# Data-driven Inverter-based Volt/VAr Control for Partially Observable Distribution Networks

arXiv: https://arxiv.org/abs/2007.16039 · HTML: https://mlsanity.com/paper/2007.16039/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jul 2020.

## Built on by (most cited first, 1 of 1)

- [Robust Deep Reinforcement Learning for Inverter-based Volt-Var Control in Partially Observable Distribution Networks](https://mlsanity.com/paper/2408.06776/) (2024)

## Abstract (arXiv)

For active distribution networks (ADNs) integrated with massive inverter-based energy resources, it is impractical to maintain the accurate model and deploy measurements at all nodes due to the large-scale of ADNs. Thus, current models of ADNs are usually involving significant errors or even unknown. Moreover, ADNs are usually partially observable since only a few measurements are available at pilot nodes or nodes with significant users. To provide a practical Volt/Var control (VVC) strategy for such networks, a data-driven VVC method is proposed in this paper. Firstly, the system response policy, approximating the relationship between the control variables and states of monitoring nodes, is estimated by a recursive regression closed-form solution. Then, based on real-time measurements and the newly updated system response policy, a VVC strategy with convergence guarantee is realized. Since the recursive regression solution is embedded in the control stage, a data-driven closed-loop VVC framework is established. The effectiveness of the proposed method is validated in an unbalanced distribution system considering nonlinear loads where not only the rapid and self-adaptive voltage regulation is realized but also system-wide optimization is achieved.