Layout Optimization of 500kV Substation Main Wiring Diagrams Based on Enhanced Genetic Algorithm

This paper proposes a structure-aware layout optimization method for 500kV substation main wiring diagrams based on an enhanced genetic algorithm. Substation Configuration Description (SCD) files, which encode the logical topology and equipment relationships in XML format, are parsed to extract volt...

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Bibliographic Details
Published in:2025 8th International Conference on Computer Information Science and Application Technology (CISAT) pp. 1118 - 1121
Main Author: Liu, Junyi
Format: Conference Proceeding
Language:English
Published: IEEE 11.07.2025
Subjects:
500kV substation
Convergence
enhanced genetic algorithm
Genetic algorithms
Layout
layout optimization
main wiring diagram
Stability analysis
Substations
Topology
Voltage
Wiring
XML
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Summary:This paper proposes a structure-aware layout optimization method for 500kV substation main wiring diagrams based on an enhanced genetic algorithm. Substation Configuration Description (SCD) files, which encode the logical topology and equipment relationships in XML format, are parsed to extract voltage levels, bay units, and device connections. A geometric modeling framework is then constructed to abstract the layout into structured regional blocks. A two-segment chromosome encoding is designed to represent both region assignment and intra-region ordering. The optimization process incorporates a bi-objective fitness function, considering load imbalance and logical connection distance, to guide the search toward spatially balanced and topologically compact layouts. Graph-guided repair and region-aware perturbation strategies are introduced to improve convergence stability and layout feasibility. Experimental validation on a 500kV substation demonstrates that the proposed method achieves better space distribution and faster convergence compared to standard genetic algorithms, offering practical value for automated layout generation in intelligent substation design.
DOI:10.1109/CISAT66811.2025.11181851