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All Journal International Journal of Power Electronics and Drive Systems (IJPEDS)
Kumar, T. Sathish
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Control & Systems Engineering Electrical & Electronics Engineering

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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.
Enhanced integration of renewable energy and smart grid efficiency with data-driven solar forecasting employing PCA and machine learning Kathirvel, Jayashree; Sreenivasan, Pushpa; Vanitha, M.; Mohammed, Soni; Kumar, T. Sathish; Adaikalam, I. Arul Doss
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.pp2645-2654

Abstract

A significant obstacle to preserving grid stability and incorporating renewable energy into smart grids is variations in solar irradiation. To improve solar power management's dependability, this research proposes a short-term solar forecasting framework powered by AI. Multiple machine learning models, such as long short-term memory (LSTM), random forest (RF), gradient boosting (GB), AdaBoost, neural networks (NN), K-Nearest neighbor (KNN), and linear regression (LR), are integrated into the suggested system, which also uses principal component analysis (PCA) for dimensionality reduction. The Abiod Sid Cheikh station in Algeria (2019-2021) provided real-world data for the model's validation. With a two-hour-ahead RMSE of 0.557 kW/m², AdaBoost had the most accuracy, whereas LR had the lowest, at 0.510 kW/m². In addition to increasing computing efficiency, PCA preserved 99.3% of the data volatility. In addition to increasing computing efficiency, PCA preserved 99.3% of the data volatility. These findings highlight the efficiency of hybrid AI models based on PCA for accurate forecasting, which is crucial for smart grid stability.
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.
Co-Authors Adaikalam, I. Arul Doss Bhoopathi, M. Dhandapani, Lakshmi Kalaivani, R. Kathirvel, Jayashree Kavitha, P. Mohammed, Soni Selvi, S. Shanti, N. Somasundaram, Deepa Sreenivasan, Pushpa Vanitha, M.