## 1 Introduction
With the construction of new-type power systems, the research on backup protection for power systems with high penetration of renewable energy sources is an important topic in the future. The power system under a high proportion of renewable energy sources will cause huge active power fluctuations when an N \(-\) 1 fault occurs [15]. Wind power output's randomness and volatility in renewable energy sources are the largest, and the higher the penetration level of wind generation, the greater the power fluctuation of the power grid [16; 17; 18]. The authors of reference [19] summarize the challenges brought by integrating renewable energy into the smart power grid and point out that few studies on backup protection can identify the LE for the power system with wind farms. The authors of reference [20] note that the high penetration of renewable energy sources leads to various stability problems in the power system, which are interconnected, and the effects of the local faults can spread to the entire grid, posing a great threat to the security and stability of the power system. In reference [21], comprehensive indices are proposed to analyze the effect of renewable energy integration on power systems based on graph theory. The authors note that power systems are more prone to cascade tripping when uncertainty levels exceed a certain range. However, these methods do not have the performance to identify the cause of branch overloading. In reference [22], the influence of wind power output on backup protection is considered for the first time, and a voltage and angle criterion based on wide-area information is proposed to identify short-circuit fault and LE, but it depends on the performance of two designed discriminant subroutines. Apart from this, no other article has been seen that considers the impact of renewable energy in identifying LE and faults. It can be concluded that there are very few articles that consider the impact of new energy sources on the method of distinguishing LE from faults. Therefore, there is a need to upgrade the existing methods for identifying the causes of branch circuit overloads while considering the integration of large wind farms into power systems. The development of a unique method for identifying the cause of branch overloads is the main motivation of this paper, and the goal of this work is to clearly distinguish between LE and fault in power systems with a high penetration of wind power, providing technical support to improve the reliability of backup protection for transmission grids with high wind power penetration. In view of the above problems, this paper proposes a method for searching the critical line set (CLS) based on the improved shortest path algorithm; then, standing on the conventional LE identification criterion based on the active power change value (APCV), it proposes a fast LE identification method based on a WAMS for high wind power penetration scenarios, laying a technical foundation for avoiding the unexpected action of backup protection and subsequent load-shedding actions. The main contributions of this paper are as follows:  
1. In order to determine the range of wide-area information that needs to be collected, a method of searching the CLS based on an improved shortest-path algorithm is proposed. This method can quickly search for dangerous lines greatly affected by LE after the faulty line is removed, laying the foundation for the subsequent load-shedding, significantly reducing the range of the analysis, and preparing for the rapid elimination of branch overloads. 2. The hidden dangers of the conventional calculation method of the APEV in the scenario with wind farms are analyzed. Then, a calculation method for the APEV considering the fluctuation of wind power output is proposed to estimate the active power of the remaining branches after the faulty branch is removed.