Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item Performance analysis of automated QFT robust controller for long-term grid tied PV simulations(Institute of Electrical and Electronics Engineers Inc., 2020) Gudimindla, H.; Krishnamurthy, M.S.; Sandhya, S.Long-term simulations are significant to understand the real-time operation of grid-tied renewable energy system configurations. Grid-tied photovoltaic system (GPV) is highly non-linear due to the dependency of real-time meteorological conditions. The non-linear behavior of the photovoltaic (PV) system with the power electronic converter makes the long-term simulation inefficient and slow. This paper presents an efficient and simple modelling approach for GPV modelling suitable for long-term simulations. The recent advancements in control strategies and system configurations, sub-module level controller operation gained much interest but the simulation of such systems can be very challenging due to a large number of power electronic components and their control, non-linear behavior of PV system. This paper proposed a genetic algorithm based robust controller design in the quantitative feedback theory (QFT) framework to extract the maximum power from GPV at the sub-module level to extradict the power losses due to partial shading conditions. The performance of the proposed controller at the PV sub-module level is evaluated through comparison with the Q-parameterization based controller. The proposed QFT methodology based robust controller is shown to have advantages over Q-parameterization approach to simulate long-term GPV operation. © 2020 IEEE.Item Performance Analysis of Adaptive Speed Reference Tracking QFT Robust Controller for Three Phase Grid connected Wind Turbine under Stochastic Wind Speed Conditions(Institute of Electrical and Electronics Engineers Inc., 2021) Gudimindla, H.; Manjunatha Sharma, K.; Sandhya, S.Due to stochastic nature of wind, stator current harmonics arise in PMSG based wind energy conversion system that causes the oscillations in wind turbine shaft. These oscillations shows adverse effect on lifespan of wind turbine. It is desirable to track the speed reference in grid connected variable speed wind system along with the maximum power extraction. In this sense, this paper presents the robust controller design methodology to achieve the reference speed tracking controller with maximum power extraction capability in variable speed PMSG driven wind turbines. Further, A modified fitness function is introduced to design the automated robust controller using Genetic algorithm in quantitative feedback theory framework. MATLAB simulations are performed on 20 kW three phase grid connected wind system to analyse the dynamic performance of proposed robust controller under step variation and stochastic wind speed conditions. It is evident from the simulations that tracking of reference speed is achieved with proposed controller under power injecting to load and power sharing with grid conditions. © 2021 IEEE.Item Performance analysis of Grid Integrated Hybrid Renewable Energy System configuration application to Residential Buildings(Institute of Electrical and Electronics Engineers Inc., 2021) Gudimindla, H.; Manjunatha Sharma, K.; Sandhya, S.Recent technological advancements and cost reduction drives increasing the penetration of renewable energy sources with grid. Utilisation factor of renewable energy sources is low due to dependency on the environmental conditions. Basically, hybrid energy systems provide constant, high-quality power for the remote communities. The sizing of the hybrid system is significant to meet the load demand of residential buildings. In this paper, A simple optimal sizing algorithm is used to model the Wind turbine and PV system to meet the residential load. Quantitave feedback theory (QFT) based robust controller is implemented for the proposed sub-module integrated PV converter and the PMSG based wind energy conversion system to extract maximum power using simple genetic algorithm. Performance of the QFT controller is analysed through MATLAB simulations under varying irradiance and wind conditions. © 2021 IEEE.Item Isolated Kannada Character Recognition using Densely Connected Convolutional Network(Institute of Electrical and Electronics Engineers Inc., 2022) Sandhya, S.; Geetha, V.Handwritten Character Recognition and Identification are one of the most interesting problem statements in the present digitized world because of its variety of applications. It has leveraged its potential in reducing the manual work of converting the documents containing handwritten characters to machine-readable texts. Recognition of handwritten characters is challenging due to various reasons like high variance in the writing styles across the globe, poor quality of the handwritten text compared to the printed text and the size of the handwritten text. Kannada language has a history of over 1000 years. Kannada vowels and consonants are curvy and symmetric in nature and hence recognition in an offline system becomes difficult. Hence, recognition of Handwritten Kannada characters effectively serves as the main objective of this work. This work proposes a DenseNet121 based Character Recognition model that effectively recognizes the Handwritten Kannada characters. Transfer Learning is used to improve the overall performance of the model. The proposed model achieved a training accuracy of 96.7% and test accuracy of 96.28%, hence proving the effectiveness of the model. © 2022 IEEE.Item YOLOv5 Model-based Ship Detection in High Resolution SAR Images(Institute of Electrical and Electronics Engineers Inc., 2023) Sapna, S.; Sandhya, S.; Shetty, R.D.; Pais, S.M.; Bhattacharjee, S.Detection of ships in Synthetic Aperture Radar (SAR) images play a crucial role in maritime surveillance, most importantly under complex sea conditions. SAR permits observation in any weather conditions, at all hours of the day and night. At present, the ship detection from SAR images is a notable area of research since it is very difficult to detect the ships in the SAR images using traditional object or target detection algorithms. In this work, a You Only Look Once version 5 (YOLOv5) based ship detection model from SAR images with faster training speed and higher accuracy is implemented and tested. This model achieved a mean average precision (mAP) of 96.2% with a training time of 8.63 hours. This work also provides a comparative analysis with the existing methods for detection of ships in SAR images. The comparison shows that the YOLOv5 based model performs better in terms of both mean average precision and training time when compared to the existing models. © 2023 IEEE.