Name
Abstract | ||
|---|---|---|
Due to the high cost of photovoltaic (PV) modules, an accurate performance estimation method is significantly valuable for studying the electrical characteristics of PV generation systems. Conventional analytical PV models are usually composed by nonlinear exponential functions and a good number of unknown parameters must be identified before using. In this paper, an adaptive-network-based fuzzy inference system (ANFIS) based modeling method is proposed to predict the current-voltage characteristics of PV modules. The effectiveness of the proposed modeling method is evaluated through comparison with Villalva's model, radial basis function neural networks (RBFNN) based model and support vector regression (SVR) based model. Simulation and experimental results confirm both the feasibility and the effectiveness of the proposed method. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.3390/sym8090096 | SYMMETRY-BASEL |
Keywords | Field | DocType |
ANFIS,modeling,characteristic estimation,photovoltaic module | Nonlinear system,Exponential function,Radial basis function neural,Control theory,Support vector machine,Performance estimation,Adaptive neuro fuzzy inference system,Photovoltaic system,Mathematics,Fuzzy inference system | Journal |
Volume | Issue | ISSN |
8 | 9 | 2073-8994 |
Citations | PageRank | References |
1 | 0.48 | 5 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ziqiang Bi | 1 | 3 | 1.99 |
| Jieming Ma | 2 | 26 | 10.15 |
| Xinyu Pan | 3 | 4 | 1.59 |
| Jian Wang | 4 | 2 | 0.88 |
| Shi Yu | 5 | 19 | 9.01 |