Power systems are nonlinear with fast changing dynamics. In order to design a nonlinear adaptive controller for damping power system oscillations, it becomes necessary to identify the dynamics of the system. This paper demonstrates the implementation of a neural network based system identifier, referred to as a neuroidentifier, on a programmable logic controller (PLC) platform. Two separate neuroidentifiers are trained using the particle swarm optimization (PSO) algorithm to identify the dynamics in a two-area four machine power system, one neuroidentifier for Area 1 and the other for Area 2. The power system is simulated in real time on the Real Time Digital Simulator (RTDS). The PLC implementing two neural networks and the PSO training algorithm is interfaced in a real time to the RTDS. Typical results are presented showing that PLC platform is able to implement the neuroidentifiers to sufficiently identify the dynamics of the two-area four machine power system.
C. A. Parrott and G. K. Venayagamoorthy, "Implementation of Neuroidentifiers Trained by PSO on a PLC Platform for a Multimachine Power System," Proceedings of the 2008 IEEE Swarm Intelligence Symposium (2008, St. Louis, MO), Institute of Electrical and Electronics Engineers (IEEE), Sep 2008.
The definitive version is available at https://doi.org/10.1109/SIS.2008.4668335
2008 IEEE Swarm Intelligence Symposium (2008, St. Louis, MO)
Electrical and Computer Engineering
National Science Foundation (U.S.)
United States. Department of Education
Keywords and Phrases
Adaptive Control; Control System Synthesis; Learning (Artificial Intelligence); Machine Control; Neurocontrollers; Nonlinear Control Systems; Particle Swarm Optimisation; Power System Control; Power System Simulation; Programmable Controllers
Article - Conference proceedings
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