Identificación de aumentos inusuales de demanda en redes de distribución eléctrica mediante estimación de estados para la disminución de perdidas

Abstract

CONDITIONING OF PROJECT PUBLICATION. The project focuses on identifying outliers in demand using statistical techniques, concentrating on increasing the operational efficiency of the electrical system and reducing energy losses. The main objective is to generate a technique that will serve for the recognition and detection of unusual increases in demand, thus facilitating the management of electrical energy. This hypothesis posits that the use of state estimation techniques will make the search for unusual increases in demand at demand levels more effective. This study is relevant because monitoring at an appropriate level can help improve the sustainability and stability of the distribution system. Regarding the tools and methodologies used, the Python programming language was employed to model and simulate a two-dimensional distribution system based on the IEEE 13 bus standard for electric distribution networks. A model was developed that allows for state estimation as well as monitoring violations in various power networks. This system was structured into several main components including environment setup, data structure design, BCB state estimator design, integration of the system for detecting unusual demand increases, and simulations based on international standards of electrical quality and safety to ensure the reliability of the findings obtained. In the results, it became clear that the applied methodology successfully allows for the recognition of unusual increases in demand. Likewise, a considerable reduction in system losses was achieved through changes in distribution. In conclusion, the use of state estimation techniques in distribution networks is suitable for energy control activities and loss optimization. This approach promotes greater and more sustainable efficiency in energy distribution. Keywords: state estimation, distribution networks, electrical demand, technical losses.