General Electric

About: General Electric is a company organization based out in Boston, Massachusetts, United States. It is known for research contribution in the topics: Turbine & Rotor (electric). The organization has 76365 authors who have published 110557 publications receiving 1885108 citations. The organization is also known as: General Electric Company & GE.

Hybrid Design of Modular Multilevel Converters for HVDC Systems Based on Various Submodule Circuits

Abstract: The modular multilevel converter (MMC) has become the most promising converter technology for high-voltage direct current (HVDC) transmission systems. However, similar to any other voltage-sourced converter-based HVDC system, MMC-HVDC systems with the half-bridge submodules (SMs) lack the capability of handling dc-side short-circuit faults, which are of severe concern for overhead transmission lines. In this paper, two new SM circuit configurations as well as a hybrid design methodology to embed the dc-fault-handling capability in the MMC-HVDC systems are proposed. By combining the features of various SM configurations, the dc-fault current path through the freewheeling diodes is eliminated and the dc-fault current is enforced to zero. Several MMC configurations based on the proposed hybrid design method and various SM circuits, that is, the half-bridge, the full-bridge, the clamp-double, and the five-level cross-connected SMs, as well as the newly proposed unipolar-voltage full-bridge and three-level cross-connected SMs, are investigated and compared in terms of the dc-fault-handing capability, semiconductor power losses, and component requirements. The studies are carried out based on time-domain simulation in the PSCAD/EMTDC software environment for various SM configurations and dc-fault conditions. The reported study results demonstrate the proposed hybrid-designed MMC-HVDC system based on the combination of the half-bridge and the proposed SM circuits is the optimal design among all evaluated systems in terms of the dc-fault-handing capability, semiconductor power losses, and component requirements.

System and method for performing risk and credit analysis of financial service applications

Abstract: The present invention discloses a method and system for performing risk and credit analysis of financial service applications with a neural network. The neural network imitates and perfects a credit manager's evaluation and decision process to control loss and guide business expansion. In particular, the neural network screens applications to control loss and to find directions where business volume can increase with a minimum increase in loss. Initially, data variables are pre-processed and applied to the neural network. The neural network in the present invention is optimized by a non-iterative regression process, as opposed to the computationally intensive back propagation algorithm.