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All Journal Indonesian Journal of Electrical Engineering and Computer Science
Dhandapani Karthikeyan
SRM Institute of Science and Technology

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PI controller for photovoltaic-fed novel multilevel inverter topologies Dhandapani Karthikeyan; Simran Rajiv Khiani; Albert Alice Hepzibah; Uthayakumar Jothilingam; Ravindran Ramkumar; Arumbu Venkadasamy Prathaban; Krishnasamy Vijayakumar
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 3: March 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i3.pp1268-1280

Abstract

This research describes the analysis and design of a unique 7-level and 9-level multi-level inverter topology with fewer DC power supplies and power switches, and a comparison using pulse width modulation (PWM). Comparisons of 7-level and 9-level multi-level inverters that analyze characteristics using optimal nearest level modulation (ONLM) technology are made using various PWM approaches such as nearest level modulation (NLM) technique, phase disposition (PD), phase opposition disposition (POD), and alternate phase opposition disposition (APOD). The output is evaluated based on a high Vrms, low total harmonic distortion (THD) harmonic profile. The controller is based on artificial intelligence (AI) and machine learning (ML) techniques. ONLM techniques are used to measure the voltage harmonics of the full load, several DC offset values are used. The control technology provided is validated using MATLAB/Simulink simulation tools and laboratory-based experimental testing.
Virtual analysis of machine learning models for diseases prediction in muskmelon Deeba Kannan; Balakrishnan Amutha; Sattianadan Dasarathan; Daniel Rosy Salomi Victoria; Vikas Maheshkar; Ravindran Ramkumar; Dhandapani Karthikeyan
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 3: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i3.pp1748-1759

Abstract

Muskmelon, a crop prized for its economic potential, has a relatively brief growth cycle. Disease susceptibility during this period can have a profound impact on yields, posing challenges for farmers. Environmental conditions are pivotal in disease occurrence. Unfavorable conditions reduce the likelihood of pathogens infecting vulnerable host plants as temperature and humidity influence pathogen behavior, including toxin synthesis, virulence protein production, and reproduction. Pathogens can lie dormant in the soil until suitable conditions activate them. When the right environment and host plants align, these dormant pathogens can cause outbreaks. Disease prediction becomes possible by analyzing environmental variables. Real-time data collected via strategically placed sensors focused on viral, fungal, and bacterial infections. Results indicated that the extreme gradient boosting (XGBoost) algorithm, with a maximum tree depth of 4 and 30 trees per iteration, achieved remarkable performance, yielding an accuracy of 97%. For comparison, the XGBoost model outperformed an 8-layer Backpropagation network with 7 nodes per layer, which achieved 95% accuracy. These findings underscore XGBoost's efficacy in forecasting and mitigating muskmelon plant diseases, offering the potential for improved crop yields and agricultural sustainability.
Co-Authors Albert Alice Hepzibah Arumbu Venkadasamy Prathaban Balakrishnan Amutha Daniel Rosy Salomi Victoria Deeba Kannan Krishnasamy Vijayakumar Ravindran Ramkumar Sattianadan Dasarathan Simran Rajiv Khiani Uthayakumar Jothilingam Vikas Maheshkar