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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) International Journal of Advances in Applied Sciences Indonesian Journal of Electrical Engineering and Computer Science
Mishra, Debani Prasad
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Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Electrical & Electronics Engineering Environmental Science Materials Science & Nanotechnology Mathematics Physics

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Journal : International Journal of Applied Power Engineering (IJAPE)

 1 
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.
Metaheuristic algorithms for parameter estimation of DC servo motors with quantized sensor measurements Mishra, Debani Prasad; Behera, Sandip Ranjan; Dash, Arul Kumar; Ojha, Prajna Jeet; Salkuti, Surender Reddy
International Journal of Applied Power Engineering (IJAPE) Vol 14, No 1: March 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v14.i1.pp101-108

Abstract

Manufacturing, aviation, and robotics have increased servo motor use due to their precision, reliability, and adaptability in various applications. This study compares three metaheuristic techniques for servo motor model parameter estimation with sensor measurement quantization, focusing on their accuracy and efficiency. Armature resistance, back electromotive force (EMF) constant, torque constant, coil inductance, friction coefficient, and rotor-load inertia are crucial to servo motor behavior prediction, significantly impacting overall system performance. Each approach was rigorously tested and analyzed to evaluate its effectiveness in predicting servo motor characteristics. The results revealed that particle swarm optimization and the firefly algorithm delivered comparable performance, particularly excelling in scenarios where sensor measurement quantization introduced noise or imprecision in the data. These methods demonstrated strong resilience and accuracy under such challenging conditions. In contrast, the genetic algorithm did not perform as well, falling short when compared to the other two techniques in handling noisy or imprecise data, indicating its relative inefficiency in such environments. These findings give servo motor designers and engineers across industries a powerful tool for performance prediction.
Optimizing vehicle-to-grid scheduling and strategic placement for dynamic wireless charging of electric vehicles Mishra, Debani Prasad; Sahay, Sanchita; Kumar, Ayush; Salkuti, Surender Reddy
International Journal of Applied Power Engineering (IJAPE) Vol 14, No 2: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v14.i2.pp328-337

Abstract

Dynamic wireless charging of electric vehicles (EVs) has become popular in intelligent transportation systems (ITS). However, both economic and smart city perspectives should be taken into account in the integration of wireless charging infrastructure for electric vehicles. Current research mainly focuses on power transfer (PT) or autonomous vehicle-to-grid (V2G) transfer. This paper presents a multilayered approach that combines optimal PT planning based on urban traffic and energy efficiency data with dynamic V2G planning. Simulation results show that the efficiency of PT placement and V2G scheduling increases and provides good results for smart city enterprises. This multilayered approach not only optimizes the efficiency of power transfer placement and V2G scheduling but also positions itself as a pivotal driver for the sustainable evolution of urban mobility. As dynamic wireless charging continues to shape the future of intelligent transportation systems, this research stands at the intersection of technological innovation, economic prudence, and urban planning, offering a blueprint for the seamless integration of EVs into the fabric of smart cities.
Comparison of MPP methods for photovoltaic system Mishra, Debani Prasad; Senapati, Rudranarayan; Biswal, Prabin; Satapathy, Swayamjyoti; Sahu, Smruti Susmita; Salkuti, Surender Reddy
International Journal of Applied Power Engineering (IJAPE) Vol 14, No 2: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v14.i2.pp338-346

Abstract

Solar electricity is usually a ubiquitous photovoltaic (PV) power source that converts sunlight into electricity. This makes solar energy a key factor in meeting the growing global demand. However, solar energy production from photovoltaic cells can be limited by many factors, so the power source needs to be optimized to reach the maximum level. One of the crucial technologies to enhance the power production of photovoltaic structures is maximum power point tracking (MPPT) measurement. This technology increases energy production by providing many advantages such as security, freedom, maximum energy efficiency, and environmental protection. MPPT continuously monitors the maximum power point of the photovoltaic structure to ensure the system operates at peak efficiency. This technology is indispensable in today’s solar systems, enabling the use of solar energy and reducing dependence on fossil fuels. By optimizing solar energy production, MPPT technology plays a crucial role in supporting the future of energy. It helps reduce climate change and promotes environmentally friendly practices through the use of renewable energy. MPPT technology also increases solar reliability, reduces maintenance costs, and improves overall performance. This makes MPPT an essential part of the modern solar system, ensuring they are efficient and effective.
Study of the development of tandem solar cells to achieve higher efficiencies Mishra, Debani Prasad; Sahu, Jayanta Kumar; Subudhi, Umamani; Sahoo, Arun Kumar; Salkuti, Surender Reddy
International Journal of Applied Power Engineering (IJAPE) Vol 14, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v14.i3.pp647-655

Abstract

Tandem solar cells are the brand-new age revolution within the photovoltaic (PV) enterprise thanks to their higher power conversion efficiency (PCE) capability as compared to single-junction solar cells, which are presently dominating, however intrinsically restrained. With the appearance of steel halide perovskite absorber substances, manufacturing extremely efficient tandem solar cells at an inexpensive price can profoundly regulate the future PV landscape. It has been formerly seen that tandem solar cells primarily based on perovskite have confirmed that they can convert mild more efficiently than stand-alone sub-cells. To reap PCEs of greater than 30%, numerous hurdles have to be addressed, and our understanding of this interesting era has to be accelerated. On this, a technique of aggregate of substances was followed and via a modified numerical technique, it was decided what preference of substances for the pinnacle and bottom sub-cell consequences in a better fee of electricity conversion efficiency (PCE). Through this study, it was discovered that the use of germanium telluride (GeTe) backside subcellular together with perovskite (MAPbI3-xClx) as pinnacle subcell can offer an excessive performance of 46.64% compared to a tandem mobile with perovskite (MAPbI3)/CIGS and perovskite (MAPbI3)/GeTe which produce decrease efficiencies. SCAPS-1D was used to evaluate and simulate the overall performance of the developed tandem cells.
Modulation and performance analysis of two-wheeler electric vehicle Mishra, Debani Prasad; Senapati, Rudranarayan; Kumar, Pavan; Bhardwaj, Lakshay; Salkuti, Surender Reddy
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.pp186-194

Abstract

When compared to traditional cars, electric vehicles (EVs) have less pollution, better fuel efficiency, and are better for the environment. This essay explores the evolution of EVs in great detail, emphasizing their vital role in lowering CO2 emissions and promoting sustainability. It builds a dynamic model for EVs using MATLAB/Simulink, which explains the state of charge (SOC) and range prediction. The study emphasizes the importance of EVs in promoting a sustainable future by thoroughly covering design details, modeling, and a scientific methodology. Through the use of modeling to clarify technical aspects and highlight the significance of EV adoption, this study highlights the vital role that EVs play in reducing environmental impact and advancing environmentally friendly transportation. It highlights EVs' potential to revolutionize the automobile sector while promoting cleaner modes of transportation. It offers a thorough overview of EV production and usage and fervently promotes their wider acceptance as a means of laying the groundwork for a more sustainable and clean future.
Optimization of load frequency control systems using PSO technique Mishra, Debani Prasad; Senapati, Rudranarayan; Yashwanth, Lingam; Uday, Peesodi; Salkuti, Surender Reddy
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 Agarwal, Nandini Agrawal, Shruti Behera, Niharika Behera, Pratyus Ranjan Behera, Sandip Ranjan Behera, Subhashis Satyabrata Bhardwaj, Lakshay Biswal, Prabin Das, Subha Ranjan Dash, Arul Kumar Gupta, Shubh Hembram, Dashmat Jhunjhunwalla, Drishana Kedia, Nisha Kumar, Aman Kumar, Ayush Kumar, Pavan Kumar, Prince Lenka, Rakesh Kumar Mallick, Soubhagya Ranjan Mishra, Sivkumar Nayak, Pranav Swaroop Ojha, Prajna Jeet Padhy, Somya Siddharth 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 Salkuti, Surender Reddy Samal, Sarita Samanvi, Potnuru Satapathy, Swayamjyoti Senapati, Asutosh Senapati, Rudranarayan Sethy, Sai Kanha Shah, Dhruvi Shekhar, Priyansu Singh, Anurag Singh, Saksham Subudhi, Umamani Swain, Raj Alpha Uday, Peesodi Yagyna Duthsharma, Rampa Sri Sai Yashwanth, Lingam