Power flow forecasts at transmission grid nodes using Graph Neural Networks

The increasing share of renewable energy in the electricity grid and progressing changes in power consumption have led to fluctuating,and weather-dependent power flows.To ensure grid stability,grid operators rely on power forecasts which are crucial for grid calculations and planning.In this paper,a Multi-Task Learning approach is combined with a Graph Neural Network(GNN)to predict vertical power flows at transformers connecting high and extra-high voltage levels.The proposed method accounts for local differences in power flow characteristics by using an Embedding Multi-Task Learning approach.The use of a Bayesian embedding to capture the latent node characteristics allows to share the weights across all transformers in the subsequent node-invariant GNN while still allowing the individual behavioral patterns of the transformers to be distinguished.At the same time,dependencies between transformers are considered by the GNN architecture which can learn relationships between different transformers and thus take into account that power flows in an electricity network are not independent from each other.The effectiveness of the proposed method is demonstrated through evaluation on two real-world data sets provided by two of four German Transmission System Operators,comprising large portions of the operated German transmission grid.The results show that the proposed Multi-Task Graph Neural Network is a suitable representation learner for electricity networks with a clear advantage provided by the preceding embedding layer.It is able to capture interconnections between correlated transformers and indeed improves the performance in power flow prediction compared to standard Neural Networks.A sign test shows that the proposed model reduces the test RMSE on both data sets compared to the benchmark models significantly.

Managing power grids through topology actions: A comparative study between advanced rule-based and reinforcement learning agents

The operation of electricity grids has become increasingly complex due to the current upheaval and the increase in renewable energy production.As a consequence,active grid management is reaching its limits with conventional approaches.In the context of the Learning to Run a Power Network(L2RPN)challenge,it has been shown that Reinforcement Learning(RL)is an efficient and reliable approach with considerable potential for automatic grid operation.In this article,we analyse the submitted agent from Binbinchen and provide novel strategies to improve the agent,both for the RL and the rule-based approach.The main improvement is a N-1 strategy,where we consider topology actions that keep the grid stable,even if one line is disconnected.More,we also propose a topology reversion to the original grid,which proved to be beneficial.The improvements are tested against reference approaches on the challenge test sets and are able to increase the performance of the rule-based agent by 27%.In direct comparison between rule-based and RL agent we find similar performance.However,the RL agent has a clear computational advantage.We also analyse the behaviour in an exemplary case in more detail to provide additional insights.Here,we observe that through the N-1 strategy,the actions of both the rule-based and the RL agent become more diversified.

Autoencoder-based anomaly root cause analysis for wind turbines

A popular method to detect anomalous behaviour or specific failures in wind turbine sensor data uses a specific type of neural network called an autoencoder.These models have proven to be very successful in detecting such deviations,yet cannot show the underlying cause or failure directly.Such information is necessary for the implementation of these models in the planning of maintenance actions.In this paper we introduce a novel method:ARCANA.We use ARCANA to identify the possible root causes of anomalies detected by an autoencoder.It describes the process of reconstruction as an optimisation problem that aims to remove anomalous properties from an anomaly considerably.This reconstruction must be similar to the anomaly and thus identify only a few,but highly explanatory anomalous features,in the sense of Ockham’s razor.The proposed method is applied on an open data set of wind turbine sensor data,where an artificial error was added onto the wind speed sensor measurements to acquire a controlled test environment.The results are compared with the reconstruction errors of the autoencoder output.The ARCANA method points out the wind speed sensor correctly with a significantly higher feature importance than the other features,whereas using the non-optimised reconstruction error does not.Even though the deviation in one specific input feature is very large,the reconstruction error of many other features is large as well,complicating the interpretation of the detected anomaly.Additionally,we apply ARCANA to a set of offshore wind turbine data.Two case studies are discussed,demonstrating the technical relevance of ARCANA.