Systematic Topology Analysis and Generation Using Degree Correlations

We present a new, systematic approach for analyzing network topologies. We first introduce the dK-series of probability distributions specifying all degree correlations within d-sized subgraphs of a given graph G. Increasing values of d capture progressively more properties of G at the cost of more complex representation of the probability distribution. Using this series, we can quantitatively measure the distance between two graphs and construct random graphs that accurately reproduce virtually all metrics proposed in the literature. The nature of the dK-series implies that it will also capture any future metrics that may be proposed. Using our approach, we construct graphs for d=0,1,2,3 and demonstrate that these graphs reproduce, with increasing accuracy, important properties of measured and modeled Internet topologies. We find that the d=2 case is sufficient for most practical purposes, while d=3 essentially reconstructs the Internet AS- and router-level topologies exactly. We hope that a systematic method to analyze and synthesize topologies offers a significant improvement to the set of tools available to network topology and protocol researchers.Comment: Final versio

Topological changes in data-driven dynamic security assessment for power system control

The integration of renewable energy sources into the power system requires new operating paradigms. The higher uncertainty in generation and demand makes the operations much more dynamic than in the past. Novel operating approaches that consider these new dynamics are needed to operate the system close to its physical limits and fully utilise the existing grid assets. Otherwise, expensive investments in redundant grid infrastructure become necessary. This thesis reviews the key role of digitalisation in the shift toward a decarbonised and decentralised power system. Algorithms based on advanced data analytic techniques and machine learning are investigated to operate the system assets at the full capacity while continuously assessing and controlling security. The impact of topological changes on the performance of these data-driven approaches is studied and algorithms to mitigate this impact are proposed. The relevance of this study resides in the increasingly higher frequency of topological changes in modern power systems and in the need to improve the reliability of digitalised approaches against such changes to reduce the risks of relying on them. A novel physics-informed approach to select the most relevant variables (or features) to the dynamic security of the system is first proposed and then used in two different three-stages workflows. In the first workflow, the proposed feature selection approach allows to train classification models from machine learning (or classifiers) close to real-time operation improving their accuracy and robustness against uncertainty. In the second workflow, the selected features are used to define a new metric to detect high-impact topological changes and train new classifiers in response to such changes. Subsequently, the potential of corrective control for a dynamically secure operation is investigated. By using a neural network to learn the safety certificates for the post-fault system, the corrective control is combined with preventive control strategies to maintain the system security and at the same time reduce operational costs and carbon emissions. Finally, exemplary changes in assumptions for data-driven dynamic security assessment when moving from high inertia to low inertia systems are questioned, confirming that using machine learning based models will make significantly more sense in future systems. Future research directions in terms of data generation and model reliability of advanced digitalised approaches for dynamic security assessment and control are finally indicated.Open Acces

Improving water network management by efficient division into supply clusters

El agua es un recurso escaso que, como tal, debe ser gestionado de manera eficiente. Así, uno de los propósitos de dicha gestión debiera ser la reducción de pérdidas de agua y la mejora del funcionamiento del abastecimiento. Para ello, es necesario crear un marco de trabajo basado en un conocimiento profundo de la redes de distribución. En los casos reales, llegar a este conocimiento es una tarea compleja debido a que estos sistemas pueden estar formados por miles de nodos de consumo, interconectados entre sí también por miles de tuberías y sus correspondientes elementos de alimentación. La mayoría de las veces, esas redes no son el producto de un solo proceso de diseño, sino la consecuencia de años de historia que han dado respuesta a demandas de agua continuamente crecientes con el tiempo. La división de la red en lo que denominaremos clusters de abastecimiento, permite la obtención del conocimiento hidráulico adecuado para planificar y operar las tareas de gestión oportunas, que garanticen el abastecimiento al consumidor final. Esta partición divide las redes de distribución en pequeñas sub-redes, que son virtualmente independientes y están alimentadas por un número prefijado de fuentes. Esta tesis propone un marco de trabajo adecuado en el establecimiento de vías eficientes tanto para dividir la red de abastecimiento en sectores, como para desarrollar nuevas actividades de gestión, aprovechando esta estructura dividida. La propuesta de desarrollo de cada una de estas tareas será mediante el uso de métodos kernel y sistemas multi-agente. El spectral clustering y el aprendizaje semi-supervisado se mostrarán como métodos con buen comportamiento en el paradigma de encontrar una red sectorizada que necesite usar el número mínimo de válvulas de corte. No obstante, sus algoritmos se vuelven lentos (a veces infactibles) dividiendo una red de abastecimiento grande.Herrera Fernández, AM. (2011). Improving water network management by efficient division into supply clusters [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/11233Palanci