Dynamic line rating state estimation

Nowadays, the assets of power systems face economic, social, technical and environmental challenges. These challenges arise as a consequence of energy system dynamics that need to increase their reliability and safety as well as to incorporate new sources of generation and loads. This requires loading transformers, substation equipment and transmission lines (OHLs) close to their operating limits. In addition, in most countries, much of these elements are reaching the end of their useful life, for these reasons, it is necessary to design and build new assets with high and long investments. Given that scenario, new technologies have been developed, in order to optimize these assets. Within these technologies, it is the real-time monitoring of OHL rating (DLR), which can increase the conductor ampacity between 10 and 30\% in critical lines (bottlenecks), especially when there is high penetration of renewable generation. To summarize, DLR seeks to estimate and predict the temperature in conductors used in OHLs in order to optimize control and operation of the system, and to increase the reliability during contingencies. Given DLR benefits, methodologies for direct and indirect measurement have been developed for real-time monitoring. The indirect measurements are based on weather models and/or weather measurements in the influence area of the OHL, obtaining an overview of the atmospheric conditions without requiring intervention on the line. On the other hand, with direct measurements thermal, mechanical, and geometric variables of a given span are monitored. Due to the complexity of sensing all spans in an OHL, only spans that restrict power flows (critical spans) are monitored, assuming risks in the spans that are not being monitored. The use of direct measurements has increased because they have greater precision in the calculation of conductor ampacity. In this research, taking advantages of indirect and direct methods, a methodology to estimate and predict temperature is proposed. The goal is to increase the reliability of OHLs thermal monitoring systems. The proposed methodology consists of two stages. In the first one, the conductor temperature is estimated in all spans of an OHL assuming thermal equilibrium. For this estimation, an algorithm based on weighted least square (WLS) was developed, by means of which the best estimates of temperature are obtained, allowing to identify critical spans. Subsequently, the second stage consists in estimating and predicting the temperature in critical spans during a thermal transient. For this, an algorithm based on an extended Kalman filter (EKF) was developed. Additionally, with the EKF is estimated the wind speed and the thermal parameters of the conductor in order to reach improvements in temperature prediction. Finally, the algorithms were evaluated through simulations and experiments. As a result, when the temperature was estimated and predicted with the developed algorithms, the errors and residuals were lower than when the temperature was computed with direct and/or indirect measurements.

• Autores: Álvarez Álvarez, David Leonardo
• Tipo de recurso: Doctoral thesis
• Fecha de publicación: 2017
• Institución: Universidad Nacional de Colombia
• Repositorio: Universidad Nacional de Colombia
• Idioma: spa
• OAI Identifier: oai:repositorio.unal.edu.co:unal/62198
• Acceso en línea: https://repositorio.unal.edu.co/handle/unal/62198; http://bdigital.unal.edu.co/61156/
• Palabra clave: 6 Tecnología (ciencias aplicadas) / Technology; 62 Ingeniería y operaciones afines / Engineering; Dynamic Line Rating (DLR); Extend Kalman Filter (EKF); Overhead Lines (OHL); State Estimation; Weather Forecast; Weighted Least Square (WLS)
• Rights: openAccess
• License: Atribución-NoComercial 4.0 Internacional