G Shashibhushan

@sirmvit.edu

Asst Prof
SIRMVIT

G Shashibhushan


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https://researchid.co/shashi3

EDUCATION

M.Tech(Ph D)

RESEARCH INTERESTS

Multilevel inverters
11

Scopus Publications

Scopus Publications

  • TRUST-ENHANCED SECURE CLUSTER ROUTING IN WIRELESS SENSOR NETWORKS USING A MODIFIED MOTH FLAME OPTIMIZATION ALGORITHM
    Journal of Theoretical and Applied Information Technology, 2025
  • Synergizing edge computing with energy storage and grid integration in electric vehicles
    G. Shashibhushan, Krishnaiah Narukulla, H. Joseph Prabhakar Williams, G. Manjula, D. R. Ganesh, Sangeeta Singh
    Solving Fundamental Challenges of Electric Vehicles, 2024
    The automotive and energy industries will undergo a revolution with the integration of edge computing, energy storage systems, and grid integration in electric vehicles (EVs) to improve efficiency and sustainability. In electric vehicles (EVs), edge computing improves data processing by cutting down on latency and bandwidth utilization, allowing for real-time energy management decision-making, and optimizing battery consumption and energy distribution. Energy storage systems (ESS), which provide flexibility and bidirectional flow, are essential for EV energy management. V2G technology supports grid stability and streamlines energy exchange procedures by integrating ESS with grid infrastructure. In this chapter, the strategic advantages of edge computing and grid integration in ESS for EVs are examined, with an emphasis on practical applications' cost savings, environmental effects, and operational efficiency.
  • Energy cascade conversion system and energy-efficient infrastructure: Experimentation, results, discussion, and case studies
    Richa Khare, A. Chinnasamy, G. Shashibhushan, P. Suresh Kumar, R. Hemalatha, Sampath Boopathi
    Optimization Techniques for Hybrid Power Systems Renewable Energy Electric Vehicles and Smart Grid, 2024
    This chapter explores energy cascade conversion systems and their role in creating energy-efficient infrastructure. It highlights the importance of optimizing these systems for maximum efficiency and environmental benefits in addressing global energy sustainability and climate change challenges. The chapter discusses various optimization techniques, both traditional and cutting-edge, to enhance the performance of energy cascade conversion systems. It also explores the critical interplay between these systems and broader energy-efficient infrastructure. This text explores strategies for designing and implementing infrastructure that integrates with cascade conversion systems, minimizing energy losses during distribution and utilization. It covers advanced control algorithms, predictive maintenance, materials science innovations, and smart grid technologies. The chapter also explores socio-economic aspects of optimization, including policy frameworks, incentives, and public awareness campaigns for energy-efficient infrastructure adoption.
  • Flexible and Secure EV Charging System Using Blockchain Technology with Namib Beetle Optimization and Continual Spatio-Temporal Graph Convolutional Networks
    Rajanish Kumar Kaushal, G Shashibhushan, Hemantha C, Pandeeswaran Chellaiah, Dibyhash Bordoloi, Natrayan L
    4th International Conference on Sustainable Expert Systems Icses 2024 Proceedings, 2024
    The advancement in electric vehicles (EVs) has heightened the need to have better, safer, and smart charging solutions. There may be a problem with the implementing blockchain technology which may be a problem of scalability that can result in narrow space in providing services for multiple similar activities at different EV places at anyone time thus slowing down the rate of charging. This paper proposes a hybrid method for a flexible and secure smart charging system that combines blockchain technology with advanced techniques. The proposed method is the combined execution of together Namib beetle optimization algorithm (NBOA) and Continual Spatio- Temporal Graph Convolutional Networks (CSTGCN). Henceforth, it is called as NBOA-CSTGCN method. The objective of the proposed technique is to improve the efficiency and stability of EV charging systems. The NBOA is utilized to optimize EV charging schedules, ensuring a balanced load on the power grid. The CSTGCN is employed to predict and adapt to dynamic energy demand, facilitating efficient energy distribution across charging stations. By then, the proposed method is realized in MATLAB platform and associated to various existing approaches for example Particle Swarm Optimization (PSO), Adaptive Non-dominated Sorting Genetic Algorithm II (ANSGA-II), Krill Herd Algorithm (KHA). The proposed NBOA-CSTGCN method achieves the highest efficiency of 92.1 %, outperforming PSO (70.5%), ANSGA-II (78.2%), and KHA (86.7%). It also shows superior utility, with the Aggregator increasing from 2 in PSO to 7 in NBOA-CSTGCN, and the EV utility rising from 3.1 in PSO to 5.9 in NBOA-CSTGCN. These results demonstrate that NBOA-CSTGCN offers the best performance and effectiveness compared to existing methods.
  • Permanent Magnet Synchronous Motor Control and Implementation of this Control into the Microprocessor and its Realization
    Rathnakar G, Harendra Singh Negi, G. Shashibhushan, Praful V. Nandankar, Daxa Vekariya, Ramya Maranan
    2024 International Conference on Intelligent Systems for Cybersecurity Iscs 2024, 2024
    Traditional methods of motor control, such as scalar control or field-oriented control (FOC), may struggle to provide the same level of precision and responsiveness as vector control. This work explores the implementation of vector control in the STM32F302R8 microprocessor for precise regulation of speed and torque in electric motors, commonly utilized in industrial automation and electric vehicles. Prior to vector control implementation, the microprocessor interfaces with essential components: the inverter for DC to AC power conversion, the control module housing control algorithms, and the resolver for rotor position feedback. The process involves programming and testing to accurately evaluate the rotor's rotation angle, crucial for effective vector control by ensuring correct voltage application to motor windings. Upon successful implementation and testing, the vector control system demonstrates functionality in regulating motor speed responsively to desired speed changes.
  • Developing a Deep Learning and Reliable Optimization Techniques for Solar Photovoltaic Power Prediction
    Kumaraswamy Shivashankaraiah, Gadigi Shashibhushan, Arumugam Prema Kirubakaran, Chowdappa Anjanappa, Pradosh Kumar Sharma, Ishwarya Mayiladuthurai Vaidyanathan, Syed Noeman Taqui, Sami Al Obaid, Saleh Alfarraj
    Electric Power Components and Systems, 2024
    Due to its significant solar energy generation, solar PV power facilities have recently been utilized. Although PV power stations are highly preferable, the state’s fundamental drawback is that its output current qualities are unpredictable. Consequently, to ensure a balance and complete functioning, it would be crucial to build systems that allow accurate future projections of solar PV production in the short or medium term. Research postulates a strategy for using deep learning to estimate the short-term electricity generated by solar photovoltaic facilities. This study offers a novel method for predicting photovoltaic systems output power utilizing a Hybrid Deep Neural Network framework, making significant advancements in the field of deep learning applications to transmission system prediction issues. CNN and LSTM are combined in the postulated HDNN paradigm. Traditional deep learning techniques are employed in the initial stage. The effectiveness assessments of these techniques are instead presented in greater detail after they have been trained using firefly optimization techniques. The method with the highest reliability is chosen out of all the techniques used in previous research. Deep learning and power efficiency create a combination that appears to have a successful future, predominantlyin improving sustainable management and the digitalization of the electrical sector.
  • Sustainable energy generation from waste water: IoT integrated technologies
    U. Rahamathunnisa, K. Sudhakar, S. N. Padhi, Sumanta Bhattacharya, G. Shashibhushan, Sampath Boopathi
    Adoption and Use of Technology Tools and Services by Economically Disadvantaged Communities Implications for Growth and Sustainability, 2023
    This chapter investigates the conversion of wastewater into sustainable energy through the use of novel methods such as anaerobic digestion, microbial fuel cells, geothermal desalination, and internet of things (IoT) integration. It underlines the significance of wastewater treatment and energy sustainability, as well as the need for cost-effective solutions. IoT for real-time data collecting, analysis, and control can help with sustainable wastewater treatment and energy generation. Anaerobic digestion generates biogas, whereas microbial fuel cells convert organic molecules into energy. Geothermal desalination provides low-cost energy efficiency. IoT technology enhances performance, lowers energy consumption, and allows for remote monitoring and maintenance, all of which contribute to a more sustainable and resilient future.
  • Synergizing artificial intelligence, 5G, and cloud computing for efficient energy conversion using agricultural waste
    Anurag Vijay Agrawal, G. Shashibhushan, S. Pradeep, S. N. Padhi, D. Sugumar, Sampath Boopathi
    Sustainable Science and Intelligent Technologies for Societal Development, 2023
    The combination of artificial intelligence, 5G technology, and cloud computing has altered energy conversion processes, most notably the use of agricultural waste for sustainable energy generation. This book chapter digs into AI, 5G, and cloud computing research and development for efficient energy conversion, environmental concerns, and the viability of agricultural waste as a renewable energy resource. AI technologies provide real-time monitoring and control, while cloud computing enables data analytics and optimization. The synergistic method increases the efficiency of energy conversion, predictability, flexibility, optimization, grid integration, energy storage, and cost reduction. Compatibility, data security, and financial sustainability, on the other hand, must be addressed. The chapter emphasises the importance of this integrated strategy in addressing global energy and environmental challenges.
  • AI-Powered Lithium-Ion Battery State of Charge Estimation for Enhanced Electric Vehicle Efficiency
    G Shashibhushan
    7th International Conference on I Smac Iot in Social Mobile Analytics and Cloud I Smac 2023 Proceedings, 2023
    Efficient utilization of lithium-ion (Li-ion) batteries is critical for enhancing the overall performance of electric vehicles (EVs). Accurate estimation of the State of Charge (SoC) of these batteries plays a pivotal role in optimizing their operation. This study investigates the application of artificial intelligence techniques, specifically artificial neural networks (ANN), support vector machines (SVM), linear regression (LR), and K-Nearest Neighbors (KNN), for the estimation of battery SoC. The dataset employed in this research originates from Panasonic's 18650PF dataset, encompassing critical battery parameters. To facilitate accurate model predictions, a comprehensive preprocessing phase was conducted, followed by feature extraction using sparse principal component analysis (SPCA). By employing the extracted features, the AI models were trained to estimate the battery SoC. To evaluate the performance of these models, multiple evaluation metrics were adopted, including Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and the coefficient of determination (R2). Results from the comparative analysis indicated that the ANN model outperformed other AI techniques. The R2 value, a crucial indicator of model performance, was observed to be 0.9925 for the ANN model. This high R2 value signifies the ANN's ability to effectively capture the underlying patterns within the battery dataset, resulting in remarkably precise SoC estimates.
  • Multi-objective solution with PSO algorithm for minimization of torque ripple and speed settling time by using solar-fed 11,9 and 3-level multi-level inverter with vector control of induction motor
    Shashibhushan G., Savita Sonoli
    International Journal of Power Electronics and Drive Systems, 2020
    <p>The 11,9 & 3-level cascaded multi-level inverter is fed with vector control of induction motor. The speed performance of the machine is dependent on the PI controller used for speed control. Regulation of speed can go till 5% is allowable. If the PI controller parameters are not optimal the speed error gets increase. The torque ripple can be reduced by using the multilevel inverter. More than that the PI controller output is related with torque. So, the problem is formulated with reduction of settling time of speed and torque ripple. The Multi-objective Particle Swarm Optimization (MPSO) algorithm is used to solve the problem. And the performances are compared with PI controller and PSO-PI control of vector control drive. MATLAB is used to solve the entire system.</p>
  • Implementation of single phase nine level inverter
    International Journal of Applied Engineering Research, 2015