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All Journal International Journal of Power Electronics and Drive Systems (IJPEDS) Indonesian Journal of Electrical Engineering and Computer Science
Kathirvel, Jayashree
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Control & Systems Engineering Electrical & Electronics Engineering

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Dual axis solar tracking system for agriculture applications using machine learning Somasundaram, Deepa; Dhandapani, Lakshmi; Kathirvel, Jayashree; Sagayaraj, Marlin; Jagadeesan, Vijay Anand
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 15, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v15.i1.pp631-638

Abstract

The three most basic amenities required for human survival are food, shelter and clothing. In today's tech-savvy generation, these have experienced a great deal of scientific advancement. Unfortunately, agriculture is still more man power-oriented. So they have to rely on the hit and trial method to learn from experience which leads to waste of time. In proposed work includes an automated system using dual axis solar tracking system and gives crop recommendation for different types of soil to yield maximum. The suggested system is a dual-axis solar tracker based on machine learning that is intended to considerably increase the effectiveness of energy harvesting. The approach makes use of the logistic regression algorithm (LR) to do this. This novel strategy tries to maximize the solar panel's ability to produce energy, leading to increased energy yields. The quality of soil is predicted by using suitable sensors for crop recommendation. The data’s are temperature, humidity, pH of soil, nitrogen, phosphorous & potassium in soil and rainfall in soil are considered. For crop recommendation six algorithms- SVM, KNN, Native Bays, Logistic Regression, Decision Tree classifier, Random Forest Classifier are applied and tested. It is found that random forest classifier gave us excellent results.
Enhancing power quality in solar-wind grid-connected systems through soft computing techniques Kathirvel, Jayashree; Sreenivasan, Pushpa; Mohammed, Soni; Sethi, Rabinarayan; Syamala, Maganti
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 15, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v15.i4.pp2493-2500

Abstract

This work intends to improve estimates of solar and wind energy generation through the application of resilient backpropagation control and substantial power evolution strategy (SPES) algorithms. In comparison to particle swarm optimization and genetic algorithms, the main goal is to minimize predicting mistakes. These methods increase grid reliability by lowering total harmonic distortion (THD) and improving power quality when integrated with the IEEE-9 bus standard. In order to evaluate the hybrid system's transient and steady-state reactions, the study also highlights the importance of bolstering operation and control. A revolutionary deep learning-based approach is also suggested for predicting wind and solar hybrid energy. The power grid's efficiency and dependability in handling renewable energy sources have significantly improved, according to the results.
Smart solar maintenance: IoT-enabled automated cleaning for enhanced photovoltaic efficiency Ramalingam, Puviarasi; Kathirvel, Jayashree; Adaikalam, Arul Doss; Somasundaram, Deepa; Sreenivasan, Pushpa
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 1: July 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i1.pp14-19

Abstract

This innovative project aims to increase the effectiveness and user experience of solar panel systems by introducing a state-of-the-art dust and speck removal system. Leveraging cutting-edge technology, the system demonstrates a remarkable 32% increase in power output compared to dirty solar panels. The approach is characterized by its reliance on the universe as the system controller, reducing the need for manual intervention and minimizing the workforce required for panel cleaning. The proposed timed system utilizes water and wipers, facilitated by internet of things (IoT) technology, microcontrollers, and sensor modules for efficient and automated operation. An Android application provides user control and notifications about ongoing processes. The system’s adaptability for various settings is emphasized, offering a portable solution. The smart IoT based automatic solar panel cleaning ensures reliable performance, underscoring the project’s commitment to improve scalability, cost-efficiency, performance, integrity, and consistency.
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.
Co-Authors Adaikalam, Arul Doss Adaikalam, I. Arul Doss Dhandapani, Lakshmi Jagadeesan, Vijay Anand Kumar, T. Sathish Mohammed, Soni Ramalingam, Puviarasi Sagayaraj, Marlin Sethi, Rabinarayan Somasundaram, Deepa Sreenivasan, Pushpa Syamala, Maganti Vanitha, M.