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 doublesigmoid 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.
Synchrophasor based applications become more and more popular in today’s control centers to monitor and control transient system events. This can ensure secure system operation when dealing with bidirectional power flows, diminishing reserves and an increased number of active grid components. Today’s synchrophasor applications provide a lot of additional information about the dynamic system behavior but without significant improvement of the system operation due to the lack of interpretable and condensed results as well as missing integration into existing decision-making processes. This study presents a holistic framework for novel machine learning based applications analyzing both historical as well as online synchrophasor data streams. Different methods from dimension reduction, anomaly detection as well as time series classification are used to automatically detect disturbances combined with a web-based online visualization tool. This enables automated decision-making processes in control centers to mitigate critical system states and to ensure secure system operations (e.g., by activating curate actions). Measurement and simulation-based results are presented to evaluate the proposed synchrophasor application modules for different use cases at the transmission and distribution level.
A microgrid is an independent power system that can be connected to the grid or operated in an islanded mode. This single grid entity is widely used for furthering access to energy and ensuring reliable energy supply. However, if islanded, microgrids do not benefit from the high inertia of the main grid and can be subject to high variations in terms of voltage and frequency, which challenge their stability. In addition, operability and interoperability requirements, standards as well as directives have addressed main concerns regarding a microgrid’s reliability, use of distributed local resources and cybersecurity. Nevertheless, microgrid systems are quickly evolving through digitalization and have a large range of applications. Thus, a consensus over their testing must be further developed with the current technological development. Here, we describe existing technical requirements and assessment criteria for a microgrid’s main functionalities to foster harmonization of functionality-level testing and an international conception of system-level one. This framework is proposed as a reference document for assessment frame development serving both microgrid research and implementation for a comprehensive understanding of technical microgrid performance and its current assessment challenges, such as lack of standardization and evolving technology.
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