State Grid Nanjing Power Supply Company, Nanjing, China.
PLoS One. 2024 May 16;19(5):e0303084. doi: 10.1371/journal.pone.0303084. eCollection 2024.
The advent of smart grid technologies has brought about a paradigm shift in the management and operation of distribution networks, allowing for intricate system information to be encapsulated within semantic network models. These models, while robust, are not immune to faults within their knowledge entities, which can arise from a myriad of issues, potentially leading to verification failures and operational disruptions. Addressing this critical vulnerability, our research delves into the development of a novel fault detection methodology specifically tailored for the knowledge entity variables of semantic networks in distribution networks. In our approach, we first construct a state space equation that models the behavior of knowledge entity variables in the presence of faults. This foundational framework enables us to apply an unknown input observer strategy to effectively detect anomalies within the system. To bolster the fault identification process, we introduce the innovative use of a siamese network, a neural network architecture which is proficient in differentiating between similar datasets. Through simulation scenarios, we demonstrate the efficacy of our proposed fault detection method.
智能电网技术的出现带来了配电网络管理和运营的范式转变，使得复杂的系统信息可以封装在语义网络模型中。这些模型虽然强大，但也不能幸免于其知识实体中的故障，这些故障可能由各种问题引起，潜在地导致验证失败和操作中断。为了解决这个关键的漏洞，我们的研究致力于开发一种新的故障检测方法，专门针对配电网络语义网络的知识实体变量。在我们的方法中，我们首先构建一个状态空间方程，该方程可以在存在故障的情况下对知识实体变量的行为进行建模。这个基础框架使我们能够应用未知输入观测器策略来有效地检测系统中的异常。为了增强故障识别过程，我们引入了一种孪生网络的创新使用，这是一种擅长区分相似数据集的神经网络架构。通过仿真场景，我们展示了我们提出的故障检测方法的有效性。
