Research on MPPT control strategy based on CCAOA algorithm

Abstract

Abstract Photovoltaic (PV) arrays under partial shading conditions (PSC) can lead to multiple peaks in the power-voltage curve of PV system output. The traditional maximum power point tracking (MPPT) algorithm is difficult to solve the multi-peak problem and generally has slow convergence speed and easy fall into local optimality. To address this problem, a collaborative and cosine arithmetic optimization algorithm (CCAOA) was proposed in this paper. The cosine factor was introduced into the mathematical optimization acceleration function in traditional AOA to enhance the global search capability of the algorithm. And the circle chaotic mapping and cross-variance strategy were introduced to increase the diversity and randomness of the algorithm population. Meanwhile, a cooperative search strategy of addition and subtraction is used to strengthen the local search capability of the algorithm, thus accelerate the convergence speed of the algorithm. The effectiveness of the CCAOA is evaluated by using six typical IEEE standard test functions, and the simulation results show that compared with AOA, TSO and PSO algorithms it outperforms other algorithms in terms of convergence speed and accuracy. Appling the CCAOA into the MPPT control, the performance of MPPT control strategy based on CCAOA was verified by simulation. The simulation results illustrate that the CCAOA has better performance in tracking speed, stability and efficiency when comparing with AOA, TSO and PSO algorithms. In conclusion, the MPPT control based on CCAOA can significantly improve the power generation efficiency of PV arrays under PSC.