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All Journal Buletin Ilmiah Sarjana Teknik Elektro International Journal of Robotics and Control Systems
Kunwar Babar Ali
Meerut Institute of Engineering and Technology
Control & Systems Engineering Electrical & Electronics Engineering

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Optimizing Solar Energy Harvesting: Supervised Machine Learning-Driven Peak Power Point Tracking for Diverse Weather Conditions Zaiba Ishrat; Kunwar Babar Ali; Satvik Vats; Surender Kumar
International Journal of Robotics and Control Systems Vol 3, No 4 (2023)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v3i4.1176

Abstract

Solar Power is one of the significant prevalent forms of clean energy due to its perceived to be pollution-free and easily accessible. The market for renewable energy was established by the rapid development in electrical energy consumption and the diminution of conventional energy resources (CER). Under varying weather condition extracted energy from solar system is not constant and maximum. This study suggests the applicability of machine learning algorithm (MLA) in Peak power point tracking (P3T) methods to maximize power of a PV arrangement under varying weather conditions. Machine learning methods optimize peak power point tracking in solar photovoltaic systems by bringing agility, data-driven decision-making, and increased accuracy. MLAs improve the overall efficiency, stability, and dependability of these systems by handling the unpredictability of solar energy production under varying weather circumstances and PSCs Because MLAs are able to learn and adjust to non-linear relationships between solar intensity and PVS output. In this study, the squared multiple squared exponential Gaussian process regression method SGPRA tested in three rapidly varying ecological conditions. The performance of ML-P3T methods is validated using Matlab/Simulink, and the simulation outcome are compared with one of the most used algorithms, the variable step size incremental conductance algorithm (VINA). The Matlab/Simulink findings show that SGPRA operates significantly better under varying weather circumstances, harnessing more peak power efficiency 90%, shorter tracking time 0.13 sec, a mean error of 0.042, and superior stability.
Enhancing JSEG Color Texture Segmentation Using Quaternion Algebra Method Vijay Kumar Sharma; Owais Ahmad Shah; Kunwar Babar Ali; Ravi Kumar H. C.; Ankita Chourasia; Mohammed Mujammil Ahmed
Buletin Ilmiah Sarjana Teknik Elektro Vol. 8 No. 1 (2026): February
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v8i1.14743

Abstract

This work uses quaternion algebra to implement a unique color quantization method on the JSEG color texture segmentation. Typically, RGB color orientations in the composite hyper-planes are inverted to produce the key vectors of the color-space. Because quaternion algebra offers a highly logical way to work with homogeneous coordinates, color is represented as a quaternion in the proposed system. In this illustration, the color pixels are seen like in the 3D space such as point. The recommended model has resulted in a unique quantization method that uses level set techniques and projective geometry. This approach will be used in the JSEG color texture segmentation. This current color quantization technique is splintering clustering mechanism since it makes use of the binary quaternion moment preserving threshold technique. With this technique, color constancy throughout the spectrum and in the physical space are taken into account when they divide the color clusters located inside the RGB cube. The segmentation results are contrasted with JSEG and some of the recent established segmentation methods. These comparisons demonstrate how the proposed quantization approach strengthens the JSEG segmentation.
Co-Authors Ankita Chourasia Mohammed Mujammil Ahmed Owais Ahmad Shah Ravi Kumar H. C. Satvik Vats Surender Kumar Vijay Kumar Sharma Zaiba Ishrat