Automatic Voltage Regulator Betterment Based on a New Fuzzy FOPI+FOPD Tuned by TLBO
This paper presents a novel Fuzzy Logic Controller (FLC) framework aimed at enhancing the performance and stability of Automatic Voltage Regulators (AVRs) in power systems. The proposed system combines fuzzy control theory with the Fractional Order Proportional Integral Derivative (FOPID) technique...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Fractal and Fractional |
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| Online Access: | https://www.mdpi.com/2504-3110/9/1/21 |
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| _version_ | 1832588436420165632 |
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| author | Mokhtar Shouran Mohammed Alenezi |
| author_facet | Mokhtar Shouran Mohammed Alenezi |
| author_sort | Mokhtar Shouran |
| collection | DOAJ |
| description | This paper presents a novel Fuzzy Logic Controller (FLC) framework aimed at enhancing the performance and stability of Automatic Voltage Regulators (AVRs) in power systems. The proposed system combines fuzzy control theory with the Fractional Order Proportional Integral Derivative (FOPID) technique and employs cascading control theory to significantly improve reliability and robustness. The unique control architecture, termed Fuzzy Fractional Order Proportional Integral (PI) plus Fractional Order Proportional Derivative (PD) plus Integral (Fuzzy FOPI+FOPD+I), integrates advanced control methodologies to achieve superior performance. To optimize the controller parameters, the Teaching–Learning-Based Optimization (TLBO) algorithm is utilized in conjunction with the Integral Time Absolute Error (ITAE) objective function, ensuring precise tuning for optimal control behavior. The methodology is validated through comparative analyses with controllers reported in prior studies, highlighting substantial improvements in performance metrics. Key findings demonstrate significant reductions in peak overshoot, peak undershoot, and settling time, emphasizing the proposed controller’s effectiveness. Additionally, the robustness of the controller is extensively evaluated under challenging scenarios, including parameter uncertainties and load disturbances. Results confirm its ability to maintain stability and performance across a wide range of conditions, outperforming existing methods. This study presents a notable contribution by introducing an innovative control structure that addresses critical challenges in AVR systems, paving the way for more resilient and efficient power system operations. |
| format | Article |
| id | doaj-art-b657f953bef14513b495c08976c11e83 |
| institution | Kabale University |
| issn | 2504-3110 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Fractal and Fractional |
| spelling | doaj-art-b657f953bef14513b495c08976c11e832025-01-24T13:33:24ZengMDPI AGFractal and Fractional2504-31102024-12-01912110.3390/fractalfract9010021Automatic Voltage Regulator Betterment Based on a New Fuzzy FOPI+FOPD Tuned by TLBOMokhtar Shouran0Mohammed Alenezi1Libyan Centre for Engineering Research and Information Technology (LCERIT), Bani Walid P.O. Box 38645, LibyaWolfson Centre for Magnetics, School of Engineering, Cardiff University, Cardiff CF24 3AA, UKThis paper presents a novel Fuzzy Logic Controller (FLC) framework aimed at enhancing the performance and stability of Automatic Voltage Regulators (AVRs) in power systems. The proposed system combines fuzzy control theory with the Fractional Order Proportional Integral Derivative (FOPID) technique and employs cascading control theory to significantly improve reliability and robustness. The unique control architecture, termed Fuzzy Fractional Order Proportional Integral (PI) plus Fractional Order Proportional Derivative (PD) plus Integral (Fuzzy FOPI+FOPD+I), integrates advanced control methodologies to achieve superior performance. To optimize the controller parameters, the Teaching–Learning-Based Optimization (TLBO) algorithm is utilized in conjunction with the Integral Time Absolute Error (ITAE) objective function, ensuring precise tuning for optimal control behavior. The methodology is validated through comparative analyses with controllers reported in prior studies, highlighting substantial improvements in performance metrics. Key findings demonstrate significant reductions in peak overshoot, peak undershoot, and settling time, emphasizing the proposed controller’s effectiveness. Additionally, the robustness of the controller is extensively evaluated under challenging scenarios, including parameter uncertainties and load disturbances. Results confirm its ability to maintain stability and performance across a wide range of conditions, outperforming existing methods. This study presents a notable contribution by introducing an innovative control structure that addresses critical challenges in AVR systems, paving the way for more resilient and efficient power system operations.https://www.mdpi.com/2504-3110/9/1/21AVRTLBOfuzzy FOPI+FOPD+IITAEpower system stability |
| spellingShingle | Mokhtar Shouran Mohammed Alenezi Automatic Voltage Regulator Betterment Based on a New Fuzzy FOPI+FOPD Tuned by TLBO Fractal and Fractional AVR TLBO fuzzy FOPI+FOPD+I ITAE power system stability |
| title | Automatic Voltage Regulator Betterment Based on a New Fuzzy FOPI+FOPD Tuned by TLBO |
| title_full | Automatic Voltage Regulator Betterment Based on a New Fuzzy FOPI+FOPD Tuned by TLBO |
| title_fullStr | Automatic Voltage Regulator Betterment Based on a New Fuzzy FOPI+FOPD Tuned by TLBO |
| title_full_unstemmed | Automatic Voltage Regulator Betterment Based on a New Fuzzy FOPI+FOPD Tuned by TLBO |
| title_short | Automatic Voltage Regulator Betterment Based on a New Fuzzy FOPI+FOPD Tuned by TLBO |
| title_sort | automatic voltage regulator betterment based on a new fuzzy fopi fopd tuned by tlbo |
| topic | AVR TLBO fuzzy FOPI+FOPD+I ITAE power system stability |
| url | https://www.mdpi.com/2504-3110/9/1/21 |
| work_keys_str_mv | AT mokhtarshouran automaticvoltageregulatorbettermentbasedonanewfuzzyfopifopdtunedbytlbo AT mohammedalenezi automaticvoltageregulatorbettermentbasedonanewfuzzyfopifopdtunedbytlbo |