基于渐进式认知发现的新型配电网故障定位方法

Fault Location Method Based on Stepwise Cognition-discovery in New Distribution Network

在“双碳”目标下,持续接入分布式电源的新型配电网对运行可靠性提出了更高的要求,如何在渗透率变化的场景下提高现有故障定位方法的适应能力成为亟需解决的问题。为此,采用元学习特有的学会学习机制,提出了一种基于渐进式认知发现的新型配电网故障定位方法。首先,基于现有场景数据采用网络结构搜索算法构建当前场景个性化定位模型;然后,利用元学习算法提取模型构建过程中的知识因子,组成故障定位认知发现库;进而,在数据流和知识流的共同作用下,故障定位模型渐进地实现场景持续变化下的自主进化;最后,在PSCAD仿真平台对所提方法进行了验证。结果表明:所提方法具有定位精度高、鲁棒性强的优点,且在不同渗透率的故障场景下有着良好的泛化能力。研究结果可为基于人工智能的定位方法在实际系统中的应用提供技术支持。

Under the goals of carbon emission peak and carbon neutrality,the new distribution network with increasing penetrations of distributed generators has raised the requirements on operational reliability;moreover,how to improve the adaptability of fault location method under scenarios which penetration of renewable energy are rapid changing is an ur-gent issue to be solved.Therefore,benefiting from the learn-to-learn mechanism of meta learning,this paper proposes a novel stepwise cognitive-discovery based fault location method for new distribution networks.Firstly,a specialized loca-tion model is built by neural architecture search algorithm based on data from existing scenario.Then,the knowledge factors are acquired by meta learning during model formation process,and a cognition-discovery library of fault section location is built.Furthermore,with the help of coordinative effect of data stream and knowledge flow,self-evolving of the model under diverse scenarios is achieved.Finally,the proposed method is verified in PSCAD.Results show that the proposed method has the advantages of high positioning accuracy and strong robustness,and has strong generalization ability under diverse penetration scenarios.The research results can provide technical supports for the application of fault location method based on artificial intelligence in practical systems.