This paper deals with the essentials of synchrophasor’s applications for future power systems to increase system reliability and resilience, which have been investigated within a four-year research project. The project has several applications, covering real-time disturbance detection and blackout prevention distributed across multiple work-packages. Firstly, an advanced big-data management platform built in a real-time digital simulation (RTDS) environment is described to support measurement data collection, processing, and sharing among stakeholders. This platform further presents and demonstrates a network-splitting methodology to avoid cascading failures. Online generator coherency identification is another synchrophasor application implemented on the platform, the use of which is demonstrated in the context of controlled network splitting. Using synchrophasors, data-analytics techniques can also identify and classify disturbances in real time with minor human intervention. Therefore, a novel centralized artificial intelligence (AI) based expert system is outlined to detect and classify critical events. Finally, the paper elaborates on developing advanced system resilience metrics for real-time vulnerability assessment of power systems with a high penetration of renewable energy, focusing on increasingly relevant dynamic interactions and system instability risks.@en

Implementing accurate Distribution System State Estimation (DSSE) faces several challenges, among which the lack of observability and the high density of the distribution system. While data-driven alternatives based on Machine Learning models could be a choice, they suffer in DSSE because of the lack of labeled data. In fact, measurements in the distribution system are often noisy, corrupted, and unavailable. To address these issues, we propose the Deep Statistical Solver for Distribution System State Estimation (DSS^2), a deep learning model based on graph neural networks (GNNs) that accounts for the network structure of the distribution system and the governing power flow equations of the problem. DSS^2 is based on GNN and leverages hypergraphs to model the network as a graph into the deep-learning algorithm and to represent the heterogeneous components of the distribution systems. A weakly supervised learning approach is put forth to train the DSS^2: by enforcing the GNN output into the power flow equations, we force the DSS^2 to respect the physics of the distribution system. This strategy enables learning from noisy measurements and alleviates the need for ideal labeled data. Extensive experiments with case studies on the IEEE 14-bus, 70-bus, and 179-bus networks showed the DSS^2 outperforms the conventional Weighted Least Squares algorithm in accuracy, convergence, and computational time while being more robust to noisy, erroneous, and missing measurements. The DSS^2 achieves a competing, yet lower, performance compared with the supervised models that rely on the unrealistic assumption of having all the true labels.

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The installation of phasor measurement units (PMUs) by Enduris, one of the distribution network operators in the Netherlands to monitor their 50 kV ring distribution grid has resulted in increased observability of the power flows and voltages. However, with the increased observability, there is a need to analyse the distribution grid based on the data generated by the PMU monitoring system. This study provides steady-state and dynamic analysis of the 50 kV Enduris ring distribution grid in terms of voltage and power flow behaviour based on the real-time data acquired from the PMUs. First, the Enduris 50 kV distribution grid under study is modelled in PowerFactory software. This model is subsequently used for analysis of the distribution grid behaviour for two contingencies. The contingencies are actual grid events observed by the PMUs, and using this data, the contingencies are simulated and analysed. The analysis results of each contingency are compared with the data from the PMU monitoring system.@en