基于物理神经网络的输电网运行方式智能生成方法

doi:10.19595/j.cnki.1000-6753.tces.260671

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Abstract

With the large-scale integration of renewable energy and the increasing complexity of modern transmission network structures, critical operating samples located in high-risk regions have become increasingly scarce. Traditional scenario construction relies on iterative trial-and-error adjustments of generator outputs based on load variations, resulting in extremely high computational costs and low efficiency. Meanwhile, existing data-driven generative models predominantly adopt unconditional blind sampling. Due to the absence of physical constraints, they are highly susceptible to producing a massive amount of invalid samples with non-convergent power flow solutions.To address these issues of sample scarcity and generation distortion, a targeted generation method for transmission network operating scenarios based on a physics-informed conditional Wasserstein generative adversarial network (PINN-cWGAN) is proposed.
A feature space decoupling framework based on conditional mapping is constructed. The system nodal admittance matrix, load demand, and generator operational boundaries are formulated as prior condition features, while the generator outputs and bus voltages are defined as target features, which breaks the limitations of traditional unconditional blind sampling. Secondly, based on the Wasserstein distance and gradient penalty (WGAN-GP), a conditional generative adversarial architecture is designed to map the high-dimensional statistical distribution from specific objective operational boundaries to secure dispatch responses. Thirdly, to significantly improve the physical validity of the generated samples, AC power flow balance equations and generator output limits are embedded into the physical-informed neural network (PINN) as soft constraints. This mechanism utilizes physical residuals to provide gradient feedback, guiding the generator toward the physically feasible domain. Finally, a two-stage transfer learning strategy based on the global loading margin (LM) is proposed. The model is initially pre-trained on abundant high-margin source domain data, and then its parameters are transferred and fine-tuned on scarce low-margin target domain samples, which improves distribution learning under high-dimensional and small-sample conditions.
Simulation results on the IEEE 118-bus and PEGASE 1354-bus systems demonstrate the effectiveness of the proposed PINN-cWGAN model under multiple operating scenarios, including normal operating conditions, extreme load fluctuations, high renewable energy penetration, and N-k cascading line outages. Compared to traditional Monte Carlo trial-and-error algorithms, the proposed method reduces the generation time of 1000 valid samples from several hours to merely a few seconds. Furthermore, compared with the Generative Adversarial Network (GAN), Gaussian Mixture Model (GMM), Denoising Diffusion Probabilistic Model (DDPM), and Variational Autoencoder (VAE), the proposed method achieves improved distribution fidelity, as indicated by a lower Fréchet Inception Distance (FID). Notably, the physical compliance rate of the generated large-scale samples reaches nearly 100%, and the power flow convergence rate remains above 90% even under critically hazardous conditions (e.g., severe topological mutations), outperforming the compared data-driven methods.
The following conclusions can be drawn from the simulation analysis: (1) The conditional mapping mechanism empowers the deep generative model with conditional response capabilities, allowing for the targeted generation of massive dispatch scenarios under specified load fluctuation ranges and varied topologies. (2) Integrating the AC power flow physical residuals and hard operational boundaries into the PINN loss function effectively alleviates the physical inconsistency commonly observed in purely data-driven models, enhancing the physical consistency and practical value of the generated data. (3) The proposed LM-based transfer learning strategy successfully addresses the natural scarcity of critical low-margin samples, providing an effective data augmentation method for transmission network security assessment and risk analysis.

Key words： Transmission network operating scenarios Conditional mapping Generative adversarial network Transfer learning PINN-cWGAN

Received: 06 May 2026

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TM732

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	Li Bin
	Gan Minghao
	Ye Jinyu
	Chen Biyun
	Li Peijie

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Li Bin,Gan Minghao,Ye Jinyu等. Intelligent Generation Method of Transmission Network Operating Scenarios Based on Physics-Informed Constraints[J]. Transactions of China Electrotechnical Society, 0, (): 260671-.

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