G Shashibhushan
Verified @gmail.com
Asst Prof
SIRMVIT
EDUCATION
M.Tech(Ph D)
RESEARCH INTERESTS
Multilevel inverters
Scopus Publications
Scopus Publications
Kumaraswamy Shivashankaraiah, Gadigi Shashibhushan, Arumugam Prema Kirubakaran, Chowdappa Anjanappa, Pradosh Kumar Sharma, Ishwarya Mayiladuthurai Vaidyanathan, Syed Noeman Taqui, Sami Al Obaid, and Saleh Alfarraj
Informa UK Limited
U. Rahamathunnisa, K. Sudhakar, S. N. Padhi, Sumanta Bhattacharya, G. Shashibhushan, and Sampath Boopathi
IGI Global
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.
Anurag Vijay Agrawal, G. Shashibhushan, S. Pradeep, S. N. Padhi, D. Sugumar, and Sampath Boopathi
IGI Global
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.
G Shashibhushan
IEEE
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.
Shashibhushan G. and Savita Sonoli
Institute of Advanced Engineering and Science
<p>The 11,9 &amp; 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>