# An Anomaly Detection Ensemble for Protection Systems in Distribution Networks
## 5 Conclusions  
To address the high-dimensional heterogeneous characteristics of the operational status input data of distribution network protection systems, this paper presents a data-driven multi-model anomaly detection ensemble to effectively excavate their potential operational disturbances and abnormal statuses, thereby achieving real-time abnormal monitoring and early warnings. The main works of this paper are summarized as follows:  
1. A KPCI dimension reduction model is constructed to map and dissect the original data and compress its dimension. This can effectively deal with high-dimensional heterogeneous input data environments and diminish the computational burden to decrease the response time.
2. The IF tree-structure anomaly detection method is established, which directly identifies abnormal samples according to the corresponding normal range of operational status in each feature. It can cope with different scenarios, devices, and parameter types through an almost identical process, and, thus, can realize comprehensive and effective responses to underlying diverse anomalies.  
In general, the innovation of this algorithm is that it fully utilizes the concept of being data-driven, and, through the KPCI dimension reduction model and the IF anomaly detection model, it effectively processes large-scale high-dimensional heterogeneous operational status data. In this process, the algorithm successfully excavates potential operation disturbances and state anomalies in the relay protection system of the distribution network. Due to this multi-model design, the algorithm runs rapidly and has strong capabilities to deal with large-scale data. This not only effectively meets the needs of real-time monitoring and early warnings but also provides a new solution to deal with the complex relay protection system of the distribution network. Through a large amount of empirical analysis, we found that this algorithm can effectively deal with practical problems in the operation of the power system. The experimental results also verify that this algorithm can not only effectively detect anomalies but also has a fairly high strike accuracy. Therefore, we have reason to believe that this method will play a bigger role in the future regarding power system operational safety and in the maintenance of future smart grids. However, although this algorithm has solved the high-dimensional heterogeneous data processing problem of data-driven models to some extent, there are still some deficiencies. For instance, there may be room for optimization in the pre-processing part of data preparation, and the data expression after dimension reduction may lose some information. Therefore, our future work also includes further enhancing the stability of the algorithm and its accuracy, to dig deeper into potential rules in order to make a bigger contribution to the health, safety, and stability of power system operation.  
Conceptualization, C.Y. and C.S.; methodology, C.Y. and B.Y.; software, C.Y.; validation, C.Y., B.Y. and J.S.; resources, C.S.; writing--original draft preparation, C.Y.; writing--review and editing, C.S.; visualization, R.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Natural Science Foundation of China (52207074), the Natural Science Foundation of Hunan (2024)[9175], the Natural Science Foundation of Changsha (kq2208231), and the Innovation and Entrepreneurship Training Program of Changsha University of Science and Technology College Students (202310536058).  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflict of interest.