# Smart Grid

This study is utilized for submodule open-circuit fault detection uncertainty analysis of modular multilevel converters. The dataset consists of 8 uncertainty factors and 15 system variables under four operation scenarios. The 1000 sets of uncertainty factor samples are generated randomly as initial configuration of the system. The 15 system variables are obtained by 1000 Monte Carlo simulations. We found that there are 153 residual samples exceeded the threshold of 0.8, which indicated a high false alarm rate.

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Anomaly detection in Phasor Measurement Unit (PMU) data requires high-quality, realistic labeled datasets for algorithm training and validation. Obtaining real field labelled data is challenging due to privacy, security concerns, and the rarity of certain anomalies, making a robust testbed indispensable. This paper presents the development and implementation of a Hardware-in-the-Loop (HIL) Synchrophasor Testbed designed for realistic data generation for testing and validating PMU anomaly detection algorithms.

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The Illinois 200-Bus Cyber-Physical System (CPS) serves as an important case for studying risk analysis through the integration of physical and cyber components. This system designed to reflect real-world architectures. Access Complexity scores are assigned to its network edges based on the Common Vulnerability Scoring System (CVSS) Access Complexity (AC), allowing for the simulation of adversarial pathways within the system.

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Smart grids are nowadays featured by distributed energy resources, both renewables, traditional sources and storage systems. Generally, these components are characterized by different control technologies that interact with the generators through smart inverters. This exposes them to a variety of cyber threats. In this context, there is a need to develop datasets of attacks on these systems, in order to evaluate the risks and allow researchers to develop proper monitoring algorithms.

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It proposes a lossless and stateless compression method for IEC 61850 Sampled Values flows. A set of bitmaps is introduced to indicate the size in bytes of the sampled currents and voltages, and to flag the presence or absence of the Quality field. The method has been evaluated for different profiles, and it can provide bandwidth savings between 30 and 52%, with a very low computational cost as a counterpart. It has been implemented and tested in two different hardware platforms, with traffic generated by a Merging Unit.

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The Kirk circle is a simple and effective method for representing power graphs and visualizing their topology. In general, nodes (buses) in an electrical network are numbered with neighboring nodes assigned consecutive or closely proximal numbers. This allows for sequential mapping of these nodes in increasing order of their numerical labels to evenly spread points on a Kirk circle. In the Kirk circle, the edge connections (branches) between nodes are indicated by straight lines (chords) between the appropriate points on the circle.

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ER-SPG is a Matlab code for producing synthetic power graphs using well-known Erdos-Renyi Random Model. It scales power graphs and achieves connectivity in each scale by different approach, and accordingly connected graphs with average degree between 2 to 5 (normally between 2.3 to 3.1) can be produced by ER_SPG with the structures similar to power graphs. It also reorders the graph vertices to obtain consecutive numbering similar to power graphs. This algorithm is also provides locations of zero injection buses (ZIBs) as operational data of power graphs.

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The Integrated Energy Management and Forecasting Dataset is a comprehensive data collection specifically designed for advanced algorithmic modeling in energy management. It combines two distinct yet complementary datasets - the Energy Forecasting Data and the Energy Grid Status Data - each tailored for different but related purposes in the energy sector.

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This dataset proposes a new method of modelling dynamic loads based on instantaneous p-q theory, to be employed in large power system networks in a digital real time environment. In order to decrease the computational burden associated to the dynamic load modelling, a p-q- theory-based approach for load modelling is proposed in this dataset. This approach is based on the well-known p-q- instantaneous theory developed for power electronics converters, and it consts only of linear controllers and of a minimal usage of control loops, reducing the required computational power.

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