Probabilistic prediction for electrical loads receives more attention in recent years for leveraging big data and assessing diverse scenarios. Since the classical machine learning (ML) model as a 'blackbox' predictor cannot produce the probabilistic ...

High-resolution signals from micro-phasor measurement units (μPMU) contain crucial information about the health and status of electric equipment in power grids. In this work, we provide an end-to-end framework for fault state discovery in μPMU data. Our ...

Availability of high fidelity timeseries data is imperative for critical power grid operational tasks such as state estimation, DER scheduling, etc. However, the data obtained from the metering infrastructure is prone to disruptions due to communication ...

The shift towards an advanced electricity metering infrastructure has gained traction because of several smart meter roll-outs. This accelerated research in Non-Intrusive Load Monitoring techniques. These techniques highly benefit from the temporal ...

Smart meters are the backbone of smart grids. They provide real time electricity consumption data and and are widely used for measuring, monitoring and analyzing energy consumption. Sometimes, they enable users to perform corrective actions. But, to ...

The emergence of low-power accelerators has enabled deep learning models to be executed on mobile or embedded edge devices without relying on cloud resources. The energy-constrained nature of these devices requires a judicious choice of a deep learning ...

Fast localization of earth faults in medium voltage grids is required in order to avoid subsequent faults and to quickly restore the normal grid operation. We propose a localization approach utilizing a signature database with high-resolution transient ...

With their rising adoption and integration into smart grids, heat pumps are becoming an increasingly important source of flexible energy. Heat pump flexibility can be utilized by using controllers to remotely manage their operation while maintaining the ...

The dynamics-aware economic dispatch (DED) problem embeds low-level generator dynamics and operational constraints to enable near real-time scheduling of generation units in a power network. DED produces a more dynamic supervisory control policy than ...

Distributed energy technologies introduce new volatility to the edges of low voltage grids and increase the importance of short-term forecasting of electric loads at a granular level. To address this issue, first probabilistic forecasting models for ...

Stability assessment is an important task for maintaining reliable operations of power grids. With increased system complexity, deep learning-based stability assessment approaches are promising to address the shortfalls of the traditional time-domain ...

Stationary battery energy storage systems and electric vehicles become more and more popular at households with local photovoltaic generation. Besides improving self-consumption and autarchy, these batteries can provide flexibility to an external ...