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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| Veröffentlicht in: | 2025 8th International Conference on Computer Information Science and Application Technology (CISAT) S. 1118 - 1121 |
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IEEE
11.07.2025
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Liu, Junyi |
| Author_xml | – sequence: 1 givenname: Junyi surname: Liu fullname: Liu, Junyi email: liujunyi921@nuaa.edu.cn organization: Nanjing University of Aeronautics and Astronautics,College of Automation,Nanjing,China |
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| Snippet | This paper proposes a structure-aware layout optimization method for 500kV substation main wiring diagrams based on an enhanced genetic algorithm. Substation... |
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| SubjectTerms | 500kV substation Convergence enhanced genetic algorithm Genetic algorithms Layout layout optimization main wiring diagram Stability analysis Substations Topology Voltage Wiring XML |
| Title | Layout Optimization of 500kV Substation Main Wiring Diagrams Based on Enhanced Genetic Algorithm |
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