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All Journal International Journal of Electrical and Computer Engineering International Journal of Power Electronics and Drive Systems (IJPEDS) IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Informatics and Communication Technology (IJ-ICT) International Journal of Applied Power Engineering (IJAPE) TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Surender Reddy Salkuti
Woosong University
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Performance improvement of DC microgrids via adaptive neuro-fuzzy inference system -optimized AI-tuned fractional order proportional-integral-derivative controllers Debani Prasad Mishra; Sarita Samal; Manas Ranjan Sahu; Sonna Murari; Piyuskant Das; Surender Reddy Salkuti
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 2: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i2.pp797-804

Abstract

This paper presents a novel approach to enhance the dynamic performance of direct current (DC) microgrids using an artificial intelligence (AI)-tuned fractional order proportional-integral-derivative (FO-PID) controller, further optimized through an adaptive neuro-fuzzy inference system (ANFIS). Conventional PID controllers tend to fail when it comes to dealing with microgrid environment-related non-linearities and uncertainties, particularly under changing load and generation situations. To remedy this, the suggested approach combines AI-tuned tuning algorithms for selecting initial parameters, and then ANFIS optimization to fine-tune the FOPID gains adaptively for better control precision. The performance of the hybrid control approach is tested through MATLAB simulations on a generic DC microgrid model that includes distributed energy resources, power electronic converters, and dynamic loads. Comparative evaluation against standard PID and independent FOPID controllers verifies remarkable advantages in terms of voltage regulation, stability, and transient response in various operating conditions. Amongst the achieved outcomes, it highlights the strength of the proposed ANFIS-optimized AI-tuned FOPID controller as a smart and robust strategy for real-time control of DC microgrids.
Predicting battery life performance using artificial intelligence techniques in electric vehicles Debani Prasad Mishra; Munavath Pavan Kalyan; Shivam Tyagi; Piyushjeet Piyushjeet; Shiv Grover; Surender Reddy Salkuti
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 2: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i2.pp805-812

Abstract

Electric vehicles’ (EVs’ performance and sustainability are significantly influenced by the efficiency and lifespan of their lithium-ion batteries. This paper explores the critical factors affecting battery degradation, focusing on parameters such as charge cycles, thermal management, and voltage dynamics. Utilizing a dataset of 14 batteries, the study employs data-driven machine learning (ML) to predict the remaining useful life (RUL) of batteries. The ensemble-based regression model demonstrated superior predictive accuracy through comprehensive analysis, achieving R² values of 97.89% for training and 94.69% for testing. Feature importance analysis identified cycle index (CI) as the most critical determinant of battery health, followed by discharge time and voltage stability. Visualizations, including correlation heatmaps and residual plots, validate the robustness of the selected model. Additionally, sustainable charging strategies, such as steady current-steady voltage (also known as CC-CV), are highlighted for their role in enhancing battery longevity. This research offers actionable insights into battery management systems, providing a robust foundation for predictive maintenance and the development of sustainable electric mobility solutions.
Integrating IoT for advancing agriculture: innovations and implications for future surveys Debani Prasad Mishra; Rakesh Kumar Lenka; Aditya Kumar; Aditya Jasrotia; Surender Reddy Salkuti
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 2: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i2.pp891-899

Abstract

The internet of things (IoT) is revolutionizing agriculture, offering a paradigm shift in how we cultivate crops and manage livestock. By integrating IoT devices such as sensors, drones, and smart machinery into farming practices, agricultural operations gain unprecedented levels of data driven insights and control. This abstract emphasizes the pivotal role of IoT in agriculture and its far-reaching implications for the future. IoT empowers farmers with real-time information on essential factors like moisture of soil, nutrient levels, weather patterns, and health of crops, helping make accurate decisions while optimizing resources. Through IoT-enabled monitoring and automation, farmers can remotely manage irrigation, pest control, and livestock health, reducing manual labor and minimizing environmental impact. The implications of IoT in agriculture extend beyond individual farms, shaping the future of food production on a global scale. With a burgeoning world population and climate change threatening traditional farming methods, IoT offers solutions for enhancing productivity, sustainability, and resilience in the face of emerging challenges. From precision agriculture to smart supply chains, the revolutionary prospect of IoT in agriculture promises to ensure food security, economic viability, and environmental stewardship for generations to come.
Planar broadband antenna for 2G/3G systems Ashutosh Singh Chauhan; Priyansh Kasyap; Ankit Gupta; Debani Prasad Mishra; Surender Reddy Salkuti; Seong-Cheol Kim
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 20, No 3: June 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v20i3.18977

Abstract

A planar antenna with broadband gestalt is presented for mobile networks. The structure of the antenna is made up of a folded dipole pair with an L-figure microstrip coupling line. The microstrip coupling along with the dipoles are attached on a similar substrate. The radiation parts are plotted at 1.7 GHz, 2.2 GHz, and 2.7 GHz. A flexible coaxial cable made of perfect electric conductor (PEC) material is attached to the L-figure microstrip whereas the outside conductor made up of RO4350B material is attached to the coplanar strip of line. The gain of the antenna is almost 9 dBi. The benefit of the planar structure is that it offers a simple feeding structure and compact size that is necessary for second generation (2G)/third-generation (3G)/long-term evolution (LTE) systems. Finally, the antenna proposed is designed by using computer simulation technology (CST) microwave studio.
Agriculture data visualization and analysis using data mining techniques: application of unsupervised machine learning Kunal Badapanda; Debani Prasad Mishra; Surender Reddy Salkuti
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 20, No 1: February 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v20i1.18938

Abstract

Unsupervised machine learning is one of the accepted platforms for applying a broad data analytics challenge that involves the way to identify secret trends, unexplained associations, and other significant data from a wide dispersed dataset. The precise yield estimate for the various crops involved in the planning is a critical problem for agricultural planning. To achieve realistic and effective solutions to this problem, data mining techniques are an essential approach. Applying distplot combined with kernel density estimate (KDE) in this paper to visualize the probability density of disseminated datasets of vast crop deals for crop planning. This paper focuses on analyzing and segmenting agricultural data and determining optimal parameters to maximize crop yield using data mining techniques such as K-means clustering and principal component analysis (PCA)
GSM based load monitoring system with ADL classification and smart meter design Debani Prasad Mishra; Rudranarayan Senapati; Rohit Kumar Swain; Subhankar Dash; Raj Alpha Swain; Surender Reddy Salkuti
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i1.pp74-83

Abstract

This paper introduces a method for the classification of activities of daily living (ADL) by utilizing smart meter and smart switch data in a synergistic approach. Through the integration of these internet of things (IoT) devices, the paper aims to enhance the application of ADL classification. Guided by recent advancements in load monitoring and energy management systems, the methodology incorporates machine learning techniques to analyze data streams from both the smart meter and smart switch. Drawing inspiration from prepaid smart meter monitoring systems, IoT-based smart energy meters for optimizing energy usage, and energy metering chips with adaptable computing engines, our design incorporates diverse perspectives. Additionally, we consider the utilization of mobile communication for prepaid meters, remote detection of malfunctioning smart meters, and an empirical investigation into the acceptance of IoT-based smart meters. We substantiate our proposed approach through experimental results, showcasing its effectiveness in accurately classifying diverse ADL scenarios. This research contributes to the field of smart home technology by offering an advanced method for ADL classification. The integration of smart meter and smart switch data provides a comprehensive understanding of energy consumption patterns, opening avenues for improved energy management and informed decision-making within smart homes.
Renewable energy optimization for sustainable power generation Debani Prasad Mishra; Sarita Samal; Rohit Kumar; Arun Kumar Sahoo; Surender Reddy Salkuti
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i1.pp365-373

Abstract

To improve sustainability in power generation, this study presents a thorough data-driven method for maximizing renewable energy sources. It employs measures like capacity utilization factor (CUF) and efficiency to evaluate the performance of solar and wind energy using historical weather and energy-generating data. The study offers practical suggestions for improving renewable energy systems, such as weather-energy correlation analysis and machine learning-based forecasting models. In addition, a comparative analysis is carried out to ascertain which energy source is better, and useful real-world data is provided, including a summary of all India’s total renewable energy generation (excluding large hydro) for June 2023 and a performance comparison year over year. A useful, data-driven approach for enhancing renewable energy is provided by this work, which advances the topic of sustainable energy.
Self-adaptive firefly algorithm-based capacitor banks and distributed generation allocation in hybrid networks Seong-Cheol Kim; Sravanthi Pagidipala; Surender Reddy Salkuti
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i1.pp374-383

Abstract

Power system deregulation has made significant changes to the power grid through various technologies, privatization of entities, and improved efficiency and reliability. This work mainly focuses on different combinations of distributed generation (DG) and capacitor banks (CBs) integration to cater to multiple technical, economic, environmental, and reliable concerns. A new optimal planning framework is proposed for optimally allocating the DG units and CBs to achieve multiple objectives. In this work, an augmented objective function is formulated by considering active power losses, voltage deviation, and voltage stability index objectives. This objective function is solved considering various equality and inequality constraints. This work proposes a novel approach for allocation of DGs and CBs in the radial distribution systems (RDSs) using an evolutionary-based self-adaptive firefly algorithm (SAFA). The effectiveness of the developed planning approach is demonstrated on IEEE 33 bus RDS in MATLAB software. The obtained results indicate that proposed planning approach resulted in reduced power losses, voltage deviations, and improved voltage stability.
Optimizing solar energy forecasting and site adjustment with machine learning techniques Debani Prasad Mishra; Jayanta Kumar Sahu; Soubhagya Ranjan Nayak; Anurag Panda; Priyanshu Paramjit Dash; Surender Reddy Salkuti
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i1.pp384-392

Abstract

Estimation of solar radiation is a key task in optimizing the operation of power systems incorporating high levels of photovoltaic (PV) generation. This paper discusses the application of machine learning techniques, namely extreme gradient boosting (XGBT) and random forest (RF), to improve accuracy in the forecasting of solar radiation while adapting for different sites. Utilizing datasets such as meteorological and solar radiation data, the suggested models demonstrate the enhancement of forecasting accuracy by 39% from traditionally applied statistical practices. Along with this, this study also encompasses how endogenous and exogenous factors could be involved in better predictions of solar energy availability. From our findings, XGBT, as well as other machine learning techniques, do enjoy superior performance levels when it comes to the forecasting of solar radiation, which in turn promotes efficient management and potential adaptation of solar energy systems. This study demonstrates how this last generation of algorithms could be applied to noticeably improve the efficiency of solar power forecasting and thereby contribute to more sustainable and reliable energy systems as a byproduct of that.
Optimization of load frequency control systems using PSO technique Debani Prasad Mishra; Rudranarayan Senapati; Lingam Yashwanth; Peesodi Uday; Surender Reddy Salkuti
International Journal of Applied Power Engineering (IJAPE) Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v15.i1.pp177-185

Abstract

This paper investigates the improvement of low-frequency load control (LFC) by optimizing integral part (PID) control using particle swarm optimization (PSO). Load frequency control is important to ensure energy stability by maintaining the balance between production and consumption. Conventional proportional integral derivative controllers are widely used for this purpose; however, their performance can be further improved through optimization. This work uses particle swarm optimization, a nature-inspired algorithm, to set the parameters of the proportional integral derivative controller. PSO was chosen because it can search for good solution space and find a good agreement between control parameters, thus improving the dynamic and stable response of the system. This article provides a comprehensive evaluation of the proposed approach, including simulation results and comparisons with standard PID controllers. The effectiveness of the optimized PID controllers in reducing the frequency difference and improving the overall efficiency of the power plant under different conditions is demonstrated. This study provides insight into the use of artificial intelligence to improve control parameters in the power grid, providing a promising way to improve the efficiency and reliability of frequency controllers.
Co-Authors Abhisek Sahoo Aditya Jasrotia Aditya Kumar Aditya Prasad Mahapatra Aishwarya Priyadarshini Amba Subhadarshini Nayak Ambika Prasad Hota Ambuj Shukla Ankit Gupta Anurag Panda Anwesh Pattnaik Arghya Sardar Arun Kumar Sahoo Arun Kumar Sahoo Ashutosh Singh Chauhan Asutosh Samal Atman Panigrahi Bhabani Shankar Panda Bishweashwar Sukla Dashmat Hembram Debani Prasad Mishra Debani Prasad Mishra Debani Prasad Mishra Debani Prasad Mishra Debani Prasad Mishra Debani Prasad Mishra Debani Prasad Mishra Debani Prashad Mishra Drishana Jhunjhunwalla Gangavaram Teja Rishitha Harikrishnan K. M. Jayanta Kumar Sahu Jayanta Kumar Sahu Jayanta Kumar Sahu Kalpa Ranjan Behera Kaushiki Agrawal Kshirod Kumar Rout Kshirod Kumar Rout Kshirod Kumar Rout Kshirod Kumar Rout Kunal Badapanda Lakshay Bhardwaj Lingam Yashwanth Manas Ranjan Sahu Mandakurit Nivas Mandakuriti Nivas Monalisa Panda Munavath Pavan Kalyan Neelakanteshwar Rao Battu Neelakanteshwar Rao Battu Nimay Chandra Giri Nitish Saswat Mallik P. Sravanthi Padarabinda Palai Pallavi V. Honagond Pankaj Sharma Papia Ray Pavan Kumar Peesodi Uday Piyushjeet Piyushjeet Piyuskant Das Pooja S. Pujari Prakash Kumar Ray Pranay Kumar Panda Pratyush Gupta Priyansh Kasyap Priyanshu Paramjit Dash Raj Alpha Swain Rakesh Kumar Lenka Rakesh Kumar Yadav Ramakanta Mohanty Rambilli Krishna Prasad Rao Naidu Rambilli Krishna Prasad Rao Naidu Rishabh Vishnoi Rohit Kumar Rohit Kumar Swain Rudra Narayan Senapati Rudranarayan Senapati S. Narasimha S. Narasimha S. S. Saswat Sandeep Vuddanti Sanhita Mishra Sanhita Mishra Sarita Samal Saroj Kumar Panda Seong-Cheol Kim Shiv Grover Shivam Tyagi Sivkumar Mishra Sivkumar Mishra Sivkumar Mishra Sivkumar Mishra Smrutisikha Jena Somnath Banerjee Sonna Murari Sopa Mousumi Patro Soubhagya Ranjan Nayak Soumya Ranjan Das Sravanthi Pagidipala Subhankar Dash Subhrajit Jena Suchitra Shastri Suman Patra Suman Patra Swarnodeep Kar Truptasha Tripathy V. Sandeep Varun N. John Vinod Karknalli