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Comparison method of MPSO, FPA, and GWO algorithm in MPPT SEPIC converter under dynamic partial shading condition

机译:MPPT SEPIC转换器MPSO,FPA和GWO算法在动态部分着色条件下的比较方法

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Solar energy can be utilized through photovoltaic (PV) into electrical energy. The output power of PV can reach maximum point when the irradiation of the sun is exposed to the surface without any object being obstructed. Unfortunately, In the actual implementation of irradiation received by PV it is often blocked by a moving object or a shadow of a non-moving object. This problem resulted in the emergence of more than one maximum power point on the P-V characteristic curve. The condition can't be solved by using the usual MPPT Algorithm so that MPPT Partial shading algorithm is required. Partial shading algorithm has been developed from conventional methods to artificial intelligence methods. Hence, in this paper proposed comparison method of MPSO, FPA, and GWO in MPPT SEPIC converter under dynamic partial shading condition. The level of accuracy of all three methods is validated through simulation. The simulation results show that the three methods are able to overcome the problem of partial shading very well with a high level of accuracy. The simulation results also show that the FPA method has advantages compared to other proposed methods. The simulation results also show that the MPSO method has more advantages compared to other proposed methods in tracking accuracy and the FPA method is faster to reach convergence situation by other proposed method.
机译:通过光伏(PV)可以利用太阳能进入电能。当阳光照射到表面时,PV的输出功率可以达到最大点,而没有任何物体被阻塞。遗憾的是,在通过PV接收的照射的实际实施中,它通常被移动物体或非移动物体的阴影阻挡。这个问题导致P-V特征曲线上的一个以上的最大功率点的出现。通过使用通常的MPPT算法不能解决这种情况,以便需要MPPT部分着色算法。从传统方法开发了部分着色算法到人工智能方法。因此,本文在动态部分着色条件下提出了MPPS SEPIC转换器中MPSO,FPA和GWO的比较方法。通过仿真验证了所有三种方法的准确性水平。仿真结果表明,这三种方法能够非常好地克服部分阴影的问题,具有高精度。仿真结果还表明,与其他提出的方法相比,FPA方法具有优势。模拟结果还表明,与跟踪精度的其他提出的方法相比,MPSO方法具有更多优点,并且通过其他提出的方法更快地达到收敛情况的FPA方法更快。

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