Liu Shuai, Yang Yuqi, Qin Hui, Liu Guanjun, Qu Yuhua, Deng Shan, Gao Yuan, Li Jiangqiao, Guo Jun
School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China.
Hubei Key Laboratory of Digital Valley Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China.
Sensors (Basel). 2023 Oct 8;23(19):8324. doi: 10.3390/s23198324.
An accurate and reliable estimation of photovoltaic models holds immense significance within the realm of energy systems. In pursuit of this objective, a Boosting Flower Pollination Algorithm (BFPA) was introduced to facilitate the robust identification of photovoltaic model parameters and enhance the conversion efficiency of solar energy into electrical energy. The incorporation of a Gaussian distribution within the BFPA serves the dual purpose of conserving computational resources and ensuring solution stability. A population clustering strategy is implemented to steer individuals in the direction of favorable population evolution. Moreover, adaptive boundary handling strategies are deployed to mitigate the adverse effects of multiple individuals clustering near problem boundaries. To demonstrate the reliability and effectiveness of the BFPA, it is initially employed to extract unknown parameters from well-established single-diode, double-diode, and photovoltaic module models. In rigorous benchmarking against eight control methods, statistical tests affirm the substantial superiority of the BFPA over these controls. Furthermore, the BFPA successfully extracts model parameters from three distinct commercial photovoltaic cells operating under varying temperatures and light irradiances. A meticulous statistical analysis of the data underscores a high degree of consistency between simulated data generated by the BFPA and observed data. These successful outcomes underscore the potential of the BFPA as a promising approach in the field of photovoltaic modeling, offering substantial enhancements in both accuracy and reliability.
对光伏模型进行准确可靠的估计在能源系统领域具有极其重要的意义。为实现这一目标，引入了一种增强型花授粉算法（BFPA），以促进对光伏模型参数的稳健识别，并提高太阳能向电能的转换效率。在BFPA中纳入高斯分布具有节约计算资源和确保解稳定性的双重目的。实施种群聚类策略以引导个体朝着有利的种群进化方向发展。此外，部署自适应边界处理策略以减轻多个个体在问题边界附近聚类的不利影响。为证明BFPA的可靠性和有效性，最初将其用于从成熟的单二极管、双二极管和光伏模块模型中提取未知参数。在与八种控制方法进行的严格基准测试中，统计测试证实了BFPA相对于这些控制方法的显著优越性。此外，BFPA成功地从在不同温度和光照强度下运行的三种不同商业光伏电池中提取了模型参数。对数据进行的细致统计分析强调了BFPA生成的模拟数据与观测数据之间的高度一致性。这些成功结果突出了BFPA作为光伏建模领域一种有前景的方法的潜力，在准确性和可靠性方面都有显著提高。
