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首页> 外文期刊>Energy Conversion & Management >Particle swarm optimisation with adaptive mutation strategy for photovoltaic solar cell/module parameter extraction
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Particle swarm optimisation with adaptive mutation strategy for photovoltaic solar cell/module parameter extraction

机译：具有自适应变异策略的粒子群算法用于光伏太阳能电池/组件参数提取

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摘要

Developing an accurate model for photovoltaic solar cell and module represents a challenge to improve the overall efficiency of the photovoltaic systems use. Parameter estimation of photovoltaic solar cell and module circuit model is a crucial task that is commonly transformed into an optimisation issue solved by metaheuristic algorithms. Among these algorithms, the particle swarm optimisation has gained great interest due to its structure simplicity and rapid response. However, its major disadvantage lies in the premature convergence. In an endeavour to deal with this problem, an improved mutated particle swarm optimisation algorithm with adaptive mutation strategy is proposed in this paper. The adaptive mutation is introduced to alleviate the premature convergence problem and ensure a suitable trade-off between the explorative and exploitative capabilities over the search process. The proposed algorithm is used to identify the optimal parameters of different photovoltaic models; single diode, double diode, and photovoltaic module. The performance of the used method is firstly evaluated on measured data reported in the literature. Then, the algorithm is tested based on measured data from the laboratory work and from the data sheet of different modules. Experimental results prove that the used algorithm achieves higher accuracy and provides the lowest root mean square error compared to other previously reported parameter extraction algorithms.
机译：为光伏太阳能电池和模块开发准确的模型对提高光伏系统使用的整体效率提出了挑战。光伏太阳能电池的参数估计和模块电路模型是一项至关重要的任务，通常会转换为通过元启发式算法解决的优化问题。在这些算法中，粒子群优化由于其结构简单和快速响应而引起了极大的兴趣。但是，其主要缺点在于过早收敛。为了解决这个问题，本文提出了一种具有自适应变异策略的改进变异粒子群算法。引入自适应变异可缓解过早收敛的问题，并确保在搜索过程中探索能力和开发能力之间取得适当的折衷。该算法用于识别不同光伏模型的最优参数。单二极管，双二极管和光伏模块。首先根据文献报道的测量数据评估所用方法的性能。然后，根据实验室工作和不同模块的数据表中的测量数据对算法进行测试。实验结果证明，与其他先前报告的参数提取算法相比，所使用的算法具有更高的准确性，并提供了最低的均方根误差。

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