An Operational Data-Driven Malfunction Detection Framework for Enhanced Power Distribution System Monitoring -- The DeMaDs Approach

TL;DR

This paper introduces a data-driven malfunction detection framework for power distribution systems that enhances monitoring capabilities and can be integrated into existing infrastructure, addressing the need for better grid support with limited sensors.

Contribution

The paper presents a novel multi-stage detection framework for grid misconfigurations that is easily integrable into current metering systems.

Findings

Effective detection of grid misconfigurations demonstrated

Framework seamlessly integrates with existing infrastructure

Validation shows improved monitoring capabilities

Abstract

The changes in the electric energy system toward a sustainable future are inevitable and already on the way today. This often entails a change of paradigm for the electric energy grid, for example, the switch from central to decentralized power generation which also has to provide grid-supporting functionalities. However, due to the scarcity of distributed sensors, new solutions for grid operators for monitoring these functionalities are needed. The framework presented in this work allows to apply and assess data-driven detection methods in order to implement such monitoring capabilities. Furthermore, an approach to a multi-stage detection of misconfigurations is introduced. Details on implementations of the single stages as well as their requirements are also presented. Furthermore, testing and validation results are discussed. Due to its feature of being seamlessly integrable into system operators' current metering infrastructure, clear benefits of the proposed solution are pointed out.