Selective State-Space Models for Koopman-based Data-driven Distribution System State Estimation

TL;DR

This paper introduces MambaDSSE, a data-driven, model-free framework using Koopman theory for distribution system state estimation, improving scalability, robustness, and long-term dependency modeling.

Contribution

The work presents a novel selective state-space model integrating Koopman-theoretic probabilistic filtering for enhanced DSSE performance.

Findings

Outperforms machine learning baselines in scalability and resilience.

Effective across various test systems and DER penetration levels.

Captures long-range dependencies, improving estimation accuracy.

Abstract

Distribution System State Estimation (DSSE) plays an increasingly-important role in modern power grids due to the integration of distributed energy resources (DERs). The inherent characteristics of distribution systems make classical estimation methods struggle, and recent advancements in data-driven learning methods, although promising, exhibit systematic failure in generalization and scalability that limits their applicability. In this work, we propose MambaDSSE, a model-free data-driven framework that incorporates Koopman-theoretic probabilistic filtering with a selective state-space model that learn to infer the underlying time-varying behavior of the system from data. We evaluate the model across a variety of test systems and scenarios, and demonstrate that the proposed method outperforms machine learning baselines on scalability, resilience to DER penetration levels, and robustness to data sampling rate irregularities. We further highlight the Mamba-based SSM's ability to capture long range dependencies from data, improving performance on the DSSE task.