China Three Gorges University
National Natural Science Foundation of China (61603212)
基于支持向量机(Support Vector Machine, SVM)用于光伏阵列故障诊断时准确率不高，且易受核函数与惩罚因子参数的影响，提出一种基于海鸥算法(Seagull optimization algorithm, SOA)优化支持向量机的光伏阵列故障诊断方法。引入海鸥优化算法对SVM模型进行参数寻优，建立基于最优参数的SOA-SVM故障诊断模型；利用MATLAB软件搭建光伏阵列仿真模型，提取不同故障类型下的特征参数并输入到SOA-SVM模型进行故障诊断。实验结果表明：经SOA优化后的SVM模型故障诊断准确率显著提高；且相比ABC-SVM、PSO-SVM模型,SOA-SVM模型具有更快的寻优收敛迭代速度、更高的故障诊断准确率。
Based on the low accuracy of Support Vector Machine (SVM) in photovoltaic array fault diagnosis, and the accuracy was easily affected by kernel function and penalty factor parameters, a photovoltaic array fault diagnosis method based on support vector machine optimized by Seagull optimization algorithm (SOA) was proposed. The seagull optimization algorithm was introduced to optimize the parameters of the SVM model, and the SOA-SVM fault diagnosis model based on the optimal parameters was established; MATLAB software was used to build a photovoltaic array simulation model, and the characteristic parameters under different fault types were extracted and input into the SOA-SVM model for fault diagnosis. The experimental results show that the fault diagnosis accuracy of the SVM model optimized by SOA is significantly improved; and compared with the ABC-SVM and PSO-SVM models, the SOA-SVM model converges faster in the optimization process, and has a higher fault diagnosis accuracy.