一种新型的光伏模型参数辨识DEITLBO算法

A Novel Photovoltaic Model Parameter Identification DEITLBO Algorithm

摘要: 针对TLBO教学优化算法在光伏模型参数辨识中存在求解精度低，稳定性差等问题，提出了一种基于差分进化的改进教学优化DEITLBO算法。引入混沌精英学习法和绩效导向两种机制，根据个体差异性在更新阶段对种群进行分类，增强了TLBO算法探索与开发的能力，解决了TLBO算法容易陷入局部最优解问题；引入DE的更新策略，保证了物种的多样性，避免了TLBO算法种群个体机械性的进化模式，使算法在迭代过程中收敛速度更快，辨识结果更加稳定，提高了算法求解的精度。将DEITLBO算法应用到双二极管模型实验表明：DEITLBO算法在双二极管模型中，RMSE值、标准偏差、收敛速度及准确性均优于其他算法；利用不同辐照度和不同温度情况下的S75多晶硅光伏组件模型实测数据进行实验，实验的结果验证了DEITLBO算法的适用性。

Abstract: To address the issues of low accuracy and instability in the parameter identification of photovoltaic model parameters using the TLBO teaching optimization algorithm, a refined teaching optimization algorithm called DEITLBO, based on differential evolution (DE), was introduced. Two mechanisms, the chaotic elite Xi method and performance-oriented, were introduced to classify the population according to individual differences in the update stage, which enhanced the ability of TLBO algorithm exploration and development, and tackled the problem of TLBO algorithm’s tendency to become stuck in local optimal solutions. The introduction of DE’s update strategy aims to maintain species diversity and prevent the TLBO algorithm population from entering a mechanical evolution mode, make the algorithm converge faster in the iterative process, the identification results exhibit greater stability, and the algorithm’s solution accuracy is enhanced. Experiments on the application of the DEITLBO algorithm to the dual-diode model show that the RMSE value, the DEITLBO algorithm’s standard deviation, convergence rate, and precision are better than those of other algorithms in the dual-diode model. Experiments are carried out using the measured data of S75 polycrystalline silicon photovoltaic module model under different irradiance and different temperatures. The experimental results verify the applicability of the DEITLBO algorithm.