In order to accurately identify the key lines in the photovoltaic (PV) grid-connected system, an identification method based on the improved PageRank algorithm is proposed. Firstly, the correlation matrix reflecting the electrical characteristics of the system is constructed using the line current-carrying rate, line breaking power flow transfer rate and line coupling rate, to replace the original network topology matrix. Secondly, through the entropy method, a comprehensive evaluation index based on electrical betweenness, load deviation rate and voltage shock rate is constructed to improve the distribution of the initial PageRank (PR) value of the PV grid-connected system. To study the changes’ impact of PVs active power outputs on the identification results of key lines in the Multi-PV power system, the HGWO-SVM (Hybrid Grey Wolves Optimized Support Vector Machine) algorithm was used to obtain the PVs daily outputs prediction curves and obtain fixed outputs of PVs at different periods, so as to study the impact of the variation of PV daily output on the key line identification. Taking the IEEE 39-node system containing multi-PV as an example, the identification results show that the improved PageRank algorithm is superior to the original method in line identification accuracy. The HGWO-SVM algorithm by adaptively modifying the cross operator and mutation operator also has a certain improvement in prediction accuracy. The changes of PVs daily outputs have different degree of influence on the line criticality (namely final PR value) during periods of high light intensity and other periods of light intensity.

为了准确识别光伏并网系统中的关键线路，提出了一种基于改进的PageRank算法的识别方法。首先，利用线路载流率，线路断流功率传递率和线路耦合率构造反映系统电气特性的相关矩阵，以代替原来的网络拓扑矩阵。其次，通过熵值法，建立了基于电气中间度，负荷偏差率和电压冲击率的综合评价指标，以改善光伏并网系统初始PageRank（PR）值的分布。要研究光伏有功功率输出的变化对Multi-PV电力系统中关键线路的识别结果的影响，运用HGWO-SVM（混合灰狼优化支持向量机）算法获取光伏日产量预测曲线，获得不同时期光伏的固定产量，以研究光伏日产量变化对关键指标的影响。线路识别。以包含多PV的IEEE 39节点系统为例，识别结果表明，改进的PageRank算法在线路识别精度上优于原始方法。通过自适应修改交叉算子和变异算子的HGWO-SVM算法在预测精度上也有一定的提高。在高光强度时期和其他光强度时期，PV日输出量的变化对线路临界值（即最终PR值）的影响程度不同。