Fault-Tolerant Optimal Neurocontrol for a Static Synchronous Series Compensator Connected to a Power Network

This paper proposes a fault-tolerant optimal neurocontrol scheme (FTONC) for a static synchronous series compensator (SSSC) connected to a multi-machine benchmark power system. The dual heuristic programming technique and the 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 liner controller. A sensor evaluation and (missing sensor) restoration scheme (SERS) is designed by using the auto-associative neural networks and the particle swarm optimization technique. 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 PSCAD/EMTDC environment