir processing

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Materials Today: Proceedings 2022年 56卷 1995-2003页

作者： Mehdi, Husain Mehmood, Arshad Chinchkar, Ajay Hashmi, Abdul Wahab Malla, Chandrabhanu Mohapatra, Prabhujit Department of Mechanical Engineering Delhi Technological University New Delhi India Department of Mechanical Engineering College of Engineering University of Buraimi Oman Department of Food Science and Technology Department NIFTEM Haryana Sonipat India Advanced Manufacturing and Mechatronics Lab Department of Mechanical Engineering MNIT Jaipur India Department of Mechanical Engineering Radhakrishna Institute of Technology and Engineering Bhubaneswar India School of Advanced Sciences VIT University India

In the present investigation, the empirical correlation was successfully developed to predict the input and output responses of the multi-pass friction stir processing (FSP)/Al2O3 nanoparticles at a 95% confidence interval (C.I). The base metal AA6061 was characterized by nanoparticles Al2O3 within the structure of the coarse dendrite. These coarse and dendrites clusters were successfully broken by multi-pass FSP (MPFSP), refined the matrix grains and produced a homogenous microstructure in the stir zone (SZ). The developed model reveals that the nanoparticles Al2O3 and FSP passes were the dominating parameters to enhance the mechanical properties of the MPFSP/Al2O3. The ultimate tensile strength (UTS) and hardness were increased with increases in nanoparticles Al2O3 and the FSP passes. The optimized value of UTS, % strain and microhardness was observed as 220.07 MPa, 13.36%, and 98.44 HV, respectively, while the optimized value of nanoparticles Al2O3 and number of FSP passes were 9.65% and 1.72, respectively. © 2021

关键词： Microstructure

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Review on Scenario of Wind Power Generation and Control 1

Review on Scenario of Wind Power Generation and Control

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1st IEEE International Conference on Industrial Electronics: Developments and Applications, ICIDeA 2022

作者： Barick, Chandan Kumar Mohapatra, Bijaya Kumar Kabat, Subash Ranian Jena, Kasinath Ganthia, Bibhu Prasad Panigrahi, Chinmoy Kumar Anvi Anaya Engineering and Construction Cuttack India School of Electrical Engineering KIIT DU Bhubaneswar India Radhakrishna Institute of Technology and Engineering Department of EE Bhubaneswar India Bhadrak Institute of Engineering and Technology Department of EE Bhadrak India Indira Gandhi Institute of Technology Department of EE Dhenkanal India School of EE KIIT DU Bhubaneswar India

ISBN: (数字)9781665421492

ISBN: (纸本)9781665421492

This era brings RE ascent more popular in energy generations evolve response in enhancing concern towards environmental issues. Among all RE sources wind energy becomes more attractive in the sense of nonhazardous, cleanest and most cost-effective source of energy. This 20th century appeals the risk of shortage in fossil fuel, pollutions, carbon dioxide emissions and the surge of power demands. This develops a hope to settle our future energy demands to be recovered the wind energy can play the vital role in this aspect. Fulfilling the clean energy source, this renewable source can also be a part of growth in energy sector. This review work presents the worldwide generations of wind power and its control considering all constraints and parameters. © 2022 IEEE.

关键词： Wind power

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Enhancing Smart Grid Security: Detecting Electricity Theft through Ensemble Deep Learning 8

Enhancing Smart Grid Security: Detecting Electricity Theft t...

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8th International Conference on Communication and Electronics Systems, ICCES 2023

作者： Sowmya, C.S. Vibin, R. Mannam, Praveen Mounika, Lakkakula Kabat, Subash Ranjan Patra, Jyoti Prasad Presidency University Department of Ece Karnataka Bengaluru India Cms College of Engineering and Technology Department of Electrical and Electronics Engineering Tamilnadu Coimbatore India Kkr&ksr Institute of Technology and Sciences Dept of Electrical and Electronics Engineering Andhra-Pradesh Guntur India Sr University Warangal Lakkakula Mounika School of Computer Science and Artificial Intelligence Telangana India Radhakrishna Institute of Technology and Engineering Department of Electrical Engineering Odisha Bhubaneswar India Faculty Electrical Odisha University of Technology and Research Outr Government of Odisha Mahalaxmi Vihar Ghatikia Techno Campus Odisha Bhubaneswar India

ISBN: (纸本)9798350396638

Theft of electricity is a major problem that causes financial losses and inconsistent service for paying consumers for power distribution companies all over the world. The safety of the power grid depends on the ability to identify and stop electricity theft. The use of deep learning techniques has shown great promise in recent years, particularly in the areas of computer vision and natural language processing. This study recommends a random forest-based ensemble deep learning method for identifying cases of electricity theft. The proposed ensemble deep learning model leverages the best features of many kinds of deep learning architectures, including stacked Convolutional Neural Networks (CNN)and Long Short-Term Memory (LSTM). Each architecture has its own strengths when it comes to monitoring normal and abnormal electrical use for signs of theft. The final forecast is derived by adding together the predictions of the different models in the random forest ensemble. The ensemble model is trained using a massive dataset of energy usage records and theft information. Information about consumption patterns is extracted using feature engineering methods once the dataset has been preprocessed to get rid of noise and outliers. This preprocessed dataset is used to train the ensemble model, which then optimizes its parameters to reduce prediction errors. We use many measures, including accuracy, precision, recall, and F1-score, to assess the proposed ensemble deep learning model's performance. Experiments are run against both conventional machine learning methods and standalone deep learning models to prove that the ensemble method is superior. The findings demonstrate that the ensemble model is more accurate and has a greater detection rate, making it suitable for spotting energy theft. © 2023 IEEE.

关键词： Internet of things

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