A Novel Cascading Artificial Neural Networks for Enhanced Distribution Network State Estimation

The growing integration of renewable energy sources and the widespread adoption of electric vehicles add complexity to power flows in distribution systems. Understanding the system dynamics and implementing control actions are imperative for effective grid management. While measurements in specific nodes are facilitated by smart meters, accurate sensors, communication networks, and electric vehicles, it is costly and, in some cases, impossible to obtain measurements at all nodes. In response to these challenges, this paper introduces a machine learning-based state estimation method, utilizing available measurements and estimating others. By harnessing data-driven techniques, the proposed method aims to provide accurate estimations of voltages and currents in distribution networks, offering valuable insights into flexibilities management and control processes. Specific case study is considered, confirming the efficiency of the suggested state estimation approach and providing insights into its flexibility across a range of scenarios. In addition to the mentioned benefits, the results validate the proposed method, demonstrating a small margin of error. This approach holds significant promises in improving situational awareness for power system operators and enhancing their understanding of the evolving grid dynamics.