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All Journal Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Venkatarajan, Shanmugasundaram
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Electrical & Electronics Engineering

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Design and development of photovoltaic solar system based single phase seven level inverter Govindaraj, Vijayakumar; Mayakrishnan, Sujith; Venkatarajan, Shanmugasundaram; Raman, Raja; Sundar, Ramesh
Bulletin of Electrical Engineering and Informatics Vol 13, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i1.5168

Abstract

For solar photovoltaic (PV) systems, an upgraded triple gain seven-level inverter that works both independently and while connected to the grid is proposed. The two-stage configuration of the system is boost cascaded. The first stage has a one switch improved gain converter (OSIGC) to increase and normalize the input direct current (DC) voltage, and the second stage includes a unique seven level alternating current (AC) is produced via a multilevel inverter (MLI) design with triple voltage gain. The proposed OSIGC is appropriate for a broad range of conversions. The voltage gain in MLI was achieved using switched capacitor techniques. The DC-DC converter can achieve a maximum voltage gain of twelve and the MLI can achieve a maximum voltage gain of three, resulting in a DC-DC-AC voltage that can reach 36. Maximum power point tracking (MPPT) technique based on modified perturb and observe (PO) is used in OSIGC to maximise PV module power utilisation, and MLI control utilises sinusoidal pulse width modulation (SPWM) realistically. For the purpose of analysing the suggested system, a 200 Watt prototype statel is created. With a total harmonic distortion (THD) of 0.181%, up to 92.12% of the converter system’s overall efficiency is possible.
Space vector pulse width modulation realization for three-phase voltage source inverter Palanisamy, Ramasamy; Santhakumari, Valarmathi Thangamani; Venkatarajan, Shanmugasundaram; Hemalatha, Selvaraj; Hepzibah, Albert Alice; Ramkumar, Ravindran; Sugavanam, Vidyasagar
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1976-1984

Abstract

This paper presents the implementation of space vector pulse width modulation (SVPWM) for a three-phase voltage source inverter (VSI). SVPWM is a technique used to control the output voltage of VSIs with improved efficiency and precision. The abstract outlines the key steps involved in implementing SVPWM, including reference signal clarification, sector identification, determination of voltage vectors, and switching state calculation. This proposed system provides improved output voltage of the inverter, minimized voltage stress across the switches and reduced total harmonic distortion and electromagnetic interference. The proposed implementation aims to enhance the performance of three-phase VSIs in various applications, such as motor drives, renewable energy systems, and power converters. The simulation results of proposed system are verified using MATLAB Simulink.
Improving farming by quickly detecting muskmelon plant diseases using advanced ensemble learning and capsule networks Kannan, Deeba; Sundarasrinivasa Sankaranarayanan, Nagamuthu Krishnan; Venkatarajan, Shanmugasundaram; Mahajan, Rashima; Gunasekaran, Brindha; Murugamani, Pandi Maharajan; Dhandapani, Karthikeyan
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i3.pp2090-2100

Abstract

In modern agriculture, ensuring plant health is essential for high crop yields and quality. Plant diseases pose risks to economies, communities, and the environment, making early and accurate diagnosis crucial. The internet of things (IoT) has revolutionized farming by enabling real-time crop monitoring and using drones and cameras for early disease detection. This technology helps farmers address challenges with precision and sustainability. This research propose an ensemble learning model incorporating multi-class capsule networks (MCCN) and other pre-trained model with majority voting system is implemented to predict plant diseases and pests early. The research aims to develop a robust MCCN-based ensemble prediction model for timely disease identification. To evaluate the performance of the ensemble model, various key metrics, including accuracy, and loss value, are assessed. Furthermore, a comparative analysis is conducted, benchmarking the MCCN model against other well-known pre-trained models such as residual network-101 (ResNet101), visual geometry group-19 (VGG19), and GoogleNet. This research signifies a substantial stride towards the realization of IoT-driven precision agriculture, where advanced technology and machine learning contribute to the early detection and mitigation of plant diseases, ultimately enhancing crop yield and environmental sustainability.
Co-Authors Dhandapani, Karthikeyan Govindaraj, Vijayakumar Gunasekaran, Brindha Hemalatha, Selvaraj Hepzibah, Albert Alice Kannan, Deeba Mahajan, Rashima Mayakrishnan, Sujith Murugamani, Pandi Maharajan Palanisamy, Ramasamy Raman, Raja Ramkumar, Ravindran Santhakumari, Valarmathi Thangamani Sugavanam, Vidyasagar Sundar, Ramesh Sundarasrinivasa Sankaranarayanan, Nagamuthu Krishnan