Outage State Matrix Decomposition for Power Network Node Identification
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Solution Overview
Problem
In power supply networks, outages at the lowest hierarchical level are difficult to identify due to limited insight into the network topology, leading to delayed restoration and repair, as existing monitoring tools are not available in energy distribution systems and rely on manual inference from customer complaints.
Innovation Solution
A method and apparatus that automatically identify the origin of an outage in a hierarchical network by decomposing an outage state matrix into probability matrices to determine the inner node causing the outage, using event data streams from smart meters to create a binary state matrix and applying matrix factorization algorithms to pinpoint the source.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual inference from customer complaints is used to identify outages, then system operators can track outages without additional monitoring equipment, but the identification process becomes delayed and complex due to limited insight into network topology
Solution Approach 1:
The patent applies preliminary action by pre-establishing the network topology model and probability matrices before outages occur. The system maintains ready-to-use hierarchical network structures, probability calculations for inner nodes causing outages, and superordinate node relationships, enabling immediate automated identification when outages happen without manual inference delays
Solution Approach 2:
The patent introduces an intermediary automated identification system that acts as a mediator between raw outage data and operational decision-making. This system uses probability matrices and network topology models to translate customer complaints and sensor data into actionable outage origin identification, eliminating the need for operators to manually infer causes from limited information
2Loss of information
If comprehensive monitoring tools are deployed in the energy distribution system, then insight into network operation state is improved, but the device complexity and cost increase
Solution Approach 1:
The patent applies universality by designing a monitoring system that serves multiple functions: it tracks network state, identifies outage origins, maintains topology models, and provides restoration guidance all through a single integrated platform. The system processes various data types (customer complaints, sensor readings, event data) through unified probability matrices and network models, reducing overall system complexity while improving information coverage
Solution Approach 2:
The system applies self-service by using existing network infrastructure and data sources to automatically generate comprehensive monitoring capabilities. It leverages available event data from smart meters and existing network topology information to self-construct probability matrices and identify outages without requiring extensive additional hardware or manual configuration, thereby reducing device complexity while improving information availability
Data Source
AI summary
A method for identifying automatically an inner node within a hierarchical network causing an outage of a group of leaf nodes at the lowest hierarchical level, the method including providing an outage state matrix representing an outage state of leaf nodes at the lowest hierarchical level; decomposing the state matrix into a first probability matrix indicating for each inner node the probability that the inner node forms the origin of an outage at the lowest hierarchical level of the hierarchical network and into a second probability matrix indicating for each leaf node at the lowest hierarchical level of the hierarchical network the probability that an inner node forms a hierarchical superordinate node of the respective leaf node at the lowest hierarchical level of the hierarchical network and evaluating the first probability matrix to identify the inner node having caused the outage of the group of leaf nodes.