Adaptive Neuro-Fuzzy Voltage Control for LCL-Filter Grid-Connected Converter
Inductance – Capacitance – Inductance (LCL) filter is a very attractive candidate for renewable energy system applications due to its high efficiency. High attenuation of the switching frequency harmonics, small size, low fee, and improving the overall harmonic distortion (THD). This paper presents...
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| Format: | Article |
| Language: | English |
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Unviversity of Technology- Iraq
2023-02-01
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| Series: | Engineering and Technology Journal |
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| Online Access: | https://etj.uotechnology.edu.iq/article_175732_24ca62f843ba613208a34401fc6fd356.pdf |
| _version_ | 1797337720177033216 |
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| author | Safa Olwie Abdulrahim Humod Fadhil Hasan |
| author_facet | Safa Olwie Abdulrahim Humod Fadhil Hasan |
| author_sort | Safa Olwie |
| collection | DOAJ |
| description | Inductance – Capacitance – Inductance (LCL) filter is a very attractive candidate for renewable energy system applications due to its high efficiency. High attenuation of the switching frequency harmonics, small size, low fee, and improving the overall harmonic distortion (THD). This paper presents how voltage is affected by increased loads or voltage sag. Therefore it is necessary to control it with certain controllers. The Adaptive Neuro-Fuzzy Inference System (ANFIS) is used as an intelligent controller, the voltage constraint as training data for ANFIS obtained from PI. The filter works in a good connection between the inverter and the grid and rewords unwanted harmonics from using the inverter. The mathematical models for the LCL filter are investigated. The proposed approach shows more effective results than previous performance for voltage controlling and harmonic reduction. It gives overshoot (0.5%), steady state error (0.005), settling time (0.03 sec), rise time (0.005 sec), and improving THD 8.67% to 2.33% by comparing these results of ANFIS respectively with the results of PI which gave(3%),(0.01),(0.2sec)and( 0.02sec). |
| first_indexed | 2024-03-08T09:17:41Z |
| format | Article |
| id | doaj.art-fefa08a77bb04824b3fd41fd63ad88e7 |
| institution | Directory Open Access Journal |
| issn | 1681-6900 2412-0758 |
| language | English |
| last_indexed | 2024-03-08T09:17:41Z |
| publishDate | 2023-02-01 |
| publisher | Unviversity of Technology- Iraq |
| record_format | Article |
| series | Engineering and Technology Journal |
| spelling | doaj.art-fefa08a77bb04824b3fd41fd63ad88e72024-01-31T14:15:37ZengUnviversity of Technology- IraqEngineering and Technology Journal1681-69002412-07582023-02-0141231633210.30684/etj.2022.132342.1115175732Adaptive Neuro-Fuzzy Voltage Control for LCL-Filter Grid-Connected ConverterSafa Olwie0Abdulrahim Humod1Fadhil Hasan2Department of Electrical Engineering, University of Technology -Baghdad- Iraqb Department of Electrical Engineering, University of Technology, Baghdad - IraqDepartment of Electrical Engineering, University of Technology ,Baghdad - IraqInductance – Capacitance – Inductance (LCL) filter is a very attractive candidate for renewable energy system applications due to its high efficiency. High attenuation of the switching frequency harmonics, small size, low fee, and improving the overall harmonic distortion (THD). This paper presents how voltage is affected by increased loads or voltage sag. Therefore it is necessary to control it with certain controllers. The Adaptive Neuro-Fuzzy Inference System (ANFIS) is used as an intelligent controller, the voltage constraint as training data for ANFIS obtained from PI. The filter works in a good connection between the inverter and the grid and rewords unwanted harmonics from using the inverter. The mathematical models for the LCL filter are investigated. The proposed approach shows more effective results than previous performance for voltage controlling and harmonic reduction. It gives overshoot (0.5%), steady state error (0.005), settling time (0.03 sec), rise time (0.005 sec), and improving THD 8.67% to 2.33% by comparing these results of ANFIS respectively with the results of PI which gave(3%),(0.01),(0.2sec)and( 0.02sec).https://etj.uotechnology.edu.iq/article_175732_24ca62f843ba613208a34401fc6fd356.pdfdistributed power generationlcl filtermathematical models of lcl filterpi controlanfis |
| spellingShingle | Safa Olwie Abdulrahim Humod Fadhil Hasan Adaptive Neuro-Fuzzy Voltage Control for LCL-Filter Grid-Connected Converter Engineering and Technology Journal distributed power generation lcl filter mathematical models of lcl filter pi control anfis |
| title | Adaptive Neuro-Fuzzy Voltage Control for LCL-Filter Grid-Connected Converter |
| title_full | Adaptive Neuro-Fuzzy Voltage Control for LCL-Filter Grid-Connected Converter |
| title_fullStr | Adaptive Neuro-Fuzzy Voltage Control for LCL-Filter Grid-Connected Converter |
| title_full_unstemmed | Adaptive Neuro-Fuzzy Voltage Control for LCL-Filter Grid-Connected Converter |
| title_short | Adaptive Neuro-Fuzzy Voltage Control for LCL-Filter Grid-Connected Converter |
| title_sort | adaptive neuro fuzzy voltage control for lcl filter grid connected converter |
| topic | distributed power generation lcl filter mathematical models of lcl filter pi control anfis |
| url | https://etj.uotechnology.edu.iq/article_175732_24ca62f843ba613208a34401fc6fd356.pdf |
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