Energies, Vol. 19, Pages 1323: Situational Deduction and Active Defense for Distribution Networks Under Complex Conditions: A Service-Oriented Digital Twin Approach

Energies, Vol. 19, Pages 1323: Situational Deduction and Active Defense for Distribution Networks Under Complex Conditions: A Service-Oriented Digital Twin Approach

Energies doi: 10.3390/en19051323

Authors:
Yuanyi Xia
Xianbo Du
Xing Chen
Rui Zhang
Ying Zhu

In modern distribution networks (DNs), extreme weather events and cascading faults pose severe challenges to operational safety. However, existing defense mechanisms struggle with a core question: How to maintain high-fidelity situational awareness and make precise active decisions when physical parameters drift and historical fault data is scarce? To address this, this paper proposes a situational deduction and active defense framework based on a service-oriented digital twin. First, regarding the modeling fidelity gap, a data–physics fusion mechanism is constructed. By integrating Kirchhoff’s laws with data-driven error correction, it dynamically calibrates time-varying parameters to resolve mapping distortion. Second, regarding the data scarcity bottleneck, a predictive perception method is introduced. Utilizing the digital twin as a generative engine, it augments rare fault samples to enable super-real-time deduction of future trends. Third, regarding the decision-making passivity, a service-driven simulation model is established. It transforms abstract indicators (safety, economy, resilience) into executable constraints, shifting the paradigm from ‘passive response’ to ‘active defense.’ Case studies on a modified IEEE 123-node system demonstrate that the proposed method significantly enhances resilience and decision accuracy under complex conditions.