This paper proposes a novel fault-tolerant optimal neurocontrol scheme (FTONC) for a static synchronous series compensator (SSSC) connected to a multimachine benchmark power system. The dual heuristic programming technique and radial basis function neural networks are used to design a nonlinear optimal neurocontroller (NONC) for the external control of the SSSC. Compared to the conventional external linear controller, the NONC improves the damping performance of the SSSC. The internal control of the SSSC is achieved by a conventional linear controller. A sensor evaluation and (missing sensor) restoration scheme (SERS) is designed by using the autoassociative neural networks and particle swarm optimization. This SERS provides a set of fault-tolerant measurements to the SSSC controllers, and therefore, guarantees a fault-tolerant control for the SSSC. The proposed FTONC is verified by simulation studies in the PSCAD/EMTDC environment.
W. Qiao et al., "Fault-Tolerant Optimal Neurocontrol for a Static Synchronous Series Compensator Connected to a Power Network," IEEE Transactions on Industry Applications, Institute of Electrical and Electronics Engineers (IEEE), Feb 2008.
The definitive version is available at http://dx.doi.org/10.1109/TIA.2007.912730
Electrical and Computer Engineering
Keywords and Phrases
Dual Heuristic Programming (DHP); Fault-Tolerant Optimal Neurocontrol; Missing Sensor Restoration (MSR); Particle Swarm Optimization (PSO); Radial Basis Funtion Network; Static Synchronous Series Compensator (SSSC)
Article - Journal
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