Power Grid Fragile Line Identification Using Electrical Betweenness
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Solution Overview
Problem
Existing methods for identifying fragile lines in power grids are inefficient and lack accuracy, particularly in large systems, as they assume power flow only along the shortest path and do not adequately consider the self-balance of internal power subsystems or the impact of generator damage.
Innovation Solution
A method that constructs power grids as network diagrams, sorts electrical betweenness from large to small, and removes lines based on a nonlinear model of complex network cascade failure, considering overload and weighted edges, to evaluate the severity of power grid failures using a generator-load power percentage index.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional electrical betweenness algorithm uses all path algorithms to search power generation-load node pairs, then accuracy of identifying fragile lines is improved, but calculation time increases significantly
Solution Approach 1:
The patent segments the power grid network into smaller components and processes electrical betweenness calculations in a distributed manner. By dividing the large-scale network computation into manageable segments, the algorithm reduces the computational burden on single processors and enables parallel processing, thereby decreasing overall calculation time while maintaining accuracy in identifying fragile lines.
Solution Approach 2:
The patent applies partial action by focusing calculations only on critical node pairs and paths that significantly contribute to fragile line identification. Instead of exhaustively computing all possible paths in the network, the algorithm identifies and processes only the most influential pathways, reducing computational complexity and time requirements while preserving the accuracy needed for practical applications.
2Productivity
If power flow is assumed to flow only along the shortest path, then calculation efficiency is improved, but accuracy of fragile line identification deteriorates
Solution Approach 1:
The patent introduces dynamic routing capabilities that allow power flow paths to adapt based on network conditions and failure scenarios. Instead of statically assuming shortest paths, the algorithm dynamically recalculates power flow distributions when lines are removed, capturing the actual behavior of power grids more accurately while maintaining computational efficiency through optimized dynamic programming approaches.
Solution Approach 2:
The patent employs an intermediary computational layer that bridges the gap between simple shortest-path assumptions and complex all-path algorithms. This intermediary approach uses optimized flow algorithms that consider multiple paths without requiring exhaustive enumeration, providing a middle ground that maintains both calculation efficiency and improved accuracy in fragile line identification.
3Device complexity
If connectivity level index is used to reflect damage degree, then measurement is simplified, but self-balance ability of internal power subsystems is not considered
Solution Approach 1:
The patent merges multiple evaluation dimensions into a comprehensive fragile line identification framework. It combines topological connectivity metrics with power flow characteristics and subsystem self-balance capabilities into a unified assessment model. This integration allows the system to maintain measurement simplicity while simultaneously considering the self-balance ability of internal power subsystems, resolving the contradiction between simplicity and reliability.
4Device complexity
If percentage of load loss index is used to evaluate system damage, then calculation is simplified, but damage degree of damaged generator to power grid is ignored
Solution Approach 1:
The patent applies local quality by differentiating the assessment of damage at different locations within the power grid. Instead of using a uniform load loss percentage for all components, the algorithm assigns location-specific weights and evaluation criteria that reflect the unique importance and characteristics of generators, transmission lines, and load centers. This enables simplified calculation while achieving complete damage assessment by tailoring the evaluation to local conditions.
Data Source
AI summary
Disclosed is a method for identifying fragile lines in power grid based on electrical betweenness, which comprises the following steps: constructing the power grid into a network diagram, sequentially removing lines in the network diagram, and sorting the electrical betweenness of each line from large to small; constructing a nonlinear model of complex network cascade failure considering overload and weighted edges, and respectively performing two ways of removing lines for sorted electrical betweenness, namely sequentially removing preset proportion lines and sequentially removing all lines until no new lines are removed in the network diagram; obtaining a change of generator-load power before and after each line removal, and evaluating a severity of power grid failure based on the change of generator-load power, thus completing an identification of power grid fragile lines.