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The traditional maximum power point tracking (MPPT) method is prone to falling into a local optimum under partial shading conditions and failing, while the common intelligent optimization algorithms often have disadvantages such as a low convergence accuracy, a slow convergence speed, and a low system stability. Aimed at these problems, a maximum power tracking strategy for photovoltaic (PV) system is proposed, which is based on the hybrid control of sailfish optimization (SFO) algorithm and perturbation and observation (P&O) method. The SFO algorithm uses two populations of sailfish (predator) and sardine (prey) at the same time to ensure the exploration of particles in the global space. The hybrid algorithm uses the SFO algorithm to quickly track the neighborhood of maximum power point at first, and then it uses the P&O method with a small step size to finely search for the maximum power point. In this way, it takes advantage of the piecewise step method to meet the requirements of MPPT search speed and search accuracy. Simulation results show that the hybrid control strategy effectively improves the response speed and tracking accuracy of the control system, as well as its stability.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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