Article Per Year (5 Year)
p-Index From 2020 - 2025
0.408
P-Index
This Author published in this journals
All Journal International Journal of Power Electronics and Drive Systems (IJPEDS)
Ramaiyan, Kalaivani
Unknown Affiliation
Control & Systems Engineering Electrical & Electronics Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Optimal fuzzy controller for speed control of DC drive using salp swarm algorithm Somasundaram, Deepa; Arumugham, Sasikala; Ramalingam, Puviarasi; Dhandapani, Kirubakaran; Ramaiyan, Kalaivani
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.pp1951-1958

Abstract

The inherent non-linearity of the system being investigated highlights the limitations of traditional proportional integral or PI tuning approaches. Consequently, the primary objective of this study is to construct and refine the PI controller by leveraging the salp swarm algorithm, aiming to enhance the performance of the DC drive output. Through the application of the salp swarm algorithm, the fuzzy PI controller undergoes dynamic online modifications, leading to optimal results. The controller's superior performance is achieved by employing an optimization approach to identify the optimal set of solutions for the Fuzzy PI parameters. Rigorous simulations are conducted to comprehensively evaluate the proposed salp swarm algorithm technique, assessing its viability and efficacy in real-world. Thorough simulations assess the viability of the salp swarm algorithm, evaluating its effectiveness in real-world applications. The study demonstrates the methodology's reliability through comparative analyses of DC/DC converters against alternative methods. In non-linear systems like the DC drive, innovative optimization strategies are shown to significantly boost PI controller performance. The findings offer valuable insights for advanced control system design.
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.
Co-Authors Arumugham, Sasikala Dhandapani, Kirubakaran Kavitha, P. Muthukumar, R. Rajavinu, N. Ramalingam, Puviarasi Somasundaram, Deepa