Optimal design of automatic generation control based on BBPSO-tuned PI for a restructured environment
ABSTRACTThis paper intends to model an AGC regulator for a restructured environment using Bare Bone Particle Swarm Optimization (BBPSO). The gain-controlled Proportional–Integral (PI) Controller is designed here to enhance the performance of the BBPSO algorithm along with Gaussian distribution. The...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2024-07-01
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| Series: | Automatika |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/00051144.2024.2325314 |
| _version_ | 1797265992497233920 |
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| author | P. M. Karthikeyan S. Baghya Shree |
| author_facet | P. M. Karthikeyan S. Baghya Shree |
| author_sort | P. M. Karthikeyan |
| collection | DOAJ |
| description | ABSTRACTThis paper intends to model an AGC regulator for a restructured environment using Bare Bone Particle Swarm Optimization (BBPSO). The gain-controlled Proportional–Integral (PI) Controller is designed here to enhance the performance of the BBPSO algorithm along with Gaussian distribution. The practical difficulty in handling the area control error to zero is the sudden variations in load. In practice, the tremendous contribution of deregulation in the power sector causes volatility in frequency and tie-line power deviations. To ensure the robustness of the proposed controller, three different cases of power system transactions have been considered. The performance has been validated by comparing it with Real Coded Genetic Algorithm-tuned PI controller (RCGA-PI) and Differential Evolutionary Algorithm-tuned PI controller (DE-PI) for the five area Thermal-Thermal generation test system. Moreover, the dynamic performance of an extensive range of demands and disturbances of all areas like settling time and overshoot against parametric precariousness has been done on the proposed test system. |
| first_indexed | 2024-04-25T00:53:36Z |
| format | Article |
| id | doaj.art-2bcd5e0790354683a0e5257d08b866d9 |
| institution | Directory Open Access Journal |
| issn | 0005-1144 1848-3380 |
| language | English |
| last_indexed | 2024-04-25T00:53:36Z |
| publishDate | 2024-07-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Automatika |
| spelling | doaj.art-2bcd5e0790354683a0e5257d08b866d92024-03-11T12:27:24ZengTaylor & Francis GroupAutomatika0005-11441848-33802024-07-0165392593410.1080/00051144.2024.2325314Optimal design of automatic generation control based on BBPSO-tuned PI for a restructured environmentP. M. Karthikeyan0S. Baghya Shree1Department of Electrical and Electronics Engineering, Anna University, Chennai, IndiaDepartment of Electrical and Electronics Engineering, Anna University Regional Campus, Madurai, IndiaABSTRACTThis paper intends to model an AGC regulator for a restructured environment using Bare Bone Particle Swarm Optimization (BBPSO). The gain-controlled Proportional–Integral (PI) Controller is designed here to enhance the performance of the BBPSO algorithm along with Gaussian distribution. The practical difficulty in handling the area control error to zero is the sudden variations in load. In practice, the tremendous contribution of deregulation in the power sector causes volatility in frequency and tie-line power deviations. To ensure the robustness of the proposed controller, three different cases of power system transactions have been considered. The performance has been validated by comparing it with Real Coded Genetic Algorithm-tuned PI controller (RCGA-PI) and Differential Evolutionary Algorithm-tuned PI controller (DE-PI) for the five area Thermal-Thermal generation test system. Moreover, the dynamic performance of an extensive range of demands and disturbances of all areas like settling time and overshoot against parametric precariousness has been done on the proposed test system.https://www.tandfonline.com/doi/10.1080/00051144.2024.2325314AGCderegulated power systemBBPSO-tuned PI controllerGRC |
| spellingShingle | P. M. Karthikeyan S. Baghya Shree Optimal design of automatic generation control based on BBPSO-tuned PI for a restructured environment Automatika AGC deregulated power system BBPSO-tuned PI controller GRC |
| title | Optimal design of automatic generation control based on BBPSO-tuned PI for a restructured environment |
| title_full | Optimal design of automatic generation control based on BBPSO-tuned PI for a restructured environment |
| title_fullStr | Optimal design of automatic generation control based on BBPSO-tuned PI for a restructured environment |
| title_full_unstemmed | Optimal design of automatic generation control based on BBPSO-tuned PI for a restructured environment |
| title_short | Optimal design of automatic generation control based on BBPSO-tuned PI for a restructured environment |
| title_sort | optimal design of automatic generation control based on bbpso tuned pi for a restructured environment |
| topic | AGC deregulated power system BBPSO-tuned PI controller GRC |
| url | https://www.tandfonline.com/doi/10.1080/00051144.2024.2325314 |
| work_keys_str_mv | AT pmkarthikeyan optimaldesignofautomaticgenerationcontrolbasedonbbpsotunedpiforarestructuredenvironment AT sbaghyashree optimaldesignofautomaticgenerationcontrolbasedonbbpsotunedpiforarestructuredenvironment |