The DeMaDs Open Source Modeling Framework for Power System Malfunction Detection

TL;DR

The paper introduces DeMaDs, an open-source framework for simulating power systems and detecting malfunctions using various data-driven methods, aiding development of smart grid functionalities.

Contribution

It presents a novel open-source framework that simulates power grid malfunctions and evaluates data-driven detection techniques in a unified environment.

Findings

Framework effectively simulates power system malfunctions.

Supports testing of machine learning and deep learning detection methods.

Provides an illustrative example demonstrating its capabilities.

Abstract

Modeling and simulation of electrical power systems are becoming increasingly important approaches for the development and operation of novel smart grid functionalities -- especially with regard to data-driven applications as data of certain operational states or misconfigurations can be next to impossible to obtain. The DeMaDs framework allows for the simulation and modeling of electric power grids and malfunctions therein. Furthermore, it serves as a testbed to assess the applicability of various data-driven malfunction detection methods. These include data mining techniques, traditional machine learning approaches as well as deep learning methods. The framework's capabilities and functionality are laid out here, as well as explained by the means of an illustrative example.