Optimization of FACTS devices : classification, recent trends, and future outlook

Since the inception of industrialization, power system has been an indispensable aspect of economy. With the progression of time, technology has impalpably commingled into our lifestyle. Alongside blooming technologies, energy demand is proliferating and power companies are begetting energy at their best to quench it. Growing reliance on power system has brought its quality into more advertence. Various electronic devices and topologies have been invented to enhance power quality and reliability; numerous others are still underway. During the course, power system has grown to an intricate network of sources, loads and control devices, leading to various issues such as transmission congestion and high losses. This paper discusses ways to ameliorate congestion and gives an overview of relationship between our present energy resources and ecological threats like global warming. Moreover, it points out various power system problems such as energy losses and transients. The necessity of FACTS devices has also been elaborated alongside their classification and comparison. Finally, numerous topologies and optimization methods proposed in the technical literature have been classified and analyzed to alleviate power system conundrums, and a glimpse into future energy trends is presented

Ubicación óptima de dispositivos SVC para la mejora del margen de estabilidad de voltaje en sistemas de trasmisión considerando el índice de estabilidad de voltaje L-INDEX

En este documento se presenta una metodología que permite ubicar de forma óptima dispositivos SVC en base dos criterios, la función de costos del dispositivo SVC y el índice de estabilidad L-índex. El primer criterio permite conocer el costo asociado a la instalación de dispositivos SVC en las barras del SEP, mientras que el segundo criterio permite conocer los nodos que se encuentran cerca del colapso de voltaje. Para llevar a cabo un análisis más detallado se procede a dividir el SEP en áreas de control de voltaje. El proceso empieza con el cálculo de la Jacobiana del sistema, posteriormente se calcula y analiza la matriz de sensibilidad para descomponer cada nodo en coordenadas principales. Por último, se aplica el algoritmo de aprendizaje no supervisado K-means para obtener áreas débilmente acopladas entre sí. La metodología propuesta se aplica sobre los modelos de prueba de 30 y 39 nodos de la IEEE. Para validar la metodología se compara los resultados obtenidos en el software GAMS y DIgSILENT. Se demuestra que el perfil de voltaje, comportamiento angular y perdidas de potencia reactiva mejoran de forma sustancial ante la ubicación óptima de dispositivos SVC.This document presents a methodology that allows for optimal placement of SVC devices based on two criteria, the cost function of the SVC device and the Lindex stability index. The first criterion allows to know the cost associated to the installation of SVC devices in the SEP bars, while the second criterion allows to know the nodes that are near the voltage collapse. To carry out a more detailed analysis, the SEP is divided into voltage control areas. The process begins with the calculation of the Jacobian of the system, then the sensitivity matrix is calculated and analyzed to break each node into main coordinates. Finally, the unsupervised learning algorithm K-means is applied to obtain weakly coupled areas. The proposed methodology is applied to the IEEE 30 and 39 node test models. To validate the methodology, the results obtained in the GAMS and DIgSILENT software are compared. It is demonstrated that the voltage profile, angular behavior and reactive power losses are substantially improved in the optimal location of SVC devices