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All Journal International Journal of Power Electronics and Drive Systems (IJPEDS) International Journal of Applied Power Engineering (IJAPE) International Journal of Advances in Applied Sciences Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Rao, Kambhampati Venkata Govardhan
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Control & Systems Engineering Earth & Planetary Sciences Electrical & Electronics Engineering Environmental Science Materials Science & Nanotechnology Mathematics Physics

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A new brushless DC motor driving resonant pole inverter optimized for batteries Rao, Kambhampati Venkata Govardhan; Kumar, Malligunta Kiran; Goud, Srikanth B.; Devi, Tellapati Anuradha; Rao, Gundala Srinivasa; Giriprasad, Ambati; Prashanth, ISNVR; Kalyani, Thalanki Venkata Sai
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 14, No 4: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v14.i4.pp2021-2031

Abstract

The brushless DC motor (BLDC) has gained significant popularity in industrial settings due to its notable attributes such as low inertia, rapid response, high power density, exceptional dependability, and reputation for being conservation-free. Typically, these are equipped by a tight-switching PWM inverter, which results in significant switching losses. Consequently, the dissipation of switching loss necessitates the use of sizable heat sinks, resulting in an increase in both the physical dimensions and mass of the drive system. Numerous researchers have developed soft switching inverters with the aim of minimizing switching losses. The utilization of a soft-switching circuit may give rise to additional issues, including heightened voltage stress, incomplete pulse width modulation control, and intricate control scheme or implementation. The present study introduces a basic soft switch inverter design that is suitable for employment in BLDC drive systems powered by batteries. The inverter exhibits low loss for power switching and voltage stress is less on the main switches, while also featuring a straightforward control scheme that is easily implementable. Upon conducting analytical analysis, simulation results were presented by evaluating the theoretical analysis.
The harmonic reduction techniques in shunt active power filter when integrated with non-conventional energy sources Rao, Kambhampati Venkata Govardhan; Kumar, Malligunta Kiran
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 3: March 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i3.pp1236-1245

Abstract

The article covers the control techniques of shunt active filters using switching devices using artificial neural network (ANN) Theory. The basic idea is to achieve perfect disturbance minimization in both steady and transient states. The paper talks about a four-legged converter with a voltage source that can adjust for biased currents and harmonic elements caused by non-linear loads. A shunt connected active filter is used to minimise harmonic currents. The new proposed ANN controller for the improvement of percent total harmonic distortion (THD) is in comparison. The entire power filter concept is based on a MATLAB-modeled with ANN controller. The proposed circuit in this research is studied under various operating situations and simulated, demonstrating the system's potential.
A versatile three-level CLLC resonant converter for off-board EV chargers with wide voltage adaptability contribution Guttikonda, Chandra Babu; Varma, Pinni Srinivasa; Kumar, Malligunta Kiran; Rao, Kambhampati Venkata Govardhan; Teerdala, Rakesh; Kanagala, Santoshi
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 16, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v16.i3.pp1775-1788

Abstract

The vehicle-to-grid (V2G) concept has gained significant attention in the last decade due to its potential to enhance direct current (DC) microgrid stability and reliability. Electric vehicles (EVs) play a central role in distributed energy storage systems, optimizing efficiency and enabling the integration of renewable energy sources. This study offers a unique three level CLLC resonant converter developed for off-board EV chargers to promote bidirectional power transfer between DC microgrids and EVs. The suggested converter uses resonant CLLC components and two three-level full bridges to effectively handle a broad range of EV battery voltages (200 V–700 V). To ensure effective power conversion, the first harmonic approximation (FHA) model is used to analyse the converter's resonant frequency characteristics. The proposed system achieves high efficiency (>95%), with voltage stability maintained at 750 V under various load conditions. The converter's performance was validated through MATLAB based simulations, comparing proportional integral (PI) and proportional integral derivative (PID) control strategies. The PID-controlled system demonstrated superior dynamic response, reduced current ripples, and enhanced voltage regulation compared to the PI-controlled system. This study demonstrates the viability of implementing a three-level CLLC resonant converter for efficient, bidirectional, and wide-voltage adaptation in EV charging infrastructure, thereby contributing to grid stability and renewable energy integration.
Integration and optimization of grid through ANN-based solar MPPT and battery Sujran, Kolli; Sirisha, Ankala; Swapna, Ganapaneni; Kumar, Malligunta Kiran; Rao, Kambhampati Venkata Govardhan
International Journal of Applied Power Engineering (IJAPE) Vol 14, No 4: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v14.i4.pp988-998

Abstract

Integration of solar energy into the grid is the most important aspect for achieving sustainable energy systems. This paper presents an artificial neural network-based maximum power point tracking (ANN-MPPT) system with battery storage to enhance grid efficiency. The proposed ANN-MPPT is dynamically adapted to the varying irradiance and temperature, hence ensuring optimal power extraction from the photovoltaic system. Excess energy is stored in batteries during high solar radiation and discharged when solar generation is low or grid demand is high, maintaining a stable power supply. This system enhances the grid performance in terms of supporting real-time energy exchange, load balancing, and grid stability. Efficient management of the energy fluctuations ensures reliability even at times of grid failures. Further, integration of ANN-based MPPT with battery storage reduces dependence on non-renewable sources and harmonizes solar energy utilization. It can be achieved through enabling smarter energy management and thus contributing to the resilience and efficiency of a grid for better integration of renewable energies. The proposed system can tolerate fluctuating grid demands apart from supporting the features of smart grid, hence viable for increasing stability and sustainability in the grid.
Real-time vehicle detection and speed estimation system using Raspberry Pi and camera module Jyothi, B; Pabbuleti, Bhavana; Sanjeev, Gadi; Rao, Kambhampati Venkata Govardhan; Srilakshmi, S. Sai; Jee, Atul; Kumar, Malligunta Kiran; Bikku, Thulasi; Reddy, Ch. Rami
Bulletin of Electrical Engineering and Informatics Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i6.9931

Abstract

In the era of intelligent transportation systems, real-time vehicle detection and distance estimation play a crucial role in enhancing road safety and traffic efficiency. This study proposes a low-cost, real-time system that integrates you only look once–version 8 (YOLOv8)-based deep learning for vehicle detection with monocular vision techniques for distance estimation, implemented on a Raspberry Pi embedded platform. The objective is to provide a scalable, affordable solution for traffic monitoring and collision avoidance in resource-constrained environments. The methodology involves using a camera module connected to Raspberry Pi for live video capture, YOLOv8 for object detection, and a calibrated monocular distance estimation algorithm based on bounding box dimensions and known vehicle sizes. Experimental results show that the system achieves over 90% detection accuracy under standard lighting conditions and maintains a distance estimation error below 10% for vehicles within 15 meters. The model processes video frames in real time (~0.17 seconds per frame), proving its effectiveness for embedded deployment. In conclusion, the proposed system offers a robust, power-efficient alternative to high-cost light detection and ranging (LiDAR) or stereo vision systems. Its modular design supports future enhancements such as speed estimation or multi-camera integration, making it highly relevant for smart city applications and low-cost vehicular safety systems.
Optimizing smart grids with blockchain-driven automation and demand response Jyothi, B.; Pabbuleti, Bhavana; Ponnala, Ravi; Rao, Kambhampati Venkata Govardhan; Srilakshmi, S. Sai; Narasimha, Putta Dhanush; Yadav, Mareboyina Karthik; Kumar, Malligunta Kiran; Reddy, Ch. Rami
International Journal of Advances in Applied Sciences Vol 14, No 4: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v14.i4.pp985-998

Abstract

To increase resilience, efficiency, and engagement in the network, it shall develop and test its smart grid system integrating blockchain-based authentication and automated demand response management. Simulations are made on the dynamic behavior of the grid in energy generation, consumption, and management through demand responses through MATLAB/Simulink assessment of performance and stability. Ethereum is used in implementing and managing smart contracts that automate and secure events of demand response and consumer interactions for transparency in transactions. It uses Python with Pandas to process, analyze, and visualize simulation data that gives insight into the effectiveness of demand response strategies; PostgreSQL supports the structured storage and querying of data with comprehensive data management. Proper integration of such tools can result in the proper robust simulation of the smart grid system that is highly reliable, efficient usage of energy, and can empower consumers through secure, efficient demand response mechanisms. These immediate issues about managing the grid can thus solve the way toward the future development of such smart grid technologies and their possible integration with the blockchain.
Co-Authors Bikku, Thulasi Devi, Tellapati Anuradha Giriprasad, Ambati Goud, Srikanth B. Guttikonda, Chandra Babu Jee, Atul Jyothi, B Jyothi, B. Kalyani, Thalanki Venkata Sai Kanagala, Santoshi Malligunta Kiran Kumar Narasimha, Putta Dhanush Pabbuleti, Bhavana Ponnala, Ravi Prashanth, ISNVR Rao, Gundala Srinivasa Reddy, Ch. Rami Sanjeev, Gadi Sirisha, Ankala Srilakshmi, S. Sai Sujran, Kolli Swapna, Ganapaneni Teerdala, Rakesh Varma, Pinni Srinivasa Yadav, Mareboyina Karthik