Faculty Publications
Permanent URI for this communityhttps://idr.nitk.ac.in/handle/123456789/18736
Publications by NITK Faculty
Browse
18 results
Search Results
Item A Multi-Level Control and Optimization Scheme for Islanded PV Based Microgrid: A Control Frame Work(IEEE Electron Devices Society eds@ieee.org, 2019) Mathew, P.; Madichetty, S.; Mishra, S.This paper proposes a multi-level control and optimization scheme, including grid control and node control, for an islanded 48-V PV-based low-voltage dc (LVdc) microgrid that aims to overcome the drawbacks of centralized and decentralized control schemes. The analyzed microgrid includes a 20-kW rooftop solar system as the main power source with distributed compensation systems. The central supervisory controller is responsible for updating grid characteristics and sending/receiving information to/from local node controllers, which are responsible for bus voltage regulation and energy management. The control hierarchy features optimized and safe operation (charge and discharge) of storage devices in dc microgrids. The paper also demonstrates the application of battery-supercapacitor systems to absorb system transients during load changes. The simulation showcases the continuous flow of information and decision processes via each level of control, while simultaneously taking the constraints of each subsystem into consideration. The scheme has been simulated in MATLAB/Simulink environment for various case studies to evaluate system stability and robustness. Further, the proposed scheme has been tested experimentally with its prototype and its results are explored. © 2011-2012 IEEE.Item Modified Selective Harmonics Mitigation PWM for a Switched Diode Multilevel Inverter(IOP Publishing Ltd, 2021) Sahaya Ponrekha, A.; Jagabar Sathik, J.; Lakshmanan, P.; Singh, J.; Mani, L.; Mandal, A.; Madhavan, J.A modified selective harmonic mitigation (SHM) technique for multilevel inverters considering the RMS output voltage magnitude is presented. The harmonic contents in the output voltage of these inverters must satisfy the specified grid code requirement standards. In conventional SHM techniques, the firing angles of the multilevel inverters have been derived by taking into account grid code harmonic reduction standards. When the multi-level inverters are driven with these firing pulses generated, it results in reduction of the magnitude of the inverter output voltage. In order to overcome this issue of output voltage reduction, the modified SHM optimization problem includes another constraint on the RMS output voltage limits, which results in different set of firing angles. This facilitates the use of firing angles, which takes into account the grid code standards of harmonic mitigation without compromising the value of the RMS output voltage of the inverters. The proposal has been simulated and validated in MATLAB Simulink and the experimental results are obtained for a single-phase seven level inverter with Silicon made semiconductor switches. By using the proposed method, output voltage THD (upto 40th harmonics were considered) of 5.9% was obtained, which is well below the harmonic standards specified by EN 50160. © 2021 The Electrochemical Society ("ECS"). Published on behalf of ECS by IOP Publishing Limited.Item Five-Level Switched Capacitor Inverter for Photovoltaic Applications(Taylor and Francis Ltd., 2022) Singh, A.K.; Mandal, R.K.; Raushan, R.; Anand, R.This paper proposes a switched-capacitor based single-phase five-level inverter configuration that operates under boost operation and generates a voltage that is more than the DC source voltage. The proposed five-level inverter uses a capacitor and boots the output voltage. In this proposed inverter, capacitor gets charged in parallel while it discharges in series connections so that output voltage may attain higher magnitude than the DC source voltage. Sinusoidal Pulse Width Modulation-based techniques are considered to produce the required gate pulses for operating the switching devices of the inverter. The five-level switched-capacitor inverter is combined with the PV system via DC–DC boost converters to extract the maximum power using MPPT algorithm. To verify its capability, the PV-based system is further integrated to the utility grid. The operation and performance of the suggested switched-capacitor inverter coupled with the grid-connected PV system are also analyzed by developing its model in MATLAB/Simulink environment. © 2022 IETE.Item A Precise Switching Frequency Formulation of Hysteresis-Controlled Grid-Connected Inverters Considering Nonlinear Ripple Current(Institute of Electrical and Electronics Engineers Inc., 2022) Damodaran, R.; Venkatesa Perumal, B.V.Hysteresis current control (HCC) is one of the most simple and rapid modulation techniques for multilevel grid-connected inverters (MGCIs). It controls the output current by limiting its ripple within fixed hysteresis limits. This results in a varying switching frequency, which is not known implicitly. The knowledge of switching frequency is essential for filter design, device selection, and loss calculations of the MGCI. The existing frequency estimations for HCC assume linear ripple current considering high-frequency operation. This assumption is invalid for the range of low frequencies. This leads to inaccurate estimation of switching frequency, which can have a considerable effect on system design. In this article, a more precise and generalized expression to estimate the switching frequency of the MGCI is obtained. The improvement in accuracy is demonstrated with an example of second-order filter design. The effect of change in hysteresis limits and input voltage on the switching frequency is analyzed to determine the operating point for accurate system design. Simulation and experimental results are found to be in good agreement with the theoretical claims. © 1982-2012 IEEE.Item A novel nine-level inverter with reduced component count using common leg configuration(Springer Science and Business Media Deutschland GmbH, 2023) Nageswar Rao, B.; Yellasiri, Y.; Shiva Naik, B.S.; Aditya, K.This article proposes a nine-level (9 L) inverter with a common leg configuration employing transformers and a single dc source. The suggested inverter uses eight switches and two transformers to produce 9 L output voltage. The suggested circuit minimizes the switches and transformers compared with existing transformer-based multilevel inverters (TMLI). Therefore, the proposed circuit cost, volume and complexity are also reduced. Additionally, a thorough comparison with the various 9 L inverter circuits is conducted to ensure the benefits of the suggested TMLI. A basic logic gate-based pulse width modulation (PWM) is implemented for the suggested 9 L inverter. Simulation and hardware studies verifying the feasibility and proficiency of the suggested inverter are performed. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.Item Tomato plant disease classification using Multilevel Feature Fusion with adaptive channel spatial and pixel attention mechanism(Elsevier Ltd, 2023) Sunil, C.K.; Jaidhar, C.D.; Patil, N.Agriculture's productivity has decreased in the last decade due to climate change and inappropriate usage of water, fertilizer, and pesticides, which stimulate plant diseases. Plant pathogens are the prime threat to agriculture; diseases causes the development of plant and affects the quality and yield of the crop. To enhance crop yield and quality, early perceive the pathogens and insinuation of the proper medications are essential. Deep learning approaches produce promising results for classifying the input images, and the results vary for many reasons, such as data imbalance and fewer or identical features among other classes of the dataset. In this work, tomato plant disease classification is proposed by using Multilevel Feature Fusion Network (MFFN). It employs ResNet50, MFFN, and Adaptive Attention Mechanism, which combines channel, spatial, and pixel attention to classify the tomato plant leaf images. The proposed deep learning-based approach is trained and tested on a tomato plant leaves dataset and achieved 99.88% training accuracy, 99.88% validation accuracy, and 99.83% external testing accuracy. It outperformed the existing approaches relevant to the tomato plant dataset. Further, this work also proposes a pesticide prescription module that provides pesticide information based on the type of leaf disease. © 2023 Elsevier LtdItem DFN-PSAN: Multi-level deep information feature fusion extraction network for interpretable plant disease classification(Elsevier B.V., 2024) Dai, G.; Tian, Z.; Fan, J.; Sunil, C.K.; Dewi, C.Accurate identification of crop diseases is an effective way to promote the development of intelligent and modernized agricultural production, as well as to reduce the use of pesticides and improve crop yield and quality. Deep learning methods have achieved better performance in classifying input plant disease images. However, many plant disease datasets are often constructed from controlled scenarios, and these deep learning models may not perform well when tested in real-world agricultural environments, highlighting the challenges of transitioning to natural farm environments under the new demand paradigm of Agri 4.0. Based on the above reasons, this work proposes using a multi-level deep information feature fusion extraction network (DFN-PSAN) to achieve plant disease classification in natural field environments. DFN-PSAN adopts the YOLOv5 Backbone and Neck network as the base structure DFN and uses pyramidal squeezed attention (PSA) combined with multiple convolutional layers to design a novel classification network PSAN, which fuses and processes the multi-level depth information features output from DFN and highlights the critical regions of plant disease images with the help of pixel-level attention provided by PSA, thus realizing effective classification of multiple fine-grained plant diseases. The proposed DFN-PSAN was trained and tested on three plant disease datasets. The average accuracy and F1-score exceeded 95.27%. The PSA attention mechanism saved 26% of model parameters, achieving a competitive performance among existing related methods. In addition, this work effectively enhances the transparency of the features of the model attention to plant diseases through t-SNE with SHAP interpretable methods. © 2023 Elsevier B.V.Item Multilevel Multimodal Framework for Automatic Collateral Scoring in Brain Stroke(Institute of Electrical and Electronics Engineers Inc., 2024) Raj, R.; Dayananda, D.; Gupta, A.; Mathew, J.; Kannath, S.K.; Prakash, A.; Rajan, J.In patients with ischemic brain stroke, collateral circulation plays a crucial role in selecting patients suitable for endovascular therapy. The presence of well-developed collaterals improves the patient's chances of recovery. In clinical practice, the presence of collaterals is diagnosed on a Computed Tomography Angiography scan. The radiologist grades it on the basis of subjective visual assessment, which is prone to interobserver and intraobserver variability. Computer-based methods of collateral assessment face the challenge of non-uniform scan volume, leading to manual selection of slices, meaning that the most imperative slices have to be manually selected by the radiologist. This paper proposes a multilevel multimodal hierarchical framework for automated collateral scoring. Specifically, we propose deploying a Convolutional Neural Network for image selection based on the visibility of collaterals and a multimodal model for comparing the occluded and contralateral sides of the brain for collateral scoring. We also generate a patient-level prediction by integrating automated machine learning in the proposed framework. While the proposed multimodal predictor contributes to Artificial Intelligence, the proposed end-to-end framework is an application in engineering. The proposed framework has been trained and tested on 116 patients, with five-fold cross-validation, achieving an accuracy of 91.17% for multi-class collateral scores and 94.118% for binary class collateral scores. The proposed multimodal predictor achieved a weighted F1 score of 0.86 and 0.95 on multi-class and binary-class collateral scores, respectively. The proposed framework is fast, efficient, and scalable for real-world deployments. Automated evaluation of collaterals with attention maps for explainability would complement radiologists' efforts. Code for the proposed framework is available at: https://github.com/rishiraj-cs/collaterals_ML_MM. © 2013 IEEE.Item A Reduced Component Count Self-Balance Quadruple Boost Seventeen-Level Switched Capacitor Inverter(Institute of Electrical and Electronics Engineers Inc., 2024) Ahmed, S.; Raushan, R.; Ahmad, M.W.A switched capacitor multilevel inverter (SCMLI) enables high-quality output voltage waveforms for various industrial and renewable energy applications. SCMLI uses a combination of capacitors and switches to generate multiple voltage levels from a single dc source, thereby reducing the overall cost and size of the system. This article proposes a novel configuration of a 17-level SCMLI. The proposed converter can boost four times the input voltage by exploiting the series-parallel connection of capacitors with the dc voltage source. With simple pulsewidth modulated (PWM) control, the capacitor voltages are inherently balanced under different loading conditions. Furthermore, for 11 switches, only seven independent switching signals are required. Loss analysis reveals that the proposed SCMLI has significantly reduced conduction losses, capacitor ripple voltage, voltage stress, and cost function (CF) when compared with other topologies available in the literature. Finally, the simulation results are obtained at different loads and modulation indexes. The results are experimentally validated with a scaled-down laboratory prototype. © 2024 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.Item A modified T-type multilevel inverter for renewable energy applications(Elsevier Ltd, 2024) Nageswar Rao, B.; Yellasiri, Y.; Shiva Naik, B.S.; Aditya, K.; Panda, A.K.The primary challenge in integrating renewable resources into grids using multilevel inverters (MLI) is the need for many separate DC sources and switching device counts. Transformer-based multilevel inverters (TMIs) have emerged to address this issue, aiming to minimize system components and boost source voltage with a single DC source. This research article introduces a novel TMI topology that utilizes only a single DC source and incorporates ten switches to produce good-quality load voltage with high magnitude. The proposed TMI offers several structural advantages, including self-galvanic isolation, reduced switching devices and uniform voltage levels across all turn ratios. Additionally, the TMI operates a switching method called pulse width modulation, which provides the gating pulses to all the power semiconductor devices in the proposed TMI. An experimental model has been created in a laboratory environment, and simulations are performed using the MATLAB/Simulink platform to assess the effectiveness of the suggested TMI. Furthermore, a comparison between the suggested TMI circuit and other recent TMI designs with similar characteristics is performed. This comparison is carried out to assess and validate the superior features of the proposed TMI over the alternative designs. © 2024 Elsevier B.V.