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All Journal International Journal of Power Electronics and Drive Systems (IJPEDS) International Journal of Informatics and Communication Technology (IJ-ICT) International Journal of Applied Power Engineering (IJAPE) Indonesian Journal of Electrical Engineering and Computer Science
Salkuti, Surender Reddy
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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A painstaking analysis of various conventional and AI based MPPT approaches to the PV framework Rout, Kishrod Kumar; Mishra, Debani Prasad; Mishra, Sivkumar; Salkuti, Surender Reddy
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 14, No 4: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v14.i4.pp2338-2346

Abstract

Electricity is the best technological advancement ever. Power is now used for everything in our culture because of how far it has come. Quiddity of life would not be possible without it. We know that the sole free hotspot for the PV module in our environmental factors is the sun. The PV cell changes sun-powered energy into electrical energy when the sun radiates on it. At the point when you produce power with the sunlight-based chargers, no ozone-harming substance emanations are placed into the climate. Since, the sun creates more energy than any manmade process at any point requires so as a result, in this article we will look at a variety of ways as well as a successful MPP strategy with high efficacy. It encompasses incremental conductance, perturbs & observe, and fuzzy logic approaches. A boost (DC-DC) converter ameliorates the likeness between a solar array and storage or power grid. In many solar-producing systems, DC/DC converters assist in surveilling the utmost power point by acting as a bridge betwixt load and solar panel. The intent of achieving the highest possible electricity can be done by calibrating the load to compare the current and voltage of a photovoltaic cell.
Solana blockchain technology: a review Mishra, Debani Prasad; Behera, Sandip Ranjan; Behera, Subhashis Satyabrata; Patro, Aditya Ranjan; Salkuti, Surender Reddy
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 13, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v13i2.pp197-205

Abstract

The introduction of a review article on the Solana blockchain is critical to setting the stage for the arguments and evidence to follow. This paragraph will provide context to the reader by discussing the current state of blockchain technology and introducing Solana as a potential solution. Blockchain technology has the potential for countless applications, ranging from financial transactions to secure data storage. However, existing blockchain systems suffer from scalability issues, were confirmation times and network congestion limit transaction volumes. This review paper on the Solana blockchain is valuable for those seeking an in-depth understanding of the design and efficacy. Given the increasing number of blockchain technologies available in the market, potential adopters face the challenge of selecting the most suitable blockchain network for their specific use case. A well-constructed review provides necessary information on the functioning of the technology, including its strengths and limitations. It also enables readers to compare various blockchain technologies and judge their suitability for their specific needs. Therefore, reviews like this one play a crucial role in helping to advance blockchain technology by driving the adoption of superior blockchain networks.
ChatGPT's effect on the job market: how automation affects employment in sectors using ChatGPT for customer service Mishra, Debani Prasad; Agarwal, Nandini; Shah, Dhruvi; Salkuti, Surender Reddy
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 13, No 1: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v13i1.pp116-122

Abstract

A significant language model called ChatGPT, created by OpenAI, has gained attention in artificial intelligence (AI) and natural language processing. This research paper aims to provide an in-depth analysis of ChatGPT and its potential impact on the future, including its limitations, pros and cons, and how it came to be. This paper first provides a brief overview of ChatGPT, including its architecture and training process, and how it differs from previous language models. It then delves into the model's limitations, such as its lack of common sense and susceptibility to discrimination or biases present in the data it was trained on. This paper also explores the potential benefits of ChatGPT, such as its ability to generate human-like text, its potential use in customer service, and its potential impact on the job market. The paper also discusses the ethical and social implications of ChatGPT, such as the potential for the model to perpetuate biases and the need for transparency and accountability in its deployment. Finally, the paper concludes by discussing the future of ChatGPT and similar language models and their potential impact on various industries and society as a whole. Overall, this research paper provides a comprehensive and nuanced survey of the AI tool ChatGPT and its potential impact on the future.
Alzheimer’s disease diagnosis using convolutional neural networks model Samanvi, Potnuru; Agrawal, Shruti; Mallick, Soubhagya Ranjan; Lenka, Rakesh Kumar; Palei, Shantilata; Mishra, Debani Prasad; Salkuti, Surender Reddy
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 13, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v13i2.pp206-213

Abstract

The global healthcare system and related fields are experiencing extensive transformations, taking inspiration from past trends to plan for a technologically advanced society. Neurodegenerative diseases are among the illnesses that are hardest to treat. Alzheimer’s disease is one of these conditions and is one of the leading causes of dementia. Due to the lack of permanent treatment and the complexity of managing symptoms as the severity grows, it is crucial to catch Alzheimer’s disease early. The objective of this study was to develop a convolutional neural network (CNN)-based model to diagnose early-stage Alzheimer’s disease more accurately and with less data loss than methods previously discovered. CNN, is adept at processing and recognising images and has been employed in various diagnostic tools and research in the healthcare sector, showing limitless potential. Convolutional, pooling and fully linked layers are the common layers that make up a CNN. In this paper, five CNN modelswere randomly chosen (ResNet, DenseNet, MobileNet, Inception, and Xception) and were trained. ResNet performed the best and was chosen to undergo additional modifications to improve accuracy to 95.5%. This was a remarkable achievement that made us hopeful for the performance of this model in larger datasets as well as other disease detection.
Cost optimization of electricity in energy storage system by dynamic programming Mishra, Debani Prasad; Routray, Bhabesh; Pattanayak, Nitesh; Shekhar, Priyansu; Behera, Pratyus Ranjan; Salkuti, Surender Reddy
International Journal of Applied Power Engineering (IJAPE) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v13.i2.pp343-350

Abstract

This paper presents a dynamic programming solution for the cost optimization of an electric storage system. The objective is to minimize the total cost of meeting electricity demand over a specified time interval, considering energy constraints and costs. The proposed algorithm efficiently determines the optimal energy discharge and charge strategies for the storage system, resulting in reduced overall costs. The effectiveness and efficiency of the algorithm are demonstrated through various test cases, highlighting its potential for real-world applications in energy storage systems and electric grid management. It also provides an overview of different types of electrical storage systems, review recent research on optimization techniques for energy storage, and examines recent studies on the optimization of electrical storage systems for specific applications, such as peak load shaving and grid stability. Through this comprehensive analysis, we hope to shed light on the current state of the field and identify areas for further research and improvement.
Exploratory data analysis for electric vehicle driving range prediction: insights and evaluation Mishra, Debani Prasad; Kumar, Prince; Rai, Priyanka; Kumar, Ayush; Salkuti, Surender Reddy
International Journal of Applied Power Engineering (IJAPE) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v13.i2.pp474-482

Abstract

One of the biggest challenges of electric vehicle (EV) users has been predicting the amount of driving time their vehicles will have on one battery charge. Planning a trip and reducing range anxiety depends on an accurate range estimate. This study aims to anticipate the EV driving range using machine learning methods. In this research, several regression models for predicting EV driving range will be developed and compared. A real-world dataset comprising various factors affecting EV range, such as power, trip distance, energy consumption, driving style, and environmental factors, is used for analysis. The dataset is preprocessed using exploratory data analysis methods to manage missing values, outliers, and categorical variables. The findings of this study contribute to the expanding area of EV range prediction and provide EV buyers, producers, and regulators with insightful information. The user experience can be improved, EV adoption can be boosted, and effective charging infrastructure design is made possible with accurate range prediction. The study also highlights the importance of model selection and data pretreatment in making accurate predictions.
Revolutionizing domestic solar power systems with IoT-enabled Blockchain technology Jhunjhunwalla, Drishana; Mishra, Debani Prasad; Hembram, Dashmat; Salkuti, Surender Reddy
International Journal of Applied Power Engineering (IJAPE) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v13.i1.pp255-262

Abstract

Solar power systems in homes have become the need of the hour due to the crisis of fossil fuels. Also, it is a useful way of rural electrification and cutting down on running electricity costs. This paper discusses a 26-kW solar power system for powering homes along with IoT-based monitoring. The proposed system is expected to be low in cost and highly efficient. The system can also be used as a battery backup without solar power. The emergence of Blockchain technology is poised to revolutionize the sharing of information by providing a means of building trust in decentralized settings without the reliance on intermediaries. This technological breakthrough has the potential to transform several industries, including the internet of things (IoT). In addition to Blockchain, IoT has also been able to address some of its limitations by utilizing innovative technologies like big data and cloud computing. For security, Blockchain as a decentralized application will be used. Each block typically contains the transaction data, and power consumption data which can’t be tampered with even if changing all subsequent blocks, which is expensive to do so.
Fault detection and diagnosis of electric vehicles using artificial intelligence Mishra, Debani Prasad; Padhy, Somya Siddharth; Pradhan, Partha Sarathi; Gupta, Shubh; Senapati, Asutosh; Salkuti, Surender Reddy
International Journal of Applied Power Engineering (IJAPE) Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v13.i3.pp653-660

Abstract

Electric vehicle (EV) performance is greatly influenced by the motor drive system's stability, efficiency, and safety. With the increased usage of electric vehicles, fault detection and diagnostics (FDD) of the motor drive system has become an important topic of research. In recent years, there has been a lot of interest in artificial intelligence (AI) approaches employed in FDD. This paper provides an overview of the application of AI in defect detection for electric vehicles. The FDD method is divided into two steps: feature extraction and fault classification. Feature extraction involves identifying relevant parameters or characteristics from the EV's sensors and signals, enabling the AI system to capture meaningful patterns. Subsequently, fault classification employs AI algorithms to categorize and identify specific faults based on the extracted features, facilitating efficient diagnosis and maintenance of EVs. In the realm of EVs, the combination of AI techniques and FDD has the potential to improve performance, reliability, and safety while enabling proactive maintenance and reducing downtime. Using machine learning and deep learning, we can detect the fault in the system before it starts damaging our EV.
Data analysis and visualization on titanic and student’s performance datasets-an exploratory study Kim, Seong-Cheol; Salkuti, Surender Reddy; Suresh, Alka Manvayalar; Sankaran, Madhu Sree
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 1: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v14i1.pp68-76

Abstract

Exploratory data analysis (EDA) is all about exploring the data in order to identify any underlying pattern before you try to use it to make a predictive model. It also plays a major role in the data discovery process as it is used to analyze data and to recapitulate their different characteristics, which is displayed efficiently with the help of data visualization methods. This paper aims to identify errors in the dataset, to understand the existing hidden structure and to identify new ones, to detect points in a dataset that deviate to a greater extent from the collected data (outliers), and also to find any relationship or intersection between the variables and constants. Two datasets are used namely ‘Titanic’ and ‘student’s performance’ to perform data analysis and ‘data visualization’ to depict ‘exploratory data analysis’ which acts as an important set of tools for recognizing a qualitative understanding. The datasets were explored and hence it assisted with identifying patterns, outliers, corrupt data, and discovering the relationship between the fields in the dataset.
Teaching learning based optimization algorithm for effective analysis of power quality using dynamic voltage restorer Das, Soumya Ranjan; Salkuti, Surender Reddy
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 1: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v14i1.pp268-275

Abstract

In this study, the load voltage is dynamically restored utilising the dynamic voltage restorer (DVR) using the voltage injection approach. The injected voltage is generated using a voltage-source inverter (VSI), which is necessary to correct for the utility network's sag and swell characteristics voltage. The restoration process is dependent on the condition and quality of the utility system, i.e., it injects energy into the external system for the duration of voltage sag, and during voltage swell, energy is absorbed by the compensator from the external system, causing an rise in dc link voltage, which is connected across the VSI. In this study two different controllers are employed based on a learning based optimized algorithm. The simulation results are shown using two different controllers and the performance of the proposed controller is found to be a better one.
Co-Authors Agarwal, Nandini Agrawal, Shruti Balakishan, Kasoju Pragathi Banka, Sujatha Battu, Neelakanteshwar Rao Behera, Niharika Behera, Pratyus Ranjan Behera, Sandip Ranjan Behera, Subhashis Satyabrata Bhardwaj, Lakshay Bhatt, Stuti Bhupathiraju, Sai Sruthi Biswal, Prabin Chaturya, Paipoti Pooja Das, Soumya Ranjan Das, Subha Ranjan Dash, Arul Kumar Gupta, Shubh Hembram, Dashmat Jhunjhunwalla, Drishana Jung, Chan-Mook Kumar, Aman Kumar, Ayush Kumar, Pavan Kumar, Prince Lenka, Rakesh Kumar Mallick, Soubhagya Ranjan Mishra, Debani Prasad Mishra, Sivkumar Namineni, Rishitha Nayak, Pranav Swaroop Ojha, Prajna Jeet Padhy, Somya Siddharth Pagidipala, Sravanthi Palei, Shantilata Patro, Aditya Ranjan Pattanayak, Nitesh Pradhan, Partha Sarathi Pradhan, Rudranarayan Priyadarshini, Ananya Rai, Niti Rani rai, Priyanka Rajeev, B Rout, Kishrod Kumar Routray, Bhabesh Sahay, Sanchita Sahoo, Arun Kumar Sahu, Jayanta Kumar Sahu, Smruti Susmita Samal, Sarita Samanvi, Potnuru Sankaran, Madhu Sree Satapathy, Swayamjyoti Senapati, Asutosh Senapati, Rudranarayan Seong-Cheol Kim, Seong-Cheol Sethy, Sai Kanha Shah, Dhruvi Shekhar, Priyansu Singh, Anurag Singh, Saksham Subudhi, Umamani Sunil Kumar, Chava Suresh, Alka Manvayalar Yagyna Duthsharma, Rampa Sri Sai