The integration of Distributed Energy Resources (DERs) into distribution systems greatly increases the system complexity and introduces two-way power flow. Conventional protection schemes are based upon local measurements and simple linear system models, and are thus not capable of handling the new complexity and power flow patterns in systems with high DER penetration. In this paper, we propose a data-driven protection framework to address the challenges introduced by DERs. Firstly, considering the limited available data under fault conditions, we adopt the Support Vector Data Description (SVDD) method, a commonly used one-class classifier, for distribution system fault detection, which only requires the normal data for its training process. Secondly, incremental learning is incorporated into the proposed SVDD-based protection framework to accommodate variations of the integration level of DERs in distribution systems over time. In particular, the artificial uniform-hyperspherical data generation model is incorporated into the incremental SVDD to boost the training speed. Finally, we validate the proposed method under the IEEE 123-node test feeder. Simulation results demonstrate that our proposed SVDD-based fault detection framework significantly improves the robustness and resilience against DERs in comparison with conventional protection systems. Meanwhile, the proposed online updating model outperforms the existing incremental SVDD models in terms of successful training speed.
Bibliographical noteFunding Information:
This work was supported in part by the Key-Area Research and Development Program of Guangdong Province Project under Grant 2018B030338001, in part by the Natural Science Foundation of China under Grant NSFC-61629101, in part by the Shenzhen Fundamental Research Fund under Grant JCYJ20170411102217994, in part by the National Science Foundation under Grant DMS-1923142, in part by the Open Research Fund from Shenzhen Research Institute of Big Data under Grant 2019ORF01006, in part by the National Key Research and Development Program of China under Grant 2018YFB1800800, and in part by the Guangdong Zhujiang Project under Grant 2017ZT07X152.
- Fault detection
- distributed energy resources (DERs)
- distribution systems
- online updating
- support vector data description (SVDD)