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

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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.
Leveraging machine learning for sustainable integration of renewable energy generation Sreenivasan, Pushpa; Ganesan, Keerthiga; Fawad, Iffath; Sureshkumar, Sathya; Dhandapani, Kirubakaran
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1347-1355

Abstract

Long-term economic benefits and sustainability are provided by the integration of renewable energy sources (RESs) into electrical networks. However, because of their intermittent nature and reliance on environmental factors, RESs pose issues in production and consumption balance. Because renewable energy sources like wind and solar are unpredictable, forecasting their output is essential for planning purposes and maintaining grid stability. This thesis focuses on developing effective instruments and algorithms to improve renewable energy generation estimates and handle abnormalities in consumption. These tools and algorithms include maximum power point tracking and machine learning models like random forest (RF), adaptive boosting (AdaBoost), and extreme gradient boosting (XGBoost). The methods' effectiveness is confirmed by accuracies higher than 80%, which provides speedier and more user-friendly solutions in comparison to the traditional ways. In the end, our effort seeks to offer practical instruments for anticipatory modelling and mitigating intermittentness in renewable energy sources, enabling their assimilation into current power structures to adequately supply energy requirements in a sustainable manner.
Co-Authors Arumugham, Sasikala Fawad, Iffath Ganesan, Keerthiga Ramaiyan, Kalaivani Ramalingam, Puviarasi Somasundaram, Deepa Sreenivasan, Pushpa Sureshkumar, Sathya