Intelligent PV Power Smoothing Control Using Probabilistic Fuzzy Neural Network with Asymmetric Membership Function
An intelligent PV power smoothing control using probabilistic fuzzy neural network with asymmetric membership function (PFNN-AMF) is proposed in this study. First, a photovoltaic (PV) power plant with a battery energy storage system (BESS) is introduced. The BESS consisted of a bidirectional DC/AC 3...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2017-01-01
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| Series: | International Journal of Photoenergy |
| Online Access: | http://dx.doi.org/10.1155/2017/8387909 |
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| author | Faa-Jeng Lin Su-Ying Lu Jo-Yu Chao Jin-Kuan Chang |
| author_facet | Faa-Jeng Lin Su-Ying Lu Jo-Yu Chao Jin-Kuan Chang |
| author_sort | Faa-Jeng Lin |
| collection | DOAJ |
| description | An intelligent PV power smoothing control using probabilistic fuzzy neural network with asymmetric membership function (PFNN-AMF) is proposed in this study. First, a photovoltaic (PV) power plant with a battery energy storage system (BESS) is introduced. The BESS consisted of a bidirectional DC/AC 3-phase inverter and LiFePO4 batteries. Then, the difference of the actual PV power and smoothed power is supplied by the BESS. Moreover, the network structure of the PFNN-AMF and its online learning algorithms are described in detail. Furthermore, the three-phase output currents of the PV power plant are converted to the dq-axis current components. The resulted q-axis current is the input of the PFNN-AMF power smoothing control, and the output is a smoothing PV power curve to achieve the effect of PV power smoothing. Comparing to the other smoothing methods, a minimum energy capacity of the BESS with a small fluctuation of the grid power can be achieved by the PV power smoothing control using PFNN-AMF. In addition, a personal computer- (PC-) based PV power plant emulator and BESS are built for the experimentation. From the experimental results of various irradiance variation conditions, the effectiveness of the proposed intelligent PV power smoothing control can be verified. |
| format | Article |
| id | doaj-art-42c8ba98e24c4d0fa909bae8b6947323 |
| institution | Kabale University |
| issn | 1110-662X 1687-529X |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Photoenergy |
| spelling | doaj-art-42c8ba98e24c4d0fa909bae8b69473232025-08-20T03:36:02ZengWileyInternational Journal of Photoenergy1110-662X1687-529X2017-01-01201710.1155/2017/83879098387909Intelligent PV Power Smoothing Control Using Probabilistic Fuzzy Neural Network with Asymmetric Membership FunctionFaa-Jeng Lin0Su-Ying Lu1Jo-Yu Chao2Jin-Kuan Chang3Department of Electrical Engineering, National Central University, Chungli 320, TaiwanDepartment of Electrical Engineering, National Central University, Chungli 320, TaiwanDepartment of Electrical Engineering, National Central University, Chungli 320, TaiwanDepartment of Electrical Engineering, National Central University, Chungli 320, TaiwanAn intelligent PV power smoothing control using probabilistic fuzzy neural network with asymmetric membership function (PFNN-AMF) is proposed in this study. First, a photovoltaic (PV) power plant with a battery energy storage system (BESS) is introduced. The BESS consisted of a bidirectional DC/AC 3-phase inverter and LiFePO4 batteries. Then, the difference of the actual PV power and smoothed power is supplied by the BESS. Moreover, the network structure of the PFNN-AMF and its online learning algorithms are described in detail. Furthermore, the three-phase output currents of the PV power plant are converted to the dq-axis current components. The resulted q-axis current is the input of the PFNN-AMF power smoothing control, and the output is a smoothing PV power curve to achieve the effect of PV power smoothing. Comparing to the other smoothing methods, a minimum energy capacity of the BESS with a small fluctuation of the grid power can be achieved by the PV power smoothing control using PFNN-AMF. In addition, a personal computer- (PC-) based PV power plant emulator and BESS are built for the experimentation. From the experimental results of various irradiance variation conditions, the effectiveness of the proposed intelligent PV power smoothing control can be verified.http://dx.doi.org/10.1155/2017/8387909 |
| spellingShingle | Faa-Jeng Lin Su-Ying Lu Jo-Yu Chao Jin-Kuan Chang Intelligent PV Power Smoothing Control Using Probabilistic Fuzzy Neural Network with Asymmetric Membership Function International Journal of Photoenergy |
| title | Intelligent PV Power Smoothing Control Using Probabilistic Fuzzy Neural Network with Asymmetric Membership Function |
| title_full | Intelligent PV Power Smoothing Control Using Probabilistic Fuzzy Neural Network with Asymmetric Membership Function |
| title_fullStr | Intelligent PV Power Smoothing Control Using Probabilistic Fuzzy Neural Network with Asymmetric Membership Function |
| title_full_unstemmed | Intelligent PV Power Smoothing Control Using Probabilistic Fuzzy Neural Network with Asymmetric Membership Function |
| title_short | Intelligent PV Power Smoothing Control Using Probabilistic Fuzzy Neural Network with Asymmetric Membership Function |
| title_sort | intelligent pv power smoothing control using probabilistic fuzzy neural network with asymmetric membership function |
| url | http://dx.doi.org/10.1155/2017/8387909 |
| work_keys_str_mv | AT faajenglin intelligentpvpowersmoothingcontrolusingprobabilisticfuzzyneuralnetworkwithasymmetricmembershipfunction AT suyinglu intelligentpvpowersmoothingcontrolusingprobabilisticfuzzyneuralnetworkwithasymmetricmembershipfunction AT joyuchao intelligentpvpowersmoothingcontrolusingprobabilisticfuzzyneuralnetworkwithasymmetricmembershipfunction AT jinkuanchang intelligentpvpowersmoothingcontrolusingprobabilisticfuzzyneuralnetworkwithasymmetricmembershipfunction |