Fractal geometry offers a natural language for the self-similarity and scale invariance of power networks, yet future receiving-end grids shaped by high renewable penetration, high load density and large HVDC infeed have no historical counterpart. This paper formalises the synthesis of such grids as a Constrained Fractal Iteration System (CFIS), in which synthetic topology is generated by the scale-progressive composition of three fractal operators, each coupling a spatial clustering/topology operator with an engineering constraint-projection operator. A hybrid metaheuristic realises the operators through dynamic-weighted K-means (DWK-Means), social-learning particle swarm optimisation (SL-PSO) and constraint-embedded 2-opt (CE-2-opt). On a provincial receiving-end grid at 162, 234 and 363 GW, the generated topologies are scale-invariant over a well-defined box-size range: the box-counting dimension rises from 1.675 to 1.800 with load and shows a clear voltage-layered hierarchy; the constrained dimension remains within 1.5% of the unconstrained value, showing that engineering feasibility costs almost no fractal quality. The 500 kV station set attains a correlation dimension of 1.81 versus 1.69 for the real existing grid, consistent with the interpretation of the synthetic grid as an idealised fractal upper bound. Short-circuit levels are brought within the 63 kA limit after correction, and renewable curtailment and load shedding remain zero; N-1 screening of all elements shows that the worst post-contingency loading falls from 324.7% at 162 GW to 141.1% at 363 GW. Against classical topology generators built on the same node set, the proposed backbone attains full degree-two redundancy and a comparable fractal dimension with about 24% less line length, and an ablation of the redundancy bound shows that it, rather than the short-circuit limit, governs steady-state security at the low load scale.

Volatile organic compounds (VOCs, dominated by xylene, toluene, and benzene) and paint mist emissions from ship painting represent a major environmental and health concern, posing a critical bottleneck to the green transformation of the shipbuilding industry. To tackle this challenge, this study presents an integrated recovery system designed specifically for ship automatic-spraying robots. Guided by the synergistic principle of “air-curtain containment, multi-stage adsorption, and negative-pressure recovery,” the system features a modular design that ensures full compatibility with the robots’ spraying trajectory without operational interference. Core adsorption materials, namely glass fiber filter cotton and honeycomb activated carbon fiber, were selected to suit the high-humidity and high-pollutant-concentration environment typical of ship painting. An appropriately matched axial flow fan maintains stable negative pressure throughout the system. Furthermore, the design integrates an air curtain isolation subsystem and an automated control subsystem, enabling coordinated operation and real-time adjustment. Using ANSYS Fluent, geometric and flow field simulation models were established to analyze airflow distribution and pollutant adsorption behavior, which led to the optimization of key structural and material parameters. Field experiments conducted in shipyard environments demonstrated the system’s superior performance: it achieved a VOC removal efficiency of 88.4% and a paint mist capture efficiency of 85.7% under optimal working conditions, with a maximum simulated paint mist capture efficiency of 86.2%. The system maintained stable performance under complex vertical and overhead spraying conditions, with an efficiency attenuation of less than 1.5%, and its outlet emissions fully complied with the mandatory limits specified in the Emission Standard of Air Pollutants for the Shipbuilding Industry (GB 30981.2-2025). The relative error between experimental data and simulation results is less than 2%, confirming the reliability and practicality of the proposed system. This research provides an efficient and adaptable pollution control solution for green shipbuilding and offers valuable technical insights for the sustainable upgrading of automated painting processes in heavy industries.

Fractal geometry offers a natural language for the self-similarity and scale invariance of power networks, yet future receiving-end grids shaped by high renewable penetration, high load density and large HVDC infeed have no historical counterpart. This paper formalizes the synthesis of such grids as a Constrained Fractal Iteration System (CFIS), in which synthetic topology is generated by the scale-progressive composition of three fractal operators, each coupling a spatial clustering/topology operator with an engineering constraint-projection operator. A hybrid metaheuristic realizes the operators through dynamic-weighted K-means (DWK-Means), social-learning particle swarm optimization (SL-PSO) and constraint-embedded 2-opt (CE-2-opt). On a provincial receiving-end grid at 162, 234, and 363 GW, the generated topologies are scale-invariant over a well-defined box-size range: the box-counting dimension rises from 1.675 to 1.800 with load and shows a clear voltage-layered hierarchy; the constrained dimension remains within 1.5% of the unconstrained value, showing that engineering feasibility costs almost no fractal quality. The 500 kV station set attains a correlation dimension of 1.81 versus 1.69 for the real existing grid, consistent with the interpretation of the synthetic grid as an idealized fractal upper bound. Short-circuit levels are brought within the 63 kA limit after correction, and renewable curtailment and load shedding remain zero; N-1 screening of all elements shows that the worst post-contingency loading falls from 98.5% at 162 GW to 91.4% at 363 GW, satisfying the thermal limits under single contingencies. Against classical topology generators built on the same node set, the proposed backbone attains full degree-two redundancy and a comparable fractal dimension with about 24% less line length, and an ablation of the redundancy bound shows that it, rather than the short-circuit limit, governs steady-state security at the low load scale.