Renewable energy is becoming more popular due to environmental concerns about the previous energy source. Accurate solar photovoltaic system model parameters substantially impact the efficiency of solar energy conversion to electricity. In this matter, swarm and evolutionary optimization algorithms have been widely utilized in dealing with practical problems due to their more straightforward concepts, efficacy, flexibility, and easy-to-implement procedural frameworks. However, the nonlinearity and complexity of the photovoltaic parameter identification caused swarm and evolutionary optimizers to exhibit Immaturity in the obtained solutions. To deal with such concerns on immature convergence and imbalanced searching trends, in this paper, we proposed the PSOCS algorithm based on the core components of particle swarm optimization (PSO) and the strategy of random reselection of parasitic nests that appeared in the cuckoo search. The parameters of the single-diode model and the double-diode model are identified based on several experiments. Based on the comprehensive comparisons, results indicate that the developed PSOCS algorithm has higher convergence accuracy and better stability than the original PSO, the original cuckoo search, and other studied algorithms. The findings indicate that we suggest the PSOCS algorithm as an enhanced and efficient approach for dealing with parameter extraction of solar photovoltaic modules. We think this new variant of PSO can be employed as a tool for the optimal designing of photovoltaic systems.

由于对先前能源的环境担忧，可再生能源正变得越来越流行。准确的太阳能光伏系统模型参数会极大地影响太阳能转换为电能的效率。在这方面，群算法和进化优化算法由于其更直接的概念、有效性、灵活性和易于实现的程序框架而被广泛用于处理实际问题。然而，光伏参数识别的非线性和复杂性导致群体和进化优化器在获得的解决方案中表现出不成熟。为了解决对不成熟收敛和不平衡搜索趋势的担忧，在本文中，我们基于粒子群优化（PSO）的核心组件和杜鹃搜索中出现的寄生巢的随机重选策略提出了PSOCS算法。基于多次实验确定了单二极管模型和双二极管模型的参数。综合比较，结果表明，所开发的 PSOCS 算法比原始 PSO、原始布谷鸟搜索和其他研究算法具有更高的收敛精度和更好的稳定性。研究结果表明，我们建议将 PSOCS 算法作为处理太阳能光伏模块参数提取的一种增强且有效的方法。我们认为这种 PSO 的新变体可以用作优化光伏系统设计的工具。