This work presents a novel technique for dynamic optimization of the electric power grid using brain-like stochastic identifiers and controllers called ObjectNets based on neural network architectures with recurrence. ObjectNets are neural network architectures developed to identify/control a particular object with a specific objective in hand. The IEEE 14 bus multimachine power system with a FACTS device is considered in this paper. The paper focuses on the combined minimization of the terminal voltage deviations and speed deviations at the generator terminals and the bus voltage deviation at the point of contact of the FACTS device to the power network. Simulation results are provided for the identifier and controller ObjectNets for the generators and the FACT device.

Meeting Name

39th IAS Annual Meeting of the IEEE Industry Applications Conference, 2004


Electrical and Computer Engineering

Keywords and Phrases

FACTS; IEEE 14 Bus Multimachine Power System; ObjectNets Control; Angular Velocity Control; Dynamic Optimization; Electric Generators; Electric Power Grid; Flexible AC Transmission System; Flexible AC Transmission Systems; Generator; Optimisation; Power Network; Power System Control; Power System Simulation; Recurrent Neural Nets; Recurrent Neural Network Architecture; Speed Deviation Control; Stochastic Identifiers; Voltage Control; Voltage Deviation Control

International Standard Serial Number (ISSN)


Document Type

Article - Conference proceedings

Document Version

Final Version

File Type





© 2004 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

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