## 5 Conclusions  
Given that most of the current identification of branch overload causes is carried out for conventional power grids, and with the trend of gradually increasing the proportion of renewable energy sources, there is an urgent need for a method of identifying the causes of branch overloads that considers renewable energy sources. In response to the development trend of new power systems, this paper proposes a method for quickly identifying the causes of branch overloads applicable to power systems with high wind power penetration, which can avoid the unexpected tripping of the backup protection due to LE, providing technical support for the improvement of the performance of the backup protection. Firstly, the improved shortest path algorithm was adopted to search for the CLS. Based on the adjacency matrix, the method ensures the unidirectionality of the search process, which avoids the repeated traversal of the nodes, quickly determining the collected range of the wide-area information and significantly reducing the burden of further analysis and computation, thus increasing the search speed. At the same time, the method lays a foundation for the design of the subsequent load-shedding strategy, which significantly reduces the range of the analysis, preparing for the rapid elimination of the overloaded branch. Then, the hidden dangers of the traditional calculation method of the APEV in the scenario with wind farms are analyzed, and a fast calculation method of the APEV considering the fluctuation of wind power output is proposed. This method can ensure the accuracy of the active power calculation results in scenarios with different wind power penetration rates. In the scenario set in this paper, when the wind power penetration rate exceeds 60%, the APEV obtained based on the conventional method can no longer satisfy the LE identification criterion, the cause of the branch overload appears to be misjudged, the backup protection will be tripped according to the whole delay, and the active power of the whole network will be redistributed again, which may trigger chain tripping. The APEV calculation method proposed in this paper, which takes into account the fluctuation of wind farm output, ensures that the LE identification criterion is still applicable to the grid when the penetration rate of wind power exceeds 60%. Finally, simulations of the IEEE 39-bus New England system were carried out based on PSASP. The simulation results show that the search for the CLS can be completed quickly and accurately based on the improved shortest path algorithm, quickly determining the range of the information that needs to be collected. The LE identification method proposed in this paper can reliably identify the LE in both the single-node and multi-node scenarios containing wind farms, and it remains applicable in scenarios with high wind power penetration. It takes less than 0.1 s to complete the identification of LE, which can reliably block the tripping signal within the delay period of backup protection, preventing the backup protection from unexpected action, winning time for the subsequent load-shedding action of the automatic safety device and effectively avoiding the occurrence of cascading outages. This paper mainly studies the identification of LE suitable for high penetration scenarios of wind generators to avoid blackouts caused by cascading trips of transmission lines. We are currently considering the impact of communication faults and network attacks on cascading trips and trying to realize the early warning function of communication faults