Perumal Chandrasekar

This study evaluates the Technical Efficiency (TE) and Total Factor Productivity (TFP) of 54 Indian Electricity Distribution Utilities (IEDUs) from 2020 to 2023 using Data Envelopment Analysis (DEA) and the Malmquist Productivity Index (MPI). The Charnes-Cooper-Rhodes (CCR) model revealed that only 35.18% of DMUs (19 out of 54) were technically efficient, with average efficiency improving marginally from 0.84 to 0.88. Slack-based inefficiencies resulted in an estimated input overutilization of Rs. 63,270.12 crore, highlighting significant operational inefficiencies. Further examined the Total Productivity (TFP) of IEDUs, revealing a 2.5% improvement in operational efficiency (efficiency change = 1.025), driven by gains in pure efficiency (1.036). Although a 7.6% decline in Technological Change (TECHCH = 0.924), due to outdated infrastructure and regulatory delays, offset these gains. Persistent Aggregate Technical & Commercial (AT&C) losses averaged 17.71%, leading to a 5.3% decrease in TFP. Efficient Decision-Making Units (DMUs) incurred lower costs per million units sold (Rs. 0.776 crores) than inefficient units (Rs. 0.924 crores), underscoring the importance of operational optimization. This study employs a DEA–Malmquist framework on 54 IEDUs during 2020–2023 a reform-critical period encompassing post-UDAY, RDSS, and COVID-19 to quantify inefficiency costs, benchmark peer utilities, and provide strategic direction for enhancing operational performance, financial sustainability, and sector-wide reforms.

The reliability of various energy sources can be increased and distributed production and renewable energy can be fully integrated into the power grid on a wide scale through the growth and development of the microgrid (MG). Global energy difficulties are brought about by the finite supply of fossil fuels and the world's expanding energy consumption. Due to these challenges, the electric power system has to convert to a renewable energy‐based power generation system to produce clean, green energy. However, because of the unpredictable nature of the environment, the shift toward the use of renewable energy sources raises uncertainty in the production, control, and power system operation. This manuscript proposes a renewable energy‐based energy management system for electric vehicles and AC–DC MGs. The proposed method is Hermit Crab Optimizer (HCO). The major goal of the proposed strategy is to supply steady power regardless of generation disparity, which should stop the storage devices from degrading too quickly. The HCO approach provides a stable power balance for MG operation. The proposed technique efficiently strikes a power balance to meet load requirements and recharge electric cars. By then, the proposed strategy is implemented in the MATLAB platform and the execution is computed with the existing procedure. The proposed technique displays better outcomes in all existing systems like biogeography‐based optimization (BBO) algorithm, particle swarm optimization (PSO) algorithm, genetic algorithm (GA), and artificial neural network (ANN). The existing technique shows the cost of 25$, 30$, 35$, 40$, and the proposed technique displays the cost of 20$ which is lower than the other existing techniques.

This research aims to build unique zero voltage transition (ZVT) non-isolated bidirectional DC-DC converters for hybrid electric vehicle battery storage. First, a high-voltage gain bidirectional converter (BDC) is examined. This converter can soft-switch insulated gate bipolar transistors (IGBTs). The primary insulated-gate bipolar transistors (IGBTs) are operated under zero-current conditions throughout the turn-on to turn-off commutation phase to reduce switching losses and increase efficiency. A soft-switched cell with a resonant inductor, capacitor, and additional IGBTs achieves zero-current turn-off. A new converter uses insulated-gate bipolar transistors with zero-voltage transition operation. Soft-switched cells improve the hard-switched bridgeless DC-DC converter (BDC). Resonant inductors, capacitors, and auxiliary switching devices make up the soft-switched cell. Soft-switched cells enable zero voltage turn-on of primary insulated-gate bipolar transistors. This converter charges the battery in buck mode and boosts it to provide the necessary output voltage. This study examined a 70 V/300 V power system's high-gain bidirectional converter (BDC) design simulation. The converter was tested at 50 kHz with 800 W output power. The high-gain soft-switched BDC has 96.5% boost and 97% buck efficiency. Operating principles, design analysis, and simulation assessments are included in this study.

The last ten years have seen a rise in the usage of multilayer inverters. Because they can produce waveforms with a greater harmonic spectrum and reliable output. Several high voltages and high-power applications are suitable for these new inverters. A multilayer converter not only produce high power ratings, but it also saves time and money and enhances the system performance in terms of harmonics, dv/dt strains, and pressures on the motor bearings. Owing to its flexibility and modularity, the possible topology for a power application is the cascaded H-bridge multilevel inverter (CMI) with separated DC sources. This study investigates a cascade H bridge multilevel inverter, such as a Three, Five, Seven, Nine and Eleven level inverter in order to drive a three phase AC induction motor. A Level shifted Pulse Width Modulation (LSPWM) technique was applied for the CHBMLI. Specific emphasis is given to aspects like modulation and total harmonic distortion that are preferable or necessary for multi-level converters. For both intellectual and scientific considerations, investigating sine triangle carrier pulse width modulation is deemed to be the most auspicious approach. Analysis of the many performance parameters, including load torque, motor speed, and efficiency, an extensive survey is conducted. MATLAB / Simulink is built into the entire system, and simulation results are developed. The purpose of this research is to demonstrate the performance of 9-level and 11-level fed induction motor driving results for Electric Vehicle applications. CHBMLI fed motor drive is better suitable for Induction Motor Drives.

The rapid expansion of the automotive sector promising this technology is going forward and deeply ingrained in human society. Without a doubt, the unpredictable and erratic charging demands of these devices would have an impact on the power grid's scheduling and optimal performance, which may be seen as a new issue. This research introduces an efficient energy management system for hybrid renewable energy in AC/DC microgrids, including electric vehicle (EV) renewable microgrids, utilizing sources such as solar and wind energy. These systems offer promising solutions for enhancing security, reliability, and efficiency in power systems, with the added benefit of reducing greenhouse gas emissions. The proposed optimization approach utilizes Honey Badger Algorithm (HBA) Golden Jackal Optimization (GJO) called Advanced HBA (AHBA) for voltage and power control in hybrid AC/DC microgrids with EVs. This approach addresses challenges faced by existing control methods, such as instability and complexity, by simplifying control through AHBA and facilitating efficient power sharing. Additionally, the suggested technique, which is intended for microgrids with different power profiles, streamlines electric car power references using separate inputs via AHBA. MATLAB simulations of a small-scale hybrid AC/DC microgrid is used to validate the proposed Energy Management System (EMS). The proposed approach achieves an efficiency of 99.023%.

This work deals with a novel high step-up isolated DC-DC boost converter proposed with the help of a couple of inductors. At present, eco-friendly vehicles like Electric-Vehicles (EVs), Hybrid Electric Vehicles (HEVs), and Plug-In Electric Vehicles (PEVs) are effectively used as a feasible solution for achieving high efficiency over fossil-fuel operated vehicles, releases low emissions, global warming, noise-less, low maintenance, so on. In general, the vehicles like Internal Combustion Vehicles (ICE) are propelled by using fossil fuels like petrol, diesel, gasoline, and so on. But the efficiency is very low in this system because of the usage of hydraulic and mechanical components which requires more maintenance, high fuel consumption, toxic emissions, and so on. To overcome these challenges a novel Isolated DC-DC converter has been proposed adopted from Zeta converter with slight modifications. The proposed isolated DC-DC boost converters are used in many applications, due to high step-up gains, low duty ratios, high efficiency, low EMI loss, and low switch stress, so on. It has high power capacity, greater reliable features, and a modular, compact size best suitable for vehicular applications. Employing the high turns ratio of coupled inductor attains high voltage step-up gain as well as the secondary winding is in series with a switched capacitor for obtaining increased voltage level. Following is a detailed description of the proposed Isolated DC-DC converter: (a) The energy in connected inductor leaky inductors may be recycled, increasing overall system efficiency and reducing voltage spikes. (b) Using linked capacitor and switched capacitor methods maximizes the conversion gain ratio. (c) Creates an isolation channel while the solar PV arrays are not working, preventing electric risks to humans. The performance of the system is studied in the simulation environment.

Clean energy requirements for bio-power emphasize the need for technological improvements in the solid polymer electrolyte (SPE) domain. This exceptionally high Clean energy requirements for bio-power emphasize the need for technological improvements in the solid polymer electrolyte (SPE) domain. This exceptionally high

The recent improvements related to the area of electric locomotive, power electronics, assembly processes and manufacturing of machines have increased the robustness and reliability of induction motors. Regardless of the increased availability, the application of induction motors in many fields alleges the need for operating state supervision and condition monitoring. In other words, fault identification at the initial stage helps make appropriate control decisions, influencing product quality as well as providing safety. Inspired by these demands, this work proposes a regression based modeling for the analysis of performance in induction motors. In this approach, the feature extraction process is combined with classification for efficient fault detection. Deep Belief Network (DBN) stacked with multiple Restricted Boltzmann Machine (RBM) is exploited for the robust diagnosis of faults with the adoption of training process. The influences of harmonics over induction motors are identified and the losses are mitigated. The simulation of the suggested approach and its comparison with traditional approaches are executed. An overall accuracy of 99.5% is obtained which in turn proves the efficiency of DBN in detecting faults.