In searching for a maximum power point (MPP) using a DC boost converter for photovoltaic (PV) energy conversion systems, we realised that the fast and accurate way to find the suitable duty ratio  value is the core problem to enhance the energy conversion efficiency of the PV system. Under uniform irradiation, the panels will generate the same values, so they have only one peak on the P-V curve; conventional MPP tracking methods easily obtain this MPP. However, under partial shading conditions, many peaks are created, traditional MPP tracking methods can fall into the local MPP, and this issue will cause energy loss and reduce PV energy conversion efficiency. To avoid this disadvantage, this paper proposes a hybrid method (HM) by combining the improved chicken swarm optimisation (CSO) method and the incremental conductance (InC) algorithm for a DC standalone PV energy conversion system. In this hybrid method, the improved CSO modified approach is used to search the global region, and the InC algorithm is responsible for capturing the top of this global region. MATLAB simulation and experimental results were performed to demonstrate that the proposed method has achieved the global MPP under uniform solar irradiance and partial shadow effects.

This paper proposes a novel maximum power point tracking (MPPT) method inspired by the horse racing game for standalone photovoltaic (PV) power systems, such that the highest PV power conversion efficiency is obtained. From the horse racing game rules, we develop the horse racing algorithm (HRA) with the qualifying stage and final ranking stage. The MPP can be searched even if there exist multiple local MPPs for the PV power system. Moreover, from the proposed horse racing algorithm, the calculation is reduced, so that the transient searching points are less than traditional methods, i.e., the transient oscillation is less during the MPPT control. Therefore, the HRA based MPPT method avoids local maximum power traps and achieves the MPP quickly even if considering partial shading influence and varying environment for PV panels. Evidence of the accuracy and effectiveness of the proposed HRA method is exhibited by simulation results. These results are also compared with typical particle swarm optimization (PSO) and grey wolf optimization (GWO) methods and shown better convergence time as well as transient oscillation. Within the range from 0.34 to 0.58 s, the proposed method has effectively tracked the global maximum power point, which is from 0.42 to 0.48 s faster than the conventional PSO technique and from 0.36 to 0.74 s faster than the GWO method. Finally, the obtained findings proved the effectiveness and superiority of the proposed HRA technique through experimental results. The fast response in terms of good transient oscillation and global power tracking time of the proposed method are from 0.40 to 1.0 s, while the PSO and GWO methods are from 1.56 to 1.6 s and from 1.9 to 2.2 s, respectively.

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This paper presents a new simple approach based on a short distance running race in athletics to determine the maximum power peak (MPP) of a photovoltaic (PV) power system to achieve a high speed and searching accuracy. The operating principle of this approach is based on the division of the active area of contestants at the starting line. Each position of the contestant corresponds to a duty ratio value to control the DC boost converter. Based on the proposed athletics running algorithm, the optimal duty ratio value is determined for the load to receive the best electrical power from the PV system. Based on new concepts of regional division and updated positions, the global MPP can be determined precisely with the rapid convergence of the positions to the global maximum power region. Consequently, the proposed method demonstrates excellent tracking ability with high accuracy and zero oscillation amplitude. Simulation results show that the proposed algorithm achieves high efficiency, particularly under partial shading effects.