Ubicación óptima de PMUS basado en criterios de observabilidad y evaluación mediante búsqueda tabú /

The steady growth of electricity networks has made it imperative to use new technologies to improve the reliability of the electrical system, Phasor Measurement Units (PMUs) are devices advanced metering suited to provide relevant information, such information improves safety and reliability system, however one of the crucial points is to determine the optimal location for a complete observability of the system, minimizing the total number of PMU's installed, thus achieving minimize the use of server space. In the present work is to determine the optimal location of the PMU's using an meta-heuristic called tabu search, it will be find the best solution to the problem, meeting all the parameters and constraints of the proposed objective function. For the modeling of the problem is taken as examples of test models IEEE 14, 30 and 118 bars, subsequently the analysis of results of each system is performed and compared with other optimization algorithms such as genetic algorithm, modified harmony search and optimization by swarm of binary particles.El constante crecimiento de las redes eléctricas ha hecho que sea imprescindible utilizar nuevas tecnologías capaces de mejorar la confiablidad del sistema eléctrico, las unidades de medición fasorial (PMU) son dispositivos de medición avanzada idóneos para proporcionar la información pertinente, dicha información mejora la seguridad y confiablidad del sistema, sin embargo uno de los puntos cruciales es determinar la ubicación óptima de las PMU’s para una completa observabilidad del sistema minimizando el número total de PMU’s instaladas, logrando además minimizar el uso del espacio en los servidores. En el presente trabajo se busca determinar la ubicación óptima de las PMU’s mediante el uso de una meta-heurística denominada búsqueda tabú, la misma se encargará de hallar la mejor solución al problema, cumpliendo con todas las restricciones de la función objetivo planteada. Para el modelamiento del problema se toma como ejemplos los modelos de prueba de la IEEE de 14, 30 y 118 barras, posteriormente se realiza el análisis de resultados de cada sistema y se los compara con otros algoritmos de optimización como: algoritmo genético, búsqueda armónica modificada y optimización por enjambre de partículas binarias

Identification of Critical Locations and Reduced Model State Estimation for Power System Analysis

In order to reduce the carbon footprint and the cost of electric energy, the owners of electric power utilities today are faced with the task of reducing the use of expensive and carbon intensive fossil fuels and significantly increasing the amount of energy from renewable sources in their grids while meeting an increase in electricity demand. To deal with increase in demand, electric utilities operate very close to their maximum capacities and this sometimes results in violating security limits. Therefore, the integration of intermittent renewable energy into the utility grids poses serious concerns that must be addressed to ensure grid stability. In order to improve monitoring of their system, utilities are increasing the number of measurement devices in the system. However, not all collectible data contain important, necessary or unique information about the system, so storing and analyzing them comes at a considerable financial cost to the company. Therefore, identifying parts of the system whose measurements provide information that reflects the general state of the system would help utilities smartly utilize resources. In this dissertation, a methodology for the identification of critical variables of power systems and their locations using eigenvalue analysis of the measurements of the system variables is developed. This analysis is based on principal component analysis (PCA). The effectiveness of monitoring critical locations of a power system in ensuring steady state system security is demonstrated. Also, an artificial neural network-based state estimator that utilizes data from regular measurement units and phasor measurement units (PMUs) placed at the critical locations is developed. A technique called state estimation is used to estimate measured and unmeasured electrical quantities. Conventional state estimation techniques require availability of many measurements. The proposed state estimator utilizes fewer measurements, leading to a reduction in the number of expensive PMUs needed and reduction in the cost of electric grid operation. Thus, electric power utilities would be able to assess the state of their grid efficiently and improve their ability to integrate renewable energy without violating the grid’s security constraints

Low Latency Intrusion Detection in Smart Grids

The transformation of traditional power grids into smart grids has seen more new technologies such as communication networks and smart meters (sensors) being integrated into the physical infrastructure of the power grids. However, these technologies pose new vulnerabilities to the cybersecurity of power grids as malicious attacks can be launched by adversaries to attack the smart meters and modify the measurement data collected by these meters. If not timely detected and removed, these attacks may lead to inaccurate system state estimation, which is critical to the system operators for control decisions such as economic dispatch and other related functions. This dissertation studies the challenges associated with cyberattacks in power grids and develops solutions to effectively and timely detect these attacks to ensure an accurate state estimation. One of the common approaches to improving the state estimation accuracy is to incorporate phasor measurement unit (PMU) devices into the system to provide extra and more secure measurements. In this research, we design algorithms that place PMUs at strategic locations to enhance the system\u27s state estimation accuracy and its capability to detect cyberattacks. This approach of installing PMU devices in power grids, nonetheless, does not guarantee a timely attack detection that is critical for a timely deployment of countermeasures to prevent catastrophic impacts from the attacks. Thus, the low latency intrusion detection problem is studied to reduce attack detection delays. The state estimation and intrusion detection problem is further extended to a dynamic power system, where there are sudden changes in system loads