Siamese recurrent neural networks for the robust classification of grid disturbances in transmission power systems considering unknown events

The automated identification and localisation of grid disturbances is a major research area and key technology for the monitoring and control of future power systems. Current recognition systems rely on sufficient training data and are very error-prone to disturbance events, which are unseen during training. This study introduces a robust Siamese recurrent neural network using attention-based embedding functions to simultaneously identify and locate disturbances from synchrophasor data. Additionally, a novel double-sigmoid classifier is introduced for reliable differentiation between known and unknown disturbance types and locations. Different models are evaluated within an open-set classification problem for a generic power transmission system considering different unknown disturbance events. A detailed analysis of the results is provided and classification results are compared with a state-of-the-art open-set classifier

Combined network intrusion and phasor data anomaly detection for secure dynamic control centers

The dynamic operation of power transmission systems requires the acquisition of reliable and accurate measurement and state information. The use of TCP/IP-based communication protocols such as IEEE C37.118 or IEC 61850 introduces different gateways to launch cyber-attacks and to compromise major system operation functionalities. Within this study, a combined network intrusion and phasor data anomaly detection system is proposed to enable a secure system operation in the presence of cyber-attacks for dynamic control centers. This includes the utilization of expert-rules, one-class classifiers, as well as recurrent neural networks to monitor different network packet and measurement information. The effectiveness of the proposed network intrusion and phasor data anomaly detection system is shown within a real-time simulation testbed considering multiple operation and cyber-attack conditions

Outlier Detection Methods for Uncovering of Critical Events in Historical Phasor Measurement Records

The scope of this survey is the uncovering of potential critical events from mixed PMU data sets. An unsupervised procedure is introduced with the use of different outlier detection methods. For that, different techniques for signal analysis are used to generate features in time and frequency domain as well as linear and non-linear dimension reduction techniques. That approach enables the exploration of critical grid dynamics in power systems without prior knowledge about existing failure patterns. Furthermore new failure patterns can be extracted for the creation of training data sets used for online detection algorithms

Simultaneous online identification and localization of disturbances in power transmission systems

Within this survey an approach is presented for the simultaneous online identification and localization of grid disturbances in transmission power systems using different techniques for multivariate time series classification. For the generation of the training data dynamic simulations are performed using DIgSILENT ® PowerFactory combined with a Monte Carlo based initial state selection. Within this survey different classifiers are developed and compared with each other including dynamic time warping, support vector machines, shapelets, recurrent neural networks and random forests. The performance is evaluated in terms of classification accuracy and prediction time

Combined Network Intrusion and Phasor Data Anomaly Detection for Secure Dynamic Control Centers

The dynamic operation of power transmission systems requires the acquisition of reliable and accurate measurement and state information. The use of TCP/IP-based communication protocols such as IEEE C37.118 or IEC 61850 introduces different gateways to launch cyber-attacks and to compromise major system operation functionalities. Within this study, a combined network intrusion and phasor data anomaly detection system is proposed to enable a secure system operation in the presence of cyber-attacks for dynamic control centers. This includes the utilization of expert-rules, one-class classifiers, as well as recurrent neural networks to monitor different network packet and measurement information. The effectiveness of the proposed network intrusion and phasor data anomaly detection system is shown within a real-time simulation testbed considering multiple operation and cyber-attack conditions

PMU-based online and offline applications for modern power system control centers in hybrid AC-HVDC transmission systems

This investigation presents new control center applications for the management of hybrid AC-DC transmission systems incorporating phasor measurements. This is done by the identification and extraction of critical events from historical and online measurement records as well as dynamic simulation data using artificial intelligence methods. The new control center applications provide a web-based dynamic monitoring interface for the operator assistance

Challenges and opportunities for phasor data based event detection in transmission control centers under cyber security constraints

The scope of this survey is the phasor based event detection under cyber security constraints in modern transmission control centers. A general concept for a physical and cyber event detection is developed as a combination of state-of-the-art disturbance classification and cyber-attack detection methods. This requires the incorporation of heterogeneous data sources and advanced data fusion and analysis techniques. An enhanced and robust recognition of the current grid situation is proposed to distinguish between different scheduled and unscheduled grid events. Furthermore, Digital Twins are considered as new promising technology for control centers and potential benefits for physical and cyber event detection are described