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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
Kavitha, P.
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Control & Systems Engineering Electrical & Electronics Engineering

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Machine learning applications for predicting system production in renewable energy Somasundaram, Deepa; Muthukumar, R.; Rajavinu, N.; Ramaiyan, Kalaivani; Kavitha, P.
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 15, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v15.i3.pp1925-1933

Abstract

Renewable energy systems play pivotal role in addressing the global challenge of sustainable energy production. Efficiently harnessing energy from renewable sources requires accurate prediction models to optimize system production. This paper delves into the realm of predictive modeling, focusing on the utilization of machine learning techniques to forecast system production in renewable energy systems. The investigation incorporates a range of factors such as wind speed, sunshine, air pressure, radiation, air temperature, and relative air humidity, alongside temporal data ('Date-Hour (NMT)'). These factors undergo rigorous curation and preprocessing to ensure the reliability and quality of the predictive model. Various machine learning algorithms, including linear regression, decision tree, random forest, and support vector machine (SVM), are employed to examine the relationships between these factors and system production. The findings are assessed using metrics such as mean squared error, mean absolute error, and R-squared. Through comparative analysis, the study illuminates the strengths and limitations of each algorithm, providing valuable insights into their suitability for renewable energy forecasting. This paper adds to renewable energy research by examining how machine learning predicts system production. The insights are valuable for researchers, practitioners, and policymakers in sustainable energy development.
Wind turbine defect detection using deep learning Somasundaram, Deepa; Vanitha, M.; Kumar, T. Sathish; Adaikalam, I. Arul Doss; Kavitha, P.; Kalaivani, R.
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 16, No 2: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v16.i2.pp1348-1355

Abstract

Wind turbines play a critical role in the generation of renewable energy, but their maintenance and inspection, especially in large-scale wind farms, present significant challenges. Traditionally, wind turbines have been inspected manually, a process that is not only time-consuming but also costly and risky. Unmanned aerial vehicles (UAVs) have emerged as an efficient alternative, offering a safer and more economical means of gathering inspection data. However, the challenge lies in the manual analysis of the collected data, which demands expertise and considerable time. This paper proposes using object detection algorithms, specifically YOLOv8, to automate the detection of wind turbines and their defects, streamlining the inspection process. The model is trained on wind turbine images to identify potential faults such as cracks and corrosion. This approach aims to increase the accuracy and efficiency of wind turbine maintenance, ensuring prompt defect detection and reducing both operational costs and downtime.
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.
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.
Comparative analysis of multi-output machine learning models for solar irradiance and wind speed forecasting: A case study in Tamil Nadu, India Selvi, S.; Shanti, N.; Dhandapani, Lakshmi; Bhoopathi, M.; Kumar, T. Sathish; Kavitha, P.
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 17, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v17.i1.pp786-796

Abstract

The growing share of wind and solar energy has created challenges in electrical networks, mainly due to intermittency, fluctuations, and uncertainty. These issues affect power system stability, grid operations, and the balance between supply and demand. To address this, accurate prediction of solar irradiance and wind speed is critical for integrating renewable energy into power systems. In this study, we propose a multi-output machine learning approach to predict both global horizontal irradiance (GHI) and wind speed simultaneously. The study uses historical meteorological data obtained from the National Solar Radiation Database (NSRDB) for Tamil Nadu, India. Six regression algorithms: linear regression, gradient boosting, random Forest, extreme gradient boosting (XGB), light gradient boosting machine (LightGBM), and categorical boosting (CatBoost) are tested under identical conditions. Model hyperparameters were tuned using GridSearchCV and Bayesian optimization to ensure robust performance. Before modeling, a comprehensive statistical analysis, including input feature distribution and correlation analysis, was conducted. Model accuracy was evaluated using RMSE, MAE, and R² metrics on both training and testing datasets. The results showed that ensemble tree-based methods outperformed the baseline linear model. Among them, CatBoost produced the best results for GHI prediction, while random forest delivered the most reliable wind speed forecasts, demonstrating strong predictive capability for renewable energy applications.
Machine learning-based real-time power stability optimization for photovoltaic systems using hybrid inductor-capacitor patterns Kathirvel, Jayashree; Pushpa, S.; Kavitha, P.; Sureshkumar, Sathya; Andi, Kannan; Pramasivam, Prabakaran
International Journal of Applied Power Engineering (IJAPE) Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v15.i1.pp248-256

Abstract

Photovoltaic (PV) systems often face real-time power stability challenges due to rapid fluctuations in solar irradiance and varying load conditions, which conventional control strategies struggle to manage effectively. Addressing this limitation, the present study proposes a novel machine learning-based control framework integrated with a hybrid inductor-capacitor (LC) network to enhance dynamic power regulation. The proposed system employs predictive algorithms to adjust LC parameters in real time, enabling adaptive voltage and current stabilization during transient conditions. Simulation results validate the model's effectiveness, showing a 58% reduction in power fluctuation (from 12% to 5%) and consistent improvement in voltage stability index (VSI), maintaining values above 0.95 compared to 0.88-0.93 in traditional systems. Moreover, the approach reduces computation time by 66% (150 ms versus 450 ms for PID-based systems), supporting faster and more efficient control actions. These outcomes demonstrate that the proposed intelligent control strategy significantly improves energy efficiency, voltage stability, and responsiveness in PV systems, offering a scalable solution for reliable grid integration of renewable energy sources.
Co-Authors Adaikalam, I. Arul Doss Andi, Kannan Bhoopathi, M. Devaraj, V. Dhandapani, Lakshmi Ezhilarasi, G. Kalaivani, R. Kathirvel, Jayashree Kumar, T. Sathish Lakshmi, D. Muthukumar, R. Prakash, G. Pramasivam, Prabakaran Pramila, V. Pushpa, S. Rajavinu, N. Ramaiyan, Kalaivani Selvi, S. Shanti, N. Somasundaram, Deepa Sureshkumar, Sathya Vanitha, M.