## 3 Reconstruction Method and Protection Algorithm
### Characteristics of Current Trajectory Images
#### 3.2.4 Protection Principle and Training Effects  
The protection algorithm based on two-dimensional spatial current trajectory images and deep learning operates as follows: after a fault occurs, the PV side and system side currents are collected over a defined period. These currents are then combined to reconstruct a detailed characteristic current trajectory, processed into a cross-overlapping data set, and sent to the A-CNN model for training. Real-time monitoring of the current at both ends of the three-phase line enables the system to continuously reconstruct and update the current trajectories, which are fed into the trained neural network for fault identification. Once a fault waveform aligns with the characteristic trajectory, the system can accurately differentiate between internal and external faults, identify faulty or normal phases, and issue either an action or blocking signal to the relay. This advanced algorithm significantly improves upon traditional methods by enhancing reliability, reducing response time, and increasing fault sensitivity, particularly in complex systems like PV stations. Furthermore, it is highly robust against common issues such as CT saturation, data anomalies, outliers, and varying fault locations, ensuring a more consistent and efficient protection response under a wide range of fault conditions. The entire process of real-time monitoring, fault analysis, and protection signaling is illustrated in Figure 12. This demonstrates how the algorithm's innovative use of deep learning not only increases the accuracy and speed of fault detection but also provides an adaptive framework that can handle the evolving challenges of new power systems. The method is particularly suited to grids with high renewable energy penetration, where conventional protection schemes often struggle to perform effectively under dynamic and rapidly changing conditions. Among them, a few points which should be clarified are as follows:  
Figure 12: Protection algorithm based on two-dimensional spatial current trajectory image and A-CNN. (1) Source of the data set. The data set cannot implement an enumeration of all cases. But, A-CNN has good generalization ability. Similar fault conditions can be accurately identified, which meets the reliability requirements of relay protection. (2) Realization of fault location. In the new protection algorithm, A-CNN adopts a two-class network. As shown in Figure 12, label 0 indicates that this phase is a fault phase during an internal fault, and label 1 indicates that this phase is a normal phase or during an external fault. Three-phase fault waveforms are sent to A-CNN for detection, satisfying selectivity. (3) Meaning of dynamic coverage. A continuous waveform is composed of a point set with a certain sampling frequency. At each moment, the recorded wave of the previous cycle is sent to A-CNN for fault identification. Considering the generalization ability of A-CNN, a complete 20 ms fault waveform after the fault moment can be accurately identified, which will greatly improve rapidity. (4) Situation of backup protection. The new protection algorithm is a double-ended electrical quantity measurement protection, which cannot be used as a backup protection for the transmission line. But the algorithm can be used as a backup of traditional protection and phase selection elements to further improve robustness. Further, the structure definition of A-CNN is shown in Table 1 and Figure 13. Meanwhile, Table 1 compares the structure of a traditional artificial neural network (ANN) with that of an A-CNN. The A-CNN model described in Table 1 is used for training, and results are shown in Table 2.  
The results of Table 2 show that A-CNN outperforms other classical neural network models. The reasons for the poor performance of the other models are as follows: ANN, as a traditional neural network, lacks a convolutional layer. LeNet's use of the sigmoid activation function leads to the vanishing gradient problem.