Haisen Wang

The P-V and I-V curves of photovoltaic (PV) strings show multiple peaks when exposed to partial shading conditions (PSCs). The traditional maximum power point tracking (MPPT) method cannot track the global maximum power point (GMPP) due to the multi-peak characteristics, power fluctuation, and tracking speed. In this paper, a multi-peak PV MPPT method based on the honey badger algorithm (HBA) is proposed to track the GMPP in a localized shading environment. The performance of this method is also compared and analyzed with the traditional MPPT methods based on the perturbation observation (P&O) method and Particle Swarm Optimization (PSO) algorithm. The experimental results have proven that, compared with the MPPT methods based on P&O and PSO, the proposed multi-peak MPPT method based on the HBA algorithm has a faster tracking speed, higher tracking accuracy, and fewer iterations.

This paper proposes a cyclic shift (CS) reconfiguration scheme and a two-stage MPPT (TS-MPPT) method to enhance the energy supply of solar-powered unmanned aerial vehicle (SPUAV) photovoltaic (PV) systems under partial shading conditions (PSCs). The proposed CS reconfiguration scheme disperses shadows by moving PV modules longitudinally and deduces the optimal movement distance. This scheme not only improves power delivery during PSCs, but also simplifies the maximum power point tracking (MPPT) method by aligning the global maximum power point (GMPP) with the right power point (RPP). Furthermore, the proposed TS-MPPT method takes the derivative of the P-V curve to position the maximum power point (MPP) on the x-axis, and the MPP is tracked by solving the intersection points of the linear equation. The proposed method significantly reduces the tracking time and eliminates the drift phenomenon. The proposed reconfiguration scheme and control method are simulated and experimentally verified under various PSCs. Results indicate that, compared to the total-cross-tied (TCT) scheme, the CS reconfiguration scheme enhances PV energy output by 25.65%. Compared to the P&O method, the TS-MPPT method reduced the start-up time and tracking time by 60.6% and 39.1%, respectively.

To address the issue of efficiency degradation and output power oscillation of photovoltaic (PV) arrays under partial shading conditions (PSCs), this article proposes a global maximum power point tracking (GMPPT) method based on an online data-driven hyperbolic function model. The proposed method utilizes real-time sampled data to establish a curve-fitting model for PV output estimation. By employing adaptive weighting factor, the online fitting model is dynamically combined with the conventional perturbation and observation (P&O) method. Compared to existing GMPPT algorithms, the proposed method achieves fast tracking speed and effectively narrows the search range, reducing the voltage trajectory during the search process. Therefore, the proposed GMPPT method is capable of adapting to complex PSCs with frequent environmental changes, thereby enhancing the overall performance of PV systems. The effectiveness of the proposed GMPPT method is validated through comparative experiments with existing approaches.