| Title |
A hybrid adaptive-prediction maximum power point tracking method for the smart city with massive photovoltaic |
| ID_Doc |
42179 |
| Authors |
Deng, WY; Chen, CY; Zhang, YJ; Cheng, RT; Dai, ST; Zhou, XY |
| Title |
A hybrid adaptive-prediction maximum power point tracking method for the smart city with massive photovoltaic |
| Year |
2022 |
| Published |
|
| DOI |
10.1016/j.egyr.2022.10.288 |
| Abstract |
The partial shadow condition seriously affects the efficiency of the photovoltaic system in the modern city with dense built and other occlusions. From this, the characteristic curve of the photovoltage system shows multi-peak, which further increases the difficulty of getting photovoltaic systems to operate at maximum efficiency. As an efficient technique, the intelligent optimized maximum power point tracking method relies on initialization information and is difficult to balance the tracking performance. Therefore, a hybrid adaptive-prediction maximum power tracking method is proposed in this paper. Firstly, the neighborhood range of the maximum power points is located by the fuzzy predicted mechanism at the upper layer. Secondly, on the bottom layer, based on improving the Cuckoo search algorithm, the proposed method uses an interpolation function fitting curve to guide the particles to converge accurately on the bottom layer. At the same time, the output voltage of the system under an open loop is directly controlled by the duty cycle of the control signal, which improves the universality of the method. Finally, the simulation results show that the proposed method is superior to other advanced methods in tracking speed and with smaller power oscillations and comparable tracking accuracy, for which the proposed method is suitable for the city with complex environments and dense buildings. (c) 2022 The Authors. Published by Elsevier Ltd. This is an open access article under theCCBYlicense (http://creativecommons.org/licenses/by/4.0/). |
| Author Keywords |
Partial shadow conditions; Intelligent optimization algorithm; Adaptive prediction; Improved cuckoo algorithm |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:001011928900025 |
| WoS Category |
Energy & Fuels |
| Research Area |
Energy & Fuels |
| PDF |
https://doi.org/10.1016/j.egyr.2022.10.288
|