An improved hybrid global maximum power tracking control method based on particle swarm optimization and disturbance observation

By combining particle swarm optimization and perturbation observation methods, a hybrid global maximum power point tracking (MPPT) control method was developed, which solved the problem of multiple peak values ​​under partial shading of photovoltaic panels, achieved fast and accurate maximum power point tracking, and improved the output efficiency of the photovoltaic array.

CN115599161BActive Publication Date: 2026-03-24NORTHWESTERN POLYTECHNICAL UNIV
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Patent Information

Application Number
CN202211179350.X
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2022-09-27
Publication Date
2026-03-24
Estimated Expiration
2042-09-27

AI Technical Summary

Technical Problem

When photovoltaic panels are partially shaded, the traditional MPPT algorithm has difficulty effectively tracking the global maximum power point, resulting in multiple peaks in the power output curve of the photovoltaic array. Existing intelligent algorithms are complex and computationally intensive, while heuristic algorithms lack accuracy and efficiency.

Method used

By combining particle swarm optimization and perturbation observation methods, and by improving the initialization method and setting transition conditions, the number of particles is reduced, the accuracy of the algorithm is improved, and the algorithm is restarted to find the maximum power point when the environment changes.

Benefits of technology

It accelerates the algorithm's convergence speed, reduces the probability of getting stuck in local optima, improves the accuracy and efficiency of maximum power point tracking, and adapts to changes in the external environment.

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Abstract

The application discloses an improved hybrid global maximum power tracking control method based on a particle swarm optimization and a disturbance observation, and relates to the photovoltaic power generation field.The application comprises the following steps: finding a peak value where a maximum power point is located based on a particle swarm algorithm; and continuously tracking the maximum power point by using a disturbance observation method.The application directly controls a duty cycle of a boost circuit; the application improves an initialization method of the particle swarm algorithm, uses fewer particles, and reduces the probability of the algorithm falling into a local optimal point; the application proposes a transition condition from the particle swarm algorithm to the disturbance observation method, can reduce the iteration number of the particle swarm algorithm, and can faster reach the maximum power point; and the application sets multiple restart conditions for environmental changes, so that the algorithm can find the maximum power point again in the case of environmental changes.
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