Estimación del modelo dinámico de centrales eléctricas mediante filtros de Kalman escalonados

Este trabajo presenta una novedosa técnica de estimación de parámetros para sistemas de generación que incluyen el par turbina-generador, junto con los controladores ordinarios, véase, el Turbine Governor, el Automatic Voltage Regulator y el Power System Stabilizer. Esta técnica propuesta se basa en el Unscented Kalman Filter para realizar una estimación conjunta del estado dinámico del sistema y un conjunto de parámetros modificados con los cuales se pueden calcular los parámetros originales del modelo. A opinión personal del autor, este trabajo es un primer intento a la hora de estimar un conjunta tan completo de variables dinámicas y parámetros, usando exclusivamente medidas externas, tomadas en los terminales de la máquina. Este Trabajo Fin de Máster supone una gran mejora con respecto a los resultados obtenidos por el autor en su Trabajo Fin de Grado, conduciendo estas modificaciones a la publicación del correspondiente artículo científico.This project presents a parameter estimation technique for generation sets including the synchronous machine-turbine pair, along with the customary controllers: turbine governor, automatic voltage regulator and power system stabilizer. The proposed technique is based on the Unscented Kalman Filter for the joint estimation of the system dynamic state and a modified set of parameters from which the actual model parameters and constants can be computed. To the author’s knowledge, this work is the first attempt to estimate such a full set of dynamic state variables and parameters, using just external measurements taken at the generator terminal bus. The project itself supposes a great improvement respecting the author’s final degree project. These modifications have driven to the publication of the corresponding paper.Universidad de Sevilla. Máster en Sistemas de Energía Eléctric

Estimación de parámetros en aerogeneradores síncronos regulados

En este trabajo de investigación se tiene como objetivo principal realizar una estimación lo más precisa posible de los parámetros que definen la dinámica interna de un aerogenerador síncrono, así como las constantes propias del convertidor en fuente de tensión acoplado al mismo. Para la estimación se va a utilizar el filtrado de Kalman, en concreto su formulación Cubature Kalman Filter, cuyo algoritmo se ha escrito en código Matlab y que utilizará una serie de mediciones ruidosas procedentes de una simulación efectuada en MATLAB Simulink de una turbina eólica integrada en un sistema eléctrico de potencia sencillo.The main purpose of this research is estimating accurately the parameters involved in the dynamic equations of a eolic generation set jointly with the parameters of the coupled voltage source converter. The estimation technique proposed is base don Kalman filters, particularly in the Cubature Kalman Filter formulation, whose algorithm has been implemented in MATLAB. The input signals considered are noisy measurements obtained from a simulation carried out using MATLAB Simulink, representing an eolic turbine integrated in a simple electric power system.Universidad de Sevilla. Máster en Ingeniería Industria

Modelado y análisis de la evolución de una epidemia vírica mediante filtros de Kalman: el caso del COVID-19 en España

Documento de trabajo. Difundido en idUS a solicitud de los autoresEste trabajo presenta una metodología original para el tratamiento de los datos reportados de positivos y fallecidos por una epidemia vírica. El objetivo principal es caracterizar la evolución de la progresión del número de infectados reales, y en consecuencia poder predecir en qué momento se alcanzará el pico de la epidemia en un caso de estudio concreto, en este caso la del Covid-19 en España. Los resultados obtenidos muestran claramente el efecto beneficioso de las medidas de confinamiento adoptadas, y prevén que el pico se producirá aproximadamente a finales de marzo o principios de abri

Identification of the phase connectivity in distribution systems through constrained least squares and confidence-based sequential assignment

This paper addresses the customer-phase identification problem in three-phase distribution grids including three-phase customers characterized by aggregated energy measurements. The proposed technique first solves a relaxed problem, in which the binary nature of the variables is ignored, which leads to a constrained, least-squares estimation, using as inputs the active and reactive energy readings provided by the smart meters, along with the energy delivered by each phase at the head of the feeder. With the estimated values of the decision variables, and their corresponding variances, a confidence-based selection technique is then applied for the sequential assignment of the customer with the highest joint probability of being connected to one of the three phases but not to the other two. The performance of the proposed procedure is assessed with five different scenarios in terms of accuracy for increasing number of loads and measurement errors. The robustness of the algorithm is additionally tested in the presence of model errors, and its performance is compared to that of existing methods.Project Solar to Vehicle (S2V) INV-3-2021-I-038Research project HySGrid+ CER-2019101

A fitting procedure for probability density functions of service restoration times. Application to underground cables in medium-voltage networks

Distribution companies have the responsibility to provide a quality service to their customers, according to the existing regulation. Reliability issues, such as power outages, are registered in databases for a quantitative evaluation of this quality. This paper uses one of these historical records to make a statistical analysis of service restoration times, applied to the particular case of underground cables in medium voltage networks. An algo-rithm is proposed to fit the raw data to the probability density functions typically used in reliability analysis. The best-fitted distribution is determined in each case according to the information provided by a set of goodness-of -fit tests. Different groups are considered for the elements of the systems, concerning their functionality and voltage level. The presented procedure is applied to an electrical network with more than 350 feeders. Results have been obtained globally, showing that the observed service restoration time is lower than the estimated maximum limit in 98.00% of cases. The probability functions provided by the proposed algorithm can be used to improve the accuracy of the reliability models for the electric power system.8 página

Influence of the wind variability on the calculation of dynamic line rating

This paper presents a novel technique to calculate the ampacity of an overhead transmission line in real time, considering the dynamic evolution of the conductor temperature. In case this method cannot be applied due to lack of adequate information, a correction is proposed for the maximum current, based on Monte Carlo simulations assuming unfavorable external conditions, and validated with real data from a weather station. This technique might be used to avoid the temporary violation of the minimum electrical clearance in transmission lines.Research project HySGrid+ CER-2019101

Application of Kalman filter based estimation techniques to electric power systems

This thesis presents several applications of dynamic state estimators based on Kalman filtering to different fields of the electric power systems. First, a parameter estimation technique is proposed, applied to a generation set composed by the synchronous machine along with the frequency regulation (speed governor) and the voltage controllers (automatic voltage regulator and power system stabilizer). The proposed method is based on a formulation of the unscented Kalman filter, being this study the first attempt, to the authors’ knowledge, to include the full generation set in the estimator model, with the corresponding state variables and parameters, using just external measurements taken at the generator terminal bus. A similar estimation technique, using the cubature Kalman filter, is implemented subsequently for a joint estimation of the dynamic state and the model parameters of a variable speed wind turbine with permanent magnet synchronous generator and back to back voltage source converter. In this case, the major contribution consists of the inclusion of the control parameters in the state vector to be estimated. Finally, three Kalman filter formulations (unscented Kalman filter, cubature Kalman filter and ensemble Kalman filter) are implemented to address the problem of identifying the electrical phase of single phase consumers in distribution grids, using for this purpose hourly energy measurements exclusively. The accuracy and robustness of these estimators are compared in different case studies with variations in the number of loads and errors in the measurements and the considered model

Monitoring and Tracking the Evolution of a Viral Epidemic Through Nonlinear Kalman Filtering: Application to the COVID-19 Case

This work presents a novel methodology for systematically processing the time series that report the number of positive, recovered and deceased cases from a viral epidemic, such as Covid-19. The main objective is to unveil the evolution of the number of real infected people, and consequently to predict the peak of the epidemic and subsequent evolution. For this purpose, an original nonlinear model relating the raw data with the time-varying geometric ratio of infected people is elaborated, and a Kalman Filter is used to estimate the involved state variables. A hypothetical simulated case is used to show the adequacy and limitations of the proposed method. Then, several countries, including China, South Korea, Italy, Spain, U.K. and the USA, are tested to illustrate its behavior when reallife data are processed. The results obtained clearly show the beneficial effect of the severe lockdowns imposed by many countries worldwide, but also that the softer social distancing measures adopted afterwards have been almost always insufficient to prevent the subsequent virus waves

A fitting procedure for probability density functions of service restoration times. Application to underground cables in medium-voltage networks

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).Distribution companies have the responsibility to provide a quality service to their customers, according to the existing regulation. Reliability issues, such as power outages, are registered in databases for a quantitative evaluation of this quality. This paper uses one of these historical records to make a statistical analysis of service restoration times, applied to the particular case of underground cables in medium voltage networks. An algorithm is proposed to fit the raw data to the probability density functions typically used in reliability analysis. The best-fitted distribution is determined in each case according to the information provided by a set of goodness-of-fit tests. Different groups are considered for the elements of the systems, concerning their functionality and voltage level. The presented procedure is applied to an electrical network with more than 350 feeders. Results have been obtained globally, showing that the observed service restoration time is lower than the estimated maximum limit in 98.00% of cases. The probability functions provided by the proposed algorithm can be used to improve the accuracy of the reliability models for the electric power system

A Thermal Model for Three-Core Armored Submarine Cables Based on Distributed Temperature Sensing

This paper presents a procedure for the derivation of an equivalent thermal network-based model applied to three-core armored submarine cables. The heat losses of the different metallic cable parts are represented as a function of the corresponding temperatures and the conductor current, using a curve-fitting technique. The model was applied to two cables with different filler designs, supposed to be equipped with distributed temperature sensing (DTS) and the optical fiber location in the equivalent circuit was adjusted so that the conductor temperature could be accurately estimated using the sensor measurements. The accuracy of the proposed model was tested for both stationary and dynamic loading conditions, with the corresponding simulations carried out using a hybrid 2D-thermal/3D-electromagnetic model and the finite element method for the numerical resolution. Mean relative errors between 1 and 3% were obtained using an actual current profile. The presented procedure can be used by cable manufacturers or by utilities to properly evaluate the cable thermal situation.FEDER/Ministerio de Ciencia e Innovación—Agencia Estatal de Investigación ENE2017-89669-RUniversidad de Sevilla (VI PPIT-US) 2018/0000074