Sliding Mode Real-Time Control of Photovoltaic Systems Using Neural Estimators
The maximum power point tracking (MPPT) problem has attracted the attention of many researchers, because it is convenient to obtain the maximum power of a photovoltaic module regardless of the weather conditions and the load. In this paper, a novel control for a boost DC/DC converter has been introd...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2016-01-01
|
| Series: | International Journal of Photoenergy |
| Online Access: | http://dx.doi.org/10.1155/2016/5214061 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832567094258958336 |
|---|---|
| author | J. A. Ramos-Hernanz O. Barambones J. M. Lopez-Guede I. Zamora P. Eguia M. Farhat |
| author_facet | J. A. Ramos-Hernanz O. Barambones J. M. Lopez-Guede I. Zamora P. Eguia M. Farhat |
| author_sort | J. A. Ramos-Hernanz |
| collection | DOAJ |
| description | The maximum power point tracking (MPPT) problem has attracted the attention of many researchers, because it is convenient to obtain the maximum power of a photovoltaic module regardless of the weather conditions and the load. In this paper, a novel control for a boost DC/DC converter has been introduced. It is based on a sliding mode controller (SMC) that takes a current signal as reference instead of a voltage, which is generated by a neuronal reference current generator. That reference current indicates the current (IMPP) at the maximum power point (MPP) for given weather conditions. In order to test the designed control system, a photovoltaic module model based on a second artificial neuronal network (ANN) has been obtained from experimental data gathered during 18 months in the Faculty of Engineering Vitoria-Gasteiz (Spain). We have analyzed the performance of such model and we found that it is very accurate (MSE = 0.062 A and R = 0.991 with test dataset). We also have tested the performance of the overall SMC design with both simulated and real tests, concluding that it guarantees that the power in the output of the converter is very close to the power of the photovoltaic module output. |
| format | Article |
| id | doaj-art-4ce78a81d8f64ecc9ea7e7d34eb2c015 |
| institution | Kabale University |
| issn | 1110-662X 1687-529X |
| language | English |
| publishDate | 2016-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Photoenergy |
| spelling | doaj-art-4ce78a81d8f64ecc9ea7e7d34eb2c0152025-02-03T01:02:22ZengWileyInternational Journal of Photoenergy1110-662X1687-529X2016-01-01201610.1155/2016/52140615214061Sliding Mode Real-Time Control of Photovoltaic Systems Using Neural EstimatorsJ. A. Ramos-Hernanz0O. Barambones1J. M. Lopez-Guede2I. Zamora3P. Eguia4M. Farhat5Electrical Engineering Department, Faculty of Engineering Vitoria-Gasteiz, University of the Basque Country, Nieves Cano 12, 01006 Vitoria-Gasteiz, SpainSystems Engineering and Automatic Control Department, Faculty of Engineering Vitoria-Gasteiz, University of the Basque Country, Nieves Cano 12, 01006 Vitoria-Gasteiz, SpainSystems Engineering and Automatic Control Department, Faculty of Engineering Vitoria-Gasteiz, University of the Basque Country, Nieves Cano 12, 01006 Vitoria-Gasteiz, SpainElectrical Engineering Department, Faculty of Engineering, University of the Basque Country, Alameda Urquijo, s/n, 48013 Bilbao, SpainElectrical Engineering Department, Faculty of Engineering, University of the Basque Country, Alameda Urquijo, s/n, 48013 Bilbao, SpainDepartment of Electrical, Electronics and Communications Engineering, American University of Ras Al Khaimah, Sheikh Saqr Bin Khalid Rd., Ras Al Khaimah, UAEThe maximum power point tracking (MPPT) problem has attracted the attention of many researchers, because it is convenient to obtain the maximum power of a photovoltaic module regardless of the weather conditions and the load. In this paper, a novel control for a boost DC/DC converter has been introduced. It is based on a sliding mode controller (SMC) that takes a current signal as reference instead of a voltage, which is generated by a neuronal reference current generator. That reference current indicates the current (IMPP) at the maximum power point (MPP) for given weather conditions. In order to test the designed control system, a photovoltaic module model based on a second artificial neuronal network (ANN) has been obtained from experimental data gathered during 18 months in the Faculty of Engineering Vitoria-Gasteiz (Spain). We have analyzed the performance of such model and we found that it is very accurate (MSE = 0.062 A and R = 0.991 with test dataset). We also have tested the performance of the overall SMC design with both simulated and real tests, concluding that it guarantees that the power in the output of the converter is very close to the power of the photovoltaic module output.http://dx.doi.org/10.1155/2016/5214061 |
| spellingShingle | J. A. Ramos-Hernanz O. Barambones J. M. Lopez-Guede I. Zamora P. Eguia M. Farhat Sliding Mode Real-Time Control of Photovoltaic Systems Using Neural Estimators International Journal of Photoenergy |
| title | Sliding Mode Real-Time Control of Photovoltaic Systems Using Neural Estimators |
| title_full | Sliding Mode Real-Time Control of Photovoltaic Systems Using Neural Estimators |
| title_fullStr | Sliding Mode Real-Time Control of Photovoltaic Systems Using Neural Estimators |
| title_full_unstemmed | Sliding Mode Real-Time Control of Photovoltaic Systems Using Neural Estimators |
| title_short | Sliding Mode Real-Time Control of Photovoltaic Systems Using Neural Estimators |
| title_sort | sliding mode real time control of photovoltaic systems using neural estimators |
| url | http://dx.doi.org/10.1155/2016/5214061 |
| work_keys_str_mv | AT jaramoshernanz slidingmoderealtimecontrolofphotovoltaicsystemsusingneuralestimators AT obarambones slidingmoderealtimecontrolofphotovoltaicsystemsusingneuralestimators AT jmlopezguede slidingmoderealtimecontrolofphotovoltaicsystemsusingneuralestimators AT izamora slidingmoderealtimecontrolofphotovoltaicsystemsusingneuralestimators AT peguia slidingmoderealtimecontrolofphotovoltaicsystemsusingneuralestimators AT mfarhat slidingmoderealtimecontrolofphotovoltaicsystemsusingneuralestimators |