Showing 2 results for Sssc
A. Kazemi, A. Badri, S. Jadid,
Volume 1, Issue 4 (10-2005)
Abstract
In this paper, two vector control systems for investigating the performance of
Static Synchronous Series Compensators (SSSC) in steady state conditions are presented
that are based on famous d-q axis theory. The workability of proposed method to simplify
the SSSC mathematical expressions is shown. The performance of SSSC with two different
vector controllers, first based on d-q line currents(indirect control) and the second a
heuristic vector control based on real and reactive line powers (direct control), are
investigated through simulation. It is found that the new introduced direct control produces
better performance in controlling AC power system. Finally the simulation results of an
elementary two-machine system with SSSC in different cases are investigated.
A. Khoshsaadat , M. R. Mosavi, J. S. Moghani,
Volume 10, Issue 3 (9-2014)
Abstract
Static Synchronous Series Compensator (SSSC) is a series compensating Flexible AC Transmission System (FACTS) controller for maintaining to the power flow control on a transmission line by injecting a voltage in quadrature with the line current and in series mode with the line. In this work, an Adaptive Network-based Fuzzy Inference System controller (ANFISC) has been proposed for controlling of the SSSC-based damping system and applied to a Single Machine Infinite Bus (SMIB) power system. For implementation of the learning process in this controller, we use of the one approach of the learning ability that named as Forward Signal and Backward Error Back-Propagation (FSBEBP) method for improving of the system efficiency. This artificial intelligence-based control model leads to a controller with adaptive structure, improved correctness, high damping ability and dynamic performance. System implementation is easy and it requires 49 fuzzy rules for inference engine of the system. As compared with the other complex neuro-fuzzy systems, this controller has medium number of the fuzzy rules and low number of layers, but it has high accuracy. In order to demonstrate of the proposed controller ability, it is simulated and its output compared with that of classic Lead-Lag-based Controller (LLC) and PI controller.
