A Method to Improve the Risk Perception Ability of Distribution Network in Ice Disaster Prone Areas based on Multi-Dimensional Measurement Configuration

Over the past few years, there has been an increase in the occurrence of extreme weather events, which have become a significant threat to the sustainability of the global economy and have greatly impacted everyday human activities. The impact of ice disasters, in particular, is quite substantial. When power lines experience heavy icing, it can lead to a decline in their mechanical and electrical properties, potentially resulting in power outages, wire oscillations, electrical discharges across insulators, structural collapses of transmission towers, and communication interruptions. Ensuring a reliable power supply for essential services during such harsh conditions necessitates the early detection of risks associated with the components of the distribution network when they are subjected to ice. However, the current electrical and gas measurement devices in the distribution network pose a challenge, as their failure to function properly can delay the acquisition of component failure information, thereby reducing the efficiency of ice management and emergency response planning by the control center. To tackle this issue, this paper proposes a strategy to boost the risk detection capabilities of distribution networks in regions susceptible to ice disasters by employing a multi-dimensional measurement system. The strategy starts with an assessment of the measurement data needs for distribution networks under icy conditions, followed by the selection of suitable measuring equipment to meet these needs. The paper then examines the causes of failures in distribution networks during ice disasters. Based on these findings, a risk assessment method for network components under icy conditions is introduced. Additionally, the paper explores the economic and observational implications of measurement configurations and how different setups can enhance the precision of risk detection. It then suggests a method to improve the network's risk detection capabilities during ice disasters. Lastly, the paper presents a method for identifying critical distribution network components using graph theory and an optimization approach for configuring measurement setups in icy conditions, focusing on these critical components. This holistic strategy is designed to enhance the capacity to detect and counteract distribution network failures when electrical and gas measurement devices malfunction.