arXiv subjects

Andrej Čampa

Publications and source records attributed to Andrej Čampa.

3 recordsLinked to original sources

arXiv2023

Traveling Wave Method for Event Localization and Characterization of Power Transmission Lines

Traveling wave theory is deployed today to improve the monitoring of transmission lines in electrical power grids. Most traveling wave methods require prior knowledge of the wave propagation of the transmission line, which is a major source of error as the value changes during the operation of the line. To improve the localization of events on transmission lines, we propose a new online localization method that simultaneously determines the frequency-dependent wave propagation characteristic from the traveling wave measurements of the event. Compared to conventional methods, this is achieved with one additional traveling wave measurement, but the method can still be applied in different measurement setups. We have derived the method based on the complex continuous wavelet transform. The accuracy of the method is evaluated in a simulation with a frequency-dependent transmission line model of the IEEE 39-bus system. The method was developed independently of the type of event and evaluated in test setups considering different lengths of the monitored line, line types and event locations. The localization accuracy is compared with existing online methods and analyzed with regard to the characterization capabilities. The method has a high relative localization accuracy in the range of 0.1\,\% under different test conditions.

eess.SP↗

arXiv2022

On-Premise Artificial Intelligence as a Service for Small and Medium Size Setups

Artificial Intelligence (AI) technologies are moving from customized deployments in specific domains towards generic solutions horizontally permeating vertical domains and industries. For instance, decisions on when to perform maintenance of roads or bridges or how to optimize public lighting in view of costs and safety in smart cities are increasingly informed by AI models. While various commercial solutions offer user friendly and easy to use AI as a Service (AIaaS), functionality-wise enabling the democratization of such ecosystems, open-source equivalent ecosystems are lagging behind. In this chapter, we discuss AIaaS functionality and corresponding technology stack and analyze possible realizations using open source user friendly technologies that are suitable for on-premise set-ups of small and medium sized users allowing full control over the data and technological platform without any third-party dependence or vendor lock-in.

cs.SE↗

arXiv2022

On Designing Data Models for Energy Feature Stores

The digital transformation of the energy infrastructure enables new, data driven, applications often supported by machine learning models. However, domain specific data transformations, pre-processing and management in modern data driven pipelines is yet to be addressed. In this paper we perform a first time study on generic data models that are able to support designing feature management solutions that are the most important component in developing ML-based energy applications. We first propose a taxonomy for designing data models suitable for energy applications, explain how this model can support the design of features and their subsequent management by specialized feature stores. Using a short-term forecasting dataset, we show the benefits of designing richer data models and engineering the features on the performance of the resulting models. Finally, we benchmark three complementary feature management solutions, including an open-source feature store suitable for time series.

cs.AI↗