Optimized PID controller and model order reduction of reheated turbine for load frequency control using teaching learning-based optimization
Abstract Load frequency control (LFC) systems in power grids face challenges in maintaining stability while managing computational complexity. This research presents an optimized approach combining model order reduction techniques with Teaching Learning-Based Optimization (TLBO) for PID controller t...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-87866-z |
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| author | Anurag Singh Shekhar Yadav Nitesh Tiwari Dinesh Kumar Nishad Saifullah Khalid |
| author_facet | Anurag Singh Shekhar Yadav Nitesh Tiwari Dinesh Kumar Nishad Saifullah Khalid |
| author_sort | Anurag Singh |
| collection | DOAJ |
| description | Abstract Load frequency control (LFC) systems in power grids face challenges in maintaining stability while managing computational complexity. This research presents an optimized approach combining model order reduction techniques with Teaching Learning-Based Optimization (TLBO) for PID controller tuning in single-area LFC systems. Three reduction methods—Routh Approximation, Balanced Truncation, and Hankel Norm Approximation—were implemented to reduce system order from 4th to 2nd order, achieving a 47.3% reduction in computational time. The TLBO-optimized PID controller was compared with conventional tuning methods (Ziegler-Nichols, AMIGO, S-IMC, and CHR), demonstrating superior performance with a 38.2% decrease in settling time and 42.7% reduction in peak overshoot. The Routh Approximation method exhibited optimal performance with minimum settling time (2.8s) and peak overshoot (8.4%). Sensitivity analysis revealed stable system behavior with phase margin maintained at 84.25 degrees across parameter variations. The proposed approach achieved a 56.8% reduction in Integral Square Error compared to conventional methods, establishing its effectiveness for modern power grid applications. This research provides a robust framework for implementing efficient load frequency control in power systems while maintaining system stability and performance. |
| format | Article |
| id | doaj-art-617408b7e26e4b3cbfbeaca8ebd7475a |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-617408b7e26e4b3cbfbeaca8ebd7475a2025-02-02T12:18:05ZengNature PortfolioScientific Reports2045-23222025-01-0115113410.1038/s41598-025-87866-zOptimized PID controller and model order reduction of reheated turbine for load frequency control using teaching learning-based optimizationAnurag Singh0Shekhar Yadav1Nitesh Tiwari2Dinesh Kumar Nishad3Saifullah Khalid4Department of Electrical Engineering, Madan Mohan Malaviya University of TechnologyDepartment of Electrical Engineering, Madan Mohan Malaviya University of TechnologyDepartment of Electrical Engineering, Madan Mohan Malaviya University of TechnologyDepartment of Electrical Engineering, Madan Mohan Malaviya University of TechnologyIBM Multi Activities Co. Ltd.Abstract Load frequency control (LFC) systems in power grids face challenges in maintaining stability while managing computational complexity. This research presents an optimized approach combining model order reduction techniques with Teaching Learning-Based Optimization (TLBO) for PID controller tuning in single-area LFC systems. Three reduction methods—Routh Approximation, Balanced Truncation, and Hankel Norm Approximation—were implemented to reduce system order from 4th to 2nd order, achieving a 47.3% reduction in computational time. The TLBO-optimized PID controller was compared with conventional tuning methods (Ziegler-Nichols, AMIGO, S-IMC, and CHR), demonstrating superior performance with a 38.2% decrease in settling time and 42.7% reduction in peak overshoot. The Routh Approximation method exhibited optimal performance with minimum settling time (2.8s) and peak overshoot (8.4%). Sensitivity analysis revealed stable system behavior with phase margin maintained at 84.25 degrees across parameter variations. The proposed approach achieved a 56.8% reduction in Integral Square Error compared to conventional methods, establishing its effectiveness for modern power grid applications. This research provides a robust framework for implementing efficient load frequency control in power systems while maintaining system stability and performance.https://doi.org/10.1038/s41598-025-87866-zPID controllerTeaching learning based optimizationIntegral Square ErrorLoad frequency control |
| spellingShingle | Anurag Singh Shekhar Yadav Nitesh Tiwari Dinesh Kumar Nishad Saifullah Khalid Optimized PID controller and model order reduction of reheated turbine for load frequency control using teaching learning-based optimization Scientific Reports PID controller Teaching learning based optimization Integral Square Error Load frequency control |
| title | Optimized PID controller and model order reduction of reheated turbine for load frequency control using teaching learning-based optimization |
| title_full | Optimized PID controller and model order reduction of reheated turbine for load frequency control using teaching learning-based optimization |
| title_fullStr | Optimized PID controller and model order reduction of reheated turbine for load frequency control using teaching learning-based optimization |
| title_full_unstemmed | Optimized PID controller and model order reduction of reheated turbine for load frequency control using teaching learning-based optimization |
| title_short | Optimized PID controller and model order reduction of reheated turbine for load frequency control using teaching learning-based optimization |
| title_sort | optimized pid controller and model order reduction of reheated turbine for load frequency control using teaching learning based optimization |
| topic | PID controller Teaching learning based optimization Integral Square Error Load frequency control |
| url | https://doi.org/10.1038/s41598-025-87866-z |
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