Soft Computing-Based Damping Controllers With Online Parameters Tuning for Stability Enhancement of Power Systems
<p dir="ltr">Power system stability continues to be a major challenge as modern grids grow more complex, uncertain, and increasingly reliant on renewable energy sources. This paper presents two new Neuro-Fuzzy controllers for Static Synchronous Compensators (STATCOMs): the Direct Che...
Saved in:
| Main Author: | |
|---|---|
| Other Authors: | , , , , |
| Published: |
2025
|
| Subjects: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1864513523560742912 |
|---|---|
| author | Farman Ullah Jan (22278835) |
| author2 | Rabiah Badar (5090327) Ahmad Sami Al-Shamayleh (17122985) Akie Uehara (22278838) Tomonobu Senjyu (12757166) Adnan Akhunzada (20151648) |
| author2_role | author author author author author |
| author_facet | Farman Ullah Jan (22278835) Rabiah Badar (5090327) Ahmad Sami Al-Shamayleh (17122985) Akie Uehara (22278838) Tomonobu Senjyu (12757166) Adnan Akhunzada (20151648) |
| author_role | author |
| dc.creator.none.fl_str_mv | Farman Ullah Jan (22278835) Rabiah Badar (5090327) Ahmad Sami Al-Shamayleh (17122985) Akie Uehara (22278838) Tomonobu Senjyu (12757166) Adnan Akhunzada (20151648) |
| dc.date.none.fl_str_mv | 2025-09-19T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/access.2025.3612288 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Soft_Computing-Based_Damping_Controllers_With_Online_Parameters_Tuning_for_Stability_Enhancement_of_Power_Systems/31289218 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Control engineering, mechatronics and robotics Electrical engineering Engineering practice and education Information and computing sciences Artificial intelligence Machine learning Chebyshev wavelet neural networks Nonlinear system Soft computing STATCOM Power system stability |
| dc.title.none.fl_str_mv | Soft Computing-Based Damping Controllers With Online Parameters Tuning for Stability Enhancement of Power Systems |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Power system stability continues to be a major challenge as modern grids grow more complex, uncertain, and increasingly reliant on renewable energy sources. This paper presents two new Neuro-Fuzzy controllers for Static Synchronous Compensators (STATCOMs): the Direct Chebyshev Wavelet-Based Neuro-Fuzzy Controller (DNF-CW), which adapts parameters online using fixed-structure rules, and the Indirect Chebyshev Wavelet-Based Neuro-Fuzzy Controller (IDNF-CW), which uses an online identifier to measure plant sensitivity. Chebyshev wavelet-based neural networks are utilized in the consequent part of both controllers to enable accurate local modeling and improved damping performance. The proposed methods are evaluated using a Single Machine Infinite Bus (SMIB) system and the IEEE 9-bus multi-machine system under a variety of fault and loading conditions. Benchmark comparisons include a conventional Indirect Adaptive Neuro-Fuzzy Takagi–Sugeno–Kang (IDNF-TSK) based controller, a configuration without STATCOM (No STATCOM), and a configuration with STATCOM but without auxiliary control (No Control). In the SMIB scenario, the IDNF-CW achieves a 40% reduction in settling time compared to the IDNF-TSK. In the more demanding multi-machine setup, the IDNF-CW restores stability within 3 seconds after a sequence of faults, outperforming DNF-CW and IDNF-TSK. Additionally, reductions of over 53% in the Integral of Time-weighted Absolute Error (ITAE) and 36% in the Integral of Absolute Error (IAE) are observed. These tests under multiple fault conditions and 10% measurement noise confirm stable operation, with overshoot limited to 3.57%–4.7% and minimal impact on settling time. These findings highlight the effectiveness of combining Chebyshev wavelets, adaptive control, and indirect architectures for enhancing power system stability.</p><h2 dir="ltr">Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" rel="noreferrer noopener" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2025.3612288" target="_blank">https://dx.doi.org/10.1109/access.2025.3612288</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_3a61c6a5a0b11a494a81e9b9e9a5409b |
| identifier_str_mv | 10.1109/access.2025.3612288 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/31289218 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Soft Computing-Based Damping Controllers With Online Parameters Tuning for Stability Enhancement of Power SystemsFarman Ullah Jan (22278835)Rabiah Badar (5090327)Ahmad Sami Al-Shamayleh (17122985)Akie Uehara (22278838)Tomonobu Senjyu (12757166)Adnan Akhunzada (20151648)EngineeringControl engineering, mechatronics and roboticsElectrical engineeringEngineering practice and educationInformation and computing sciencesArtificial intelligenceMachine learningChebyshev wavelet neural networksNonlinear systemSoft computingSTATCOMPower system stability<p dir="ltr">Power system stability continues to be a major challenge as modern grids grow more complex, uncertain, and increasingly reliant on renewable energy sources. This paper presents two new Neuro-Fuzzy controllers for Static Synchronous Compensators (STATCOMs): the Direct Chebyshev Wavelet-Based Neuro-Fuzzy Controller (DNF-CW), which adapts parameters online using fixed-structure rules, and the Indirect Chebyshev Wavelet-Based Neuro-Fuzzy Controller (IDNF-CW), which uses an online identifier to measure plant sensitivity. Chebyshev wavelet-based neural networks are utilized in the consequent part of both controllers to enable accurate local modeling and improved damping performance. The proposed methods are evaluated using a Single Machine Infinite Bus (SMIB) system and the IEEE 9-bus multi-machine system under a variety of fault and loading conditions. Benchmark comparisons include a conventional Indirect Adaptive Neuro-Fuzzy Takagi–Sugeno–Kang (IDNF-TSK) based controller, a configuration without STATCOM (No STATCOM), and a configuration with STATCOM but without auxiliary control (No Control). In the SMIB scenario, the IDNF-CW achieves a 40% reduction in settling time compared to the IDNF-TSK. In the more demanding multi-machine setup, the IDNF-CW restores stability within 3 seconds after a sequence of faults, outperforming DNF-CW and IDNF-TSK. Additionally, reductions of over 53% in the Integral of Time-weighted Absolute Error (ITAE) and 36% in the Integral of Absolute Error (IAE) are observed. These tests under multiple fault conditions and 10% measurement noise confirm stable operation, with overshoot limited to 3.57%–4.7% and minimal impact on settling time. These findings highlight the effectiveness of combining Chebyshev wavelets, adaptive control, and indirect architectures for enhancing power system stability.</p><h2 dir="ltr">Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" rel="noreferrer noopener" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2025.3612288" target="_blank">https://dx.doi.org/10.1109/access.2025.3612288</a></p>2025-09-19T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/access.2025.3612288https://figshare.com/articles/journal_contribution/Soft_Computing-Based_Damping_Controllers_With_Online_Parameters_Tuning_for_Stability_Enhancement_of_Power_Systems/31289218CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/312892182025-09-19T03:00:00Z |
| spellingShingle | Soft Computing-Based Damping Controllers With Online Parameters Tuning for Stability Enhancement of Power Systems Farman Ullah Jan (22278835) Engineering Control engineering, mechatronics and robotics Electrical engineering Engineering practice and education Information and computing sciences Artificial intelligence Machine learning Chebyshev wavelet neural networks Nonlinear system Soft computing STATCOM Power system stability |
| status_str | publishedVersion |
| title | Soft Computing-Based Damping Controllers With Online Parameters Tuning for Stability Enhancement of Power Systems |
| title_full | Soft Computing-Based Damping Controllers With Online Parameters Tuning for Stability Enhancement of Power Systems |
| title_fullStr | Soft Computing-Based Damping Controllers With Online Parameters Tuning for Stability Enhancement of Power Systems |
| title_full_unstemmed | Soft Computing-Based Damping Controllers With Online Parameters Tuning for Stability Enhancement of Power Systems |
| title_short | Soft Computing-Based Damping Controllers With Online Parameters Tuning for Stability Enhancement of Power Systems |
| title_sort | Soft Computing-Based Damping Controllers With Online Parameters Tuning for Stability Enhancement of Power Systems |
| topic | Engineering Control engineering, mechatronics and robotics Electrical engineering Engineering practice and education Information and computing sciences Artificial intelligence Machine learning Chebyshev wavelet neural networks Nonlinear system Soft computing STATCOM Power system stability |