Tuning of controller parameters for suppressing low frequency oscillations in electric railway traction networks using meta‐heuristic algorithms
Abstract Due to the interaction of electric multiple units (EMUs), and the electric traction networks, low frequency oscillations (LFOs) appear leading to traction blockade and overall stability related issues. For suppressing LFOs, coronavirus herd immunity optimiser (CHIO), a recently developed me...
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| Format: | Article |
| Language: | English |
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Wiley
2023-06-01
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| Series: | IET Electrical Systems in Transportation |
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| Online Access: | https://doi.org/10.1049/els2.12075 |
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| author | Prasenjit Dey Phumin Kirawanich Chaiyut Sumpavakup Aniruddha Bhattacharya |
| author_facet | Prasenjit Dey Phumin Kirawanich Chaiyut Sumpavakup Aniruddha Bhattacharya |
| author_sort | Prasenjit Dey |
| collection | DOAJ |
| description | Abstract Due to the interaction of electric multiple units (EMUs), and the electric traction networks, low frequency oscillations (LFOs) appear leading to traction blockade and overall stability related issues. For suppressing LFOs, coronavirus herd immunity optimiser (CHIO), a recently developed meta‐heuristic, has been applied for tuning controller parameters. Controller parameters are tuned to minimise the integral time absolute error (ITAE) that regulates DC‐link capacitor voltage. Results obtained using CHIO are compared with those found using other well‐established algorithms like symbiotic organisms search (SOS) and particle swarm optimisation (PSO). The supremacy of CHIO over other mentioned algorithms for mitigating LFOs was demonstrated for a diverse range of operating conditions. Results demonstrates that overshoot for the proposed algorithm‐based traction unit is 1.0061% whereas those for SOS and PSO based algorithm are obtained as 6.4542 % and 20.6166%, respectively which are quite high. CHIO is more stable than SOS and PSO and requires settling time of 0.1934 s only to reach steady‐state condition, which is 50.21% faster than SOS and 65.03% faster than PSO. Also, the total harmonic distortion (THD) for line currents of the secondary side of traction transformer (TT) are obtained as 0.88%, 2.17%, and 12.48% for CHIO, SOS, and PSO, respectively. |
| format | Article |
| id | doaj-art-1b21fd8b4daa48308413f4201d96ae66 |
| institution | Kabale University |
| issn | 2042-9738 2042-9746 |
| language | English |
| publishDate | 2023-06-01 |
| publisher | Wiley |
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| series | IET Electrical Systems in Transportation |
| spelling | doaj-art-1b21fd8b4daa48308413f4201d96ae662025-02-03T06:45:37ZengWileyIET Electrical Systems in Transportation2042-97382042-97462023-06-01132n/an/a10.1049/els2.12075Tuning of controller parameters for suppressing low frequency oscillations in electric railway traction networks using meta‐heuristic algorithmsPrasenjit Dey0Phumin Kirawanich1Chaiyut Sumpavakup2Aniruddha Bhattacharya3The Cluster of Logistics and Rail Engineering Mahidol University Salaya ThailandThe Cluster of Logistics and Rail Engineering Mahidol University Salaya ThailandResearch Centre for Combustion Technology and Alternative Energy – CTAE and College of Industrial Technology, King Mongkut's University of Technology North Bangkok Bangkok ThailandDepartment of Electrical Engineering National Institute of Technology Durgapur Durgapur West Bengal IndiaAbstract Due to the interaction of electric multiple units (EMUs), and the electric traction networks, low frequency oscillations (LFOs) appear leading to traction blockade and overall stability related issues. For suppressing LFOs, coronavirus herd immunity optimiser (CHIO), a recently developed meta‐heuristic, has been applied for tuning controller parameters. Controller parameters are tuned to minimise the integral time absolute error (ITAE) that regulates DC‐link capacitor voltage. Results obtained using CHIO are compared with those found using other well‐established algorithms like symbiotic organisms search (SOS) and particle swarm optimisation (PSO). The supremacy of CHIO over other mentioned algorithms for mitigating LFOs was demonstrated for a diverse range of operating conditions. Results demonstrates that overshoot for the proposed algorithm‐based traction unit is 1.0061% whereas those for SOS and PSO based algorithm are obtained as 6.4542 % and 20.6166%, respectively which are quite high. CHIO is more stable than SOS and PSO and requires settling time of 0.1934 s only to reach steady‐state condition, which is 50.21% faster than SOS and 65.03% faster than PSO. Also, the total harmonic distortion (THD) for line currents of the secondary side of traction transformer (TT) are obtained as 0.88%, 2.17%, and 12.48% for CHIO, SOS, and PSO, respectively.https://doi.org/10.1049/els2.12075AC‐DC power convertorspulse width modulationrailway engineeringrailways |
| spellingShingle | Prasenjit Dey Phumin Kirawanich Chaiyut Sumpavakup Aniruddha Bhattacharya Tuning of controller parameters for suppressing low frequency oscillations in electric railway traction networks using meta‐heuristic algorithms IET Electrical Systems in Transportation AC‐DC power convertors pulse width modulation railway engineering railways |
| title | Tuning of controller parameters for suppressing low frequency oscillations in electric railway traction networks using meta‐heuristic algorithms |
| title_full | Tuning of controller parameters for suppressing low frequency oscillations in electric railway traction networks using meta‐heuristic algorithms |
| title_fullStr | Tuning of controller parameters for suppressing low frequency oscillations in electric railway traction networks using meta‐heuristic algorithms |
| title_full_unstemmed | Tuning of controller parameters for suppressing low frequency oscillations in electric railway traction networks using meta‐heuristic algorithms |
| title_short | Tuning of controller parameters for suppressing low frequency oscillations in electric railway traction networks using meta‐heuristic algorithms |
| title_sort | tuning of controller parameters for suppressing low frequency oscillations in electric railway traction networks using meta heuristic algorithms |
| topic | AC‐DC power convertors pulse width modulation railway engineering railways |
| url | https://doi.org/10.1049/els2.12075 |
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