Research on the MPPT of Photovoltaic Power Generation Based on the CSA-INC Algorithm

Abstract

The existing Maximum Power Point Tracking (MPPT) method has low tracking efficiency and poor stability. It is easy to fall into the Local Maximum Power Point (LMPP) in Partial Shading Condition (PSC), resulting in the degradation of output power quality and efficiency. It was found that various bio-inspired MPPT based optimization algorithms employ different mechanisms, and their performance in tracking the Global Maximum Power Point (GMPP) varies. Thus, a Cuckoo search algorithm (CSA) combined with the Incremental conductance Algorithm (INC) is proposed (CSA-INC) is put forward for the MPPT method of photovoltaic power generation. The method can improve the tracking speed by more than 52% compared with the traditional Cuckoo Search Algorithm (CSA), and the results of the study using this algorithm are compared with the popular Particle Swarm Optimization (PSO) and the Gravitational Search Algorithm (GSA). CSA-INC has an average tracking efficiency of 99.99% and an average tracking time of 0.19 s when tracking the GMPP, which improves PV power generation's efficiency and power quality.

Novel Global MPPT Technique Based on Hybrid Cuckoo Search and Artificial Bee Colony under Partial-Shading Conditions

“…In order to comprehensively and further compare the performance of the proposed algorithm, we also conducted a comparative analysis with two additional hybrid algorithms, the PSO-P&O hybrid method proposed in the literature [15] and the CSA-INC hybrid method proposed in the literature [21].…”

“…One of its key features is that it does not require specific problem information, but rather compares the advantages and disadvantages of potential solutions to achieve the global optimal value within the population. There have been limited studies on the application of the CSA and ABC to the GMPPT, with most focusing on separately improving these two algorithms or combining them with traditional MPPT control algorithms, such as the combination of the CSA with INC [21] or ABC with P&O [22]; there is a scarcity of research that directly combines the CSA and ABC, and the relevant literature shows that the hybrid approach can improve system performance [23]. To choose a suitable combination method, we need to consider the advantages and disadvantages of each method and combine the strengths of each method to achieve better results [24].…”

A PV MPPT control method based on async-PSO and INC algorithm under shading condition

“…The INC method typically employs a fixed step size for maximal power point tracking, which does not meet the requirements for tracking speed and precision [23]. The selection of step size for a PV system is always inversely proportional to the search speed and precision of the MPP [23].…”

“…The INC method typically employs a fixed step size for maximal power point tracking, which does not meet the requirements for tracking speed and precision [23]. The selection of step size for a PV system is always inversely proportional to the search speed and precision of the MPP [23]. For sake of reconciling the disparity between tracking speed and steady-state precision, the search area may be subdivided into distinct regions with corresponding step sizes [24].…”