Efficient Database Generation for Data-driven Security Assessment of Power Systems

Power system security assessment methods require large datasets of operating points to train or test their performance. As historical data often contain limited number of abnormal situations, simulation data are necessary to accurately determine the security boundary. Generating such a database is an extremely demanding task, which becomes intractable even for small system sizes. This paper proposes a modular and highly scalable algorithm for computationally efficient database generation. Using convex relaxation techniques and complex network theory, we discard large infeasible regions and drastically reduce the search space. We explore the remaining space by a highly parallelizable algorithm and substantially decrease computation time. Our method accommodates numerous definitions of power system security. Here we focus on the combination of N-k security and small-signal stability. Demonstrating our algorithm on IEEE 14-bus and NESTA 162-bus systems, we show how it outperforms existing approaches requiring less than 10% of the time other methods require.Comment: Database publicly available at: https://github.com/johnnyDEDK/OPs_Nesta162Bus - Paper accepted for publication at IEEE Transactions on Power System

Interaction of Droop Control Structures and its Inherent Effect on the Power Transfer Limits in Multi-terminal VSC-HVDC

Future multiterminal HVDC systems are expected to utilize dc voltage droop controllers, and several control structures have been proposed in the literature. This paper proposes a methodology to analyze the impact of various types of droop control structures using small-signal stability analysis considering all possible combinations of droop gains. The different control structures are evaluated by the active power transfer capability as a function of the droop gains, considering various possible stability margins. This reveals the flexibility and robustness against active power flow variations, due to disturbances for all of the implementations. A case study analyzing a three-terminal HVDC VSC-based grid with eight different kinds of droop control schemes points out that three control structures outperform the remaining ones. In addition, a multivendor case is considered where the most beneficial combinations of control structures have been combined in order to find the best performing combination

Disturbance Attenuation of DC Voltage Droop Control Structures in a Multi-Terminal HVDC Grid

DC voltage droop control is seen as the preferred control structure for primary voltage control of future multi-terminal HVDC systems. Different droop control structures have been proposed in literature which can be classified in eight categories. This paper contributes to an analysis of the disturbance rejection of these droop control structures. The approach is based on multi-variable frequency response analysis where both ac and dc grid dynamics are incorporated. In particular, the amplification of dc voltage oscillations due to wind power variations is analyzed using singular value analysis. Further, the impact of dc cable modeling on the results is discussed. In addition, it is shown that the maximum singular value limits, frequently used in literature for MIMO-analysis, are not sufficient to prove that the impact of certain disturbances on analyzed outputs is within a certain boundary. It is necessary to verify the results by a multiple input single output analysis of the transfer functions connecting the inputs with the highest amplified output.Postprint (author's final draft

Deep Learning for Power System Security Assessment

Security assessment is among the most fundamental functions of power system operator. The sheer complexity of power systems exceeding a few buses, however, makes it an extremely computationally demanding task. The emergence of deep learning methods that are able to handle immense amounts of data, and infer valuable information appears as a promising alternative. This paper has two main contributions. First, inspired by the remarkable performance of convolutional neural networks for image processing, we represent for the first time power system snapshots as 2-dimensional images, thus taking advantage of the wide range of deep learning methods available for image processing. Second, we train deep neural networks on a large database for the NESTA 162-bus system to assess both N-1 security and small-signal stability. We find that our approach is over 255 times faster than a standard small-signal stability assessment, and it can correctly determine unsafe points with over 99% accuracy.Comment: Accepted at IEEE Powertech 2019, Milan, Ital