A Novel Topology Recovery Method for Low Voltage Distribution Networks
Low voltage distribution networks (LVDNs) suffer from limited visibility due to sparse or nonexistent measurement systems, leaving distribution network service providers with incomplete data. Maintenance activities, such as transformer upgrades and power line replacements, sometimes go undocumented, leading to unmonitored topology changes. This lack of oversight hinders network optimization, fault detection, and outage management, as utilities cannot fully monitor or control the system. With the rise of electric vehicles, having an accurate understanding of LVDN topology is crucial to avoid infrastructure damage from potential overloads. This paper introduces a method to reconstruct LVDN topology using incremental voltage and current changes from smart meters at customer endpoints. The approach identifies and maps network topologies with high accuracy, overcoming limitations of prior methods by discarding unrealistic assumptions. Specifically, it addresses grids with fewer than three pole connections and employs an AC power flow model over simplified DC approximations. Simulations across diverse configurations validate the method's effectiveness in accurately reconstructing LVDN topologies, enhancing real-world applicability.
Code (0)
등록된 구현이 없습니다.
Tasks
Fault DetectionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Topology Estimation in Bulk Power Grids: Guarantees on Exact Recovery
The topology of a power grid affects its dynamic operation and settlement in the electricity market. Real-time topology identification can enable faster control action following an emergency scenario like failure of a li…
Online learning for robust voltage control under uncertain grid topology
Voltage control generally requires accurate information about the grid's topology in order to guarantee network stability. However, accurate topology identification is challenging for existing methods, especially as the …
Phase Retrieval via Model-Free Power Flow Jacobian Recovery
Phase retrieval is a prevalent problem in digital signal processing and experimental physics that consists of estimating a complex signal from magnitude measurements. This paper expands the classical phase retrieval fram…
RetrievalTopology-Aware Graph Reinforcement Learning for Energy Storage Systems Optimal Dispatch in Distribution Networks
Optimal dispatch of energy storage systems (ESSs) in distribution networks involves jointly improving operating economy and voltage security under time-varying conditions and possible topology changes. To support fast on…
Reinforcement LearningDecision MakingA Generalized Switched-Capacitor Modular Multilevel Inverter Topology for Multiphase Electrical Machines with Capacitor-Voltage Self-Balancing Capability
Recent research on multilevel inverters shows exciting properties, including the potential to generate multiple output voltages and integrated voltage boosting. However, most presented inverter topologies have a restrict…