Research on MPPT Based on Gray Wolf Algorithm improved by Levy Flight

Abstract

In the late iteration of the gray wolf algorithm, the convergence results will have low accuracy becased of the elite retention.When implementing MPPT control, the maximum power point of the photovoltaic array cannot be accurately tracked, and it is easy to fall into the local optimum. Therefore, this paper proposes gray wolf algorithm improved with Levy flight applied to MPPT control. The algorithm introduces the Levy flight to search the head wolf position globally, then uses the group optimization of the gray wolf algorithm and the random walk of Levy flight to improve the tracking speed and accuracy of the MPPT controller. In the simulation experiment, the algorithm is modeled and verified by setting different lighting conditions. Finally, it is compared with the conductance increment method, modified hybrid method of grey wolf optimization and golden-section optimization, the original gray wolf algorithm. The results show that the algorithm meets the requirements of fast tracking speed, high accuracy and stability in MPPT control.

A path planning method for unmanned aerial vehicle based on improved wolf pack algorithm

“…In the traditional WPA, the movement of the scout wolves during the wandering behavior primarily relies on the wandering step size step d a . [26][27][28] Based on the formula, it can be observed that the values of M d and m d are fixed in the given d dimensional space. Once the step size factor C is determined, the wandering step size remains constant throughout the iterations of the wolf pack.…”

“…In the traditional WPA, the movement of the scout wolves during the wandering behavior primarily relies on the wandering step size $$$ {\mathrm{step}}_a^d $$$ 26–28 . Based on the formula, it can be observed that the values of $$$ {M}_d $$$ and $$$ {m}_d $$$ are fixed in the given $$$ d $$$ dimensional space.…”

Parameter Identification of Jiles-Atherton Model Based on Levy Whale Optimization Algorithm

“…In conformity with the coercivity H c , the maximum value of the magnetization M T IP , the maximum value of the external magnetic field is H T IP . In line with (18), the bound of parameter α is traversed with a certain step size, and then the bound of parameter a is calculated based on the values of k 1 and k max according to (17) and (26). Figure 3 illustrates the detailed procedure.…”