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The introduction of virtual resistance can effectively suppress the circulating current between microsources and improve power allocation in low-voltage microgrid, but it also causes the voltage deviation of microsources’ inverters. An optimization method of virtual resistance based on hybrid index is proposed in order to suppress circulating current and improve voltage deviation at the same time in this paper. The gradient descent method is used to design the virtual resistance optimization process, aiming at the optimization of hybrid index composed of circulating current and voltage deviation. The constraints are deduced with power quality requirements, capacity limitation and static stability, and then virtual resistance values are optimized. The effects of switching load and microsource on the optimization results are analyzed through the simulation of low-voltage microgrid, and the simulation results show that the virtual resistance optimization method can significantly suppress circulating current while improving power quality.

State-of-the-art meta-learning techniques do not optimize for zero-shot adaptation to unseen tasks, a setting in which humans excel. On the contrary, meta-learning algorithms learn hyperparameters and weight initializations that explicitly optimize for few-shot learning performance. In this work, we take inspiration from recent advances in generative modeling and language-conditioned image synthesis to propose meta-learning techniques that use natural language guidance to achieve higher zero-shot performance compared to the state-of-the-art. We do so by recasting the meta-learning problem as a multi-modal generative modeling problem: given a task, we consider its adapted neural network weights and its natural language description as equivalent multi-modal task representations. We first train an unconditional generative hypernetwork model to produce neural network weights; then we train a second "guidance" model that, given a natural language task description, traverses the hypernetwork latent space to find high-performance task-adapted weights in a zero-shot manner. We explore two alternative approaches for latent space guidance: "HyperCLIP"-based classifier guidance and a conditional Hypernetwork Latent Diffusion Model ("HyperLDM"), which we show to benefit from the classifier-free guidance technique common in image generation. Finally, we demonstrate that our approaches outperform existing meta-learning methods with zero-shot learning experiments on our Meta-VQA dataset, which we specifically constructed to reflect the multi-modal meta-learning setting.

Additional file 1. Additional figures.