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All Journal International Journal of Power Electronics and Drive Systems (IJPEDS) International Journal of Applied Power Engineering (IJAPE) Indonesian Journal of Electrical Engineering and Computer Science Journal of Applied Data Sciences
Lakshmi, D.
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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Lion swarm optimization for grid connected PV system with improved SEPIC Annapandi, P.; Lakshmi, D.; Santhoshi, B. Kavya; Annapoorani, P.
International Journal of Applied Power Engineering (IJAPE) Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v13.i3.pp603-615

Abstract

The wide deployment of grid-connected renewable energy system has piqued immense attention recently, in response to rising electricity consumption, diminishing fossil fuel reserves in addition to the need for reducing carbon emissions. Among the available sources of renewable energy, photovoltaic (PV) power generation is the most promising technology with enormous potential and easy access. This paper presents an optimum control technique for grid connected PV systems. The improved single ended primary inductor converter (SEPIC) controls and regulates PV output power to the optimum voltage level. The working of the improved SEPIC is controlled by a proportional-integral (PI) controller optimized by meta-heuristic technique of lion swarm optimization (LSO). The constant output from the converter is then supplied to the power grid through a single-phase voltage source inverter (1? VSI). The effectiveness of the proposed control strategy is ascertained using hardware validation with DSPIC3050FPGA controller and MATLAB simulation generating a reduced total harmonic distortion (THD) of 3.9% and 2.9%, respectively. Furthermore, the proposed system generates an enhanced voltage gain of 1:10 and an efficiency of 96%.
Impact of FACTS Devices on Reactive Power Optimization in Hybrid Renewable-Grid Networks Rajasree, R.; Lakshmi, D.; Batumalay, M.
Journal of Applied Data Sciences Vol 6, No 3: September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i3.743

Abstract

Renewable energy integration with conventional electric power networks creates power-quality and stability difficulties because of their inherent volatility. The reliability improvement of hybrid renewable-grid systems depends heavily on reactive power optimization for achieving voltage control as well as loss reduction. The research explores the application of Flexible AC Transmission System (FACTS) devices with special emphasis on Distribution Static Compensator (DSTATCOM) devices for distributing reactive power compensation at the distribution level. The optimization process utilizes Particle Swarm Optimization (PSO) because it demonstrates both quick convergence and strong abilities for global search within nonlinear systems. The PSO algorithm functions to determine the perfect settings of the DSTATCOM device that enables voltage regulation within safety bounds and improves power factor performance. The hybrid system connects PV array components with wind turbines for power management together with the main grid while dealing with fluctuating load requirements. Under optimized conditions simulation output shows that DSTATCOM reduces reactive power requirements in substantial amounts. DSTATCOM's implementation enables the system to achieve better voltage security together with diminished power losses and superior load power factor levels. Detailed research shows that DSTATCOM proves efficient while being attached to the main grid for real-time compensation operations. The PSO system enables it to function efficiently throughout changing conditions of power generation and load requirements. Smart grid efficiency along with resilience advances because of the combined operation of FACTS devices and swarm intelligence methods. Through its proposed method the system ensures lasting grid sustainability and manages renewable resources intermittency effectively for process innovation.
Data-Driven Optimization of UPQC Performance for Solar PV Systems in Weak Grids Using Simulation and Predictive Modeling Rajasree, R.; Lakshmi, D.; Batumalay, M.
Journal of Applied Data Sciences Vol 6, No 3: September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i3.742

Abstract

The integration of solar photovoltaic (PV) systems into weak power grids presents significant challenges due to low short circuit ratios (SCR), resulting in voltage instability, high harmonic distortion, and diminished fault tolerance. This study proposes a data-driven framework to enhance grid stability and power quality by employing a Unified Power Quality Conditioner (UPQC) integrated with Proportional-Integral (PI) controllers. A comprehensive simulation model was developed using MATLAB/Simulink and validated through hardware-in-the-loop (HIL) experiments. Key electrical performance metrics—such as voltage profiles, total harmonic distortion (THD), and reactive power—were collected and analyzed. To enhance system insight, the dataset was further processed using statistical analysis and predictive modeling techniques to evaluate control response under varying solar irradiance and load conditions. The results demonstrate that the UPQC system maintains stable voltage, reduces THD to within IEEE-519 standards, and improves power factor to 0.98. This research highlights the potential of combining power electronics control with data-centric evaluation to ensure reliable renewable energy integration in weak grid environments. The proposed system contributes toward developing intelligent grid-support solutions for sustainable energy transitions and process innovation.
Automated defect detection in submersible pump impellers using image classification Somasundaram, Deepa; Pramila, V.; Ezhilarasi, G.; Lakshmi, D.; Kavitha, P.; Kalaivani, R.
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 2: November 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i2.pp1158-1166

Abstract

Casting is a crucial manufacturing process used to produce complex metal parts, but it is often plagued by defects such as cracks, porosity, shrinkage, and cold shuts, which can compromise quality and lead to financial losses. Traditional visual inspection methods for detecting these defects are slow and prone to human error, making them inefficient for large-scale production. This project proposes automating the defect detection process using advanced AI-powered non-destructive testing (NDT) techniques. Specifically, convolutional neural networks (CNNs), a deep learning model, are employed for real-time visual inspection of castings. CNNs, trained on high-resolution images, can accurately identify surface defects such as cracks, scratches, and dimensional irregularities, significantly improving inspection speed and accuracy. The performance metrics of the system include defect detection accuracy, false positive and false negative rates, processing time, and scalability for high-volume production environments. By minimizing human intervention, this automated system reduces error rates, enhances product quality, and lowers production costs. Furthermore, the real-time capabilities of CNNs allow for rapid feedback, preventing defective parts from advancing through the production line. Overall, the integration of AI-based vision systems boosts efficiency, sustainability, and profitability in manufacturing, ensuring castings meet customer specifications with minimal errors.
Adaptive ANFIS-based MPPT for PV-powered green ships with high gain SEPIC converter Jegadeeswari, G.; Govindaraju, Rohini; Balakumar, D.; Lakshmi, D.; Marisargunam, S.; Batumalay, M.; Kirubadurai, B.
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 16, No 4: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v16.i4.pp2768-2779

Abstract

To align with global climate goals, the International Maritime Organization (IMO) has enforced strict measures to reduce greenhouse gas emissions from the shipping industry by promoting energy efficiency and cleaner propulsion methods. Ship engines remain major contributors to environmental pollution due to their dependence on fossil fuels and inefficient propulsion systems, highlighting the need for clean and sustainable alternatives. This study aims to design a renewable energy-based marine power system that effectively stores and utilizes solar energy, improving overall efficiency and reducing emissions for process innovation. A hybrid setup was developed using photovoltaic (PV) panels, batteries, and a bidirectional DC-DC converter to enable flexible power flow during both charging and discharging cycles. An adaptive neuro-fuzzy inference system (ANFIS)-based maximum power point tracking (MPPT) algorithm was employed alongside a SEPIC converter to enhance energy extraction from the PV system under dynamic conditions. The integrated system achieved a power extraction efficiency of 97.12%, confirming the effectiveness of the ANFIS-based MPPT strategy and showcasing the viability of intelligent renewable energy solutions in maritime applications.
Comparative analysis of optimization techniques for optimal EV charging station placement Somasundaram, Deepa; Prakash, G.; Rajavinu, N.; Lakshmi, D.; Kavitha, P.; Devaraj, V.
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 16, No 4: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v16.i4.pp2860-2867

Abstract

The optimal placement of electric vehicle (EV) charging stations plays a crucial role in improving accessibility, reducing travel distances, and minimizing infrastructure costs in smart urban planning. This study presents a comparative analysis of traditional optimization techniques-such as linear programming (LP), particle swarm optimization (PSO), k-means clustering, and greedy heuristic methods-alongside a machine learning-based approach using genetic algorithms (GA). A machine learning framework is implemented to simulate EV charging demand, optimize station deployment, and incorporate real-world constraints like cost, grid capacity, and user travel penalties. The results demonstrate that GA achieves superior performance in balancing cost-efficiency and user convenience, outperforming traditional techniques in solution quality under dynamic demand conditions. PSO and LP provide faster convergence but are less adaptive to changing parameters. The study highlights the potential of integrating machine learning into infrastructure planning and provides actionable insights for urban planners and policymakers in developing resilient and intelligent EV charging networks.
Co-Authors Annapandi, P. Annapoorani, P. Balakumar, D. Batumalay, M. Devaraj, V. Ezhilarasi, G. Govindaraju, Rohini Jegadeeswari, G. Kalaivani, R. Kavitha, P. Kirubadurai, B. Marisargunam, S. Prakash, G. Pramila, V. Rajasree, R. Rajavinu, N. Santhoshi, B. Kavya Somasundaram, Deepa