Artificial neural network application to power system voltage stability improvement

As power systems grow in their size and interconnections, their complexity increases. Rising costs due to inflation and increased environmental concerns has made transmission, as well as generation systems be operated closer to design limits. Hence power system voltage stability and voltage control are emerging as major problems in the day-to-day operation of stressed power systems. For secure operation and control of power systems under normal and contingency conditions it is essential to provide solutions in real time to the operator in energy control center (ECC). Artificial neural networks (ANN) are emerging as an artificial intelligence tool, which give fast, though approximate, but acceptable solutions in real time as they mostly use the parallel processing technique for computation. The solutions thus obtained can be used as a guide by the operator in ECC for power system control. This paper deals with development of an ANN architecture, which provide solutions for monitoring, and control of voltage stability in the day-to-day operation of power systems.

Reactive power optimization with different objectives in large power systems including HVDC systems and FACTS devices

Present day power systems are growing in size and complexity of operation with inter connections to neighboring systems, introduction of large generating units, EHV 400/765 kV AC transmission systems, HVDC systems and more sophisticated control devices such as FACTS. For planning and operational studies, it requires suitable modeling of all components in the power system, as the number of HVDC systems and FACTS devices of different type are incorporated in the system. This paper presents reactive power optimization with three objectives to minimize the sum of the squares of the voltage deviations (ve) of the load buses, minimization of sum of squares of voltage stability L-indices of load buses (¿L2), and also the system real power loss (Ploss) minimization. The proposed methods have been tested on typical sample system. Results for Indian 96-bus equivalent system including HVDC terminal and UPFC under normal and contingency conditions are presented.

Regioselective Deiodination of Thyroxine by Iodothyronine Deiodinase Mimics: An Unusual Mechanistic Pathway Involving Cooperative Chalcogen and Halogen Bonding

Iodothyronine deiodinases (IDs) are mammalian selenoenzymes that catalyze the conversion of thyroxine (T4) to 3,5,3'-triiodothyronine (T3) and 3,3',5'-triiodothyronine (rT3) by the outer- and inner-ring deiodination pathways, respectively. These enzymes also catalyze further deiodination of T3 and rT3 to produce a variety of di- and monoiodo derivatives. In this paper, the deiodinase activity of a series of pen-substituted naphthalenes having different amino groups is described. These compounds remove iodine selectively from the inner-ring of T4 and T3 to produce rT3 and 3,3'-diiodothyronine (3,3'-T2), respectively. The naphthyl-based compounds having two selenols in the pen-positions exhibit much higher deiodinase activity than those having two thiols or a thiol selenol pair. Mechanistic investigations reveal that the formation of a halogen bond between the iodine and chalcogen (S or Se) and the pen-interaction between two chalcogen atoms (chalcogen bond) are important for the deiodination reactions. Although the formation of a halogen bond leads to elongation of the C-I bond, the chalcogen bond facilitates the transfer of more electron density to the C-I sigma* orbitals, leading to a complete cleavage of the C-I bond. The higher activity of amino-substituted selenium compounds can be ascribed to the deprotonation of thiol/selenol moiety by the amino group, which not only increases the strength of halogen bond but also facilitates the chalcogen chalcogen interactions.