In this paper, the performance limits of faults localization are investigated using synchrophasor data. The focus is on a non-trivial operating regime where the number of Phasor Measurement Unit (PMU) sensors available is insufficient to have full observability of the grid state. Proposed analysis uses the Kullback Leibler (KL) divergence between different fault location hypotheses, which are associated with the observation model. This analysis shows that the most likely locations are concentrated in clusters of buses more tightly connected to the actual fault site akin to graph communities. Consequently, a PMU placement strategy is derived that achieves a near-optimal resolution for localizing faults for a given number of sensors. The problem is also analyzed from the perspective of sampling a graph signal, and how the placement of the PMUs i.e. the spatial sampling pattern and the topological characteristic of the grid affect the ability to successfully localize faults is studied. To highlight the superior performance of presented fault localization and placement algorithms, the proposed strategy is applied to a modified IEEE 34, IEEE-123 bus test cases and to data from a real distribution grid. Additionally, the detection of cyberphysical attacks is also examined where PMU data and relevant Supervisory Control and Data Acquisition (SCADA) network traffic information are compared to determine if a network breach has affected the integrity of the system information and/or operations [2].
SUMMARYIn this paper, a two-part (i.e., normal part and reserve part) control scheme is presented for a single wind turbine generator and then extended to a set of them called wind power plant. The proposed method can be applied under the normal operation of the system or emergency condition in which the generating power drops, suddenly. In this work, initially, a novel control scheme is proposed for both single wind turbine generator and wind power plant under the normal condition. Then, the reserve part of the scheme is presented with the aim of injecting huge amount of energy to the network, and therefore improving the frequency, after an outage of a generating unit. The proposed scheme is applied to two case studies to validate the performance. Finally, a comprehensive analysis is performed, and the effects of different parameters on the system's frequency behavior are evaluated for different wind speed profiles.
The trustworthiness and security of cyber-physical systems (CPSs), such as the power grid, are of paramount importance to ensure their safe operation, performance, and economic efficiency. The aim of many cyber-physical security techniques, such as network intrusion detection systems (NIDSs) for CPSs, is to ensure continuous reliable operation even in exposed network environments. But the validation of such methods goes well beyond standard network analysis, since meaningful tests must also integrate realistic understanding of the physical systems behavior and response to the network activity. Our goal in this paper is to showcase an example of a testbed environment that can support such validation. In it, real network traffic, emulating and industrial control network, interacts with simulated physical models in real-time, extending and leveraging "hardware-in-the-loop" and "cyber-in-the-loop" capabilities. The testbed is a bridge between theory and practice and offers a number of features, including network communications, data management, as well as the virtualization of cyber-physical state analytics performed by the NIDS. The traffic is captured by real network taps and is forwarded to a real data management environment, receiving also the data reports from the simulated industrial control environment. To illustrate the capabilities of our testbed we show how the data are cross-checked by a "physics aware" NIDS, identifying network traffic that does not comply with its cyber-physical security rules
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