This paper presents an artificial neural network (ANN) based approach to tune the parameters of the SVC reactive power controller over a wide range of typical load model parameters. The gains of PI (proportional integral) based reactive power controller are optimised for typical values of the load voltage characteristics by conventional techniques. Using the generated data, the method of multilayer feed-forward ANN with the error back-propagation training is employed. An ANN tuned static var compensator (SVC) controller has been applied to control the reactive power of variable slip/speed model of isolated wind-diesel hybrid power system. Transient responses of sample hybrid power system have also been presented.

Reactive power control of autonomous wind-diesel hybrid power systems using ANN

Chaotic ant swarm optimization for fuzzy-based tuning of power system stabilizer

A nonlinear coordinated generator excitation, static phase shifter (SPS) and static VAr compensator (SVC) controller is proposed to enhance the transient stability of a power system. The proposed controller is novel since it is able to control the three main parameters affecting AC power transmission-voltage, phase angle and reactance-in a coordinated manner. The SPS is located near the generator terminal and the SVCs are located at the midpoint of each transmission line in a power system. a nonlinear feedback control law is found which linearizes and decouples the power system. The coordinated controller consists of three separate controllers: an adaptive excitation and SPS controller; and two SVC controllers. These controllers are designed based on local measurements only. The results show that the proposed nonlinear coordinated series and shunt FACTS controller is able to enhance the transient stability of a single-machine-infinite-bus (SMIB) power system.

Design of series and shunt FACTS controller using adaptive nonlinear coordinated design techniques

Robust nonlinear coordinated generator excitation and SVC control for power systems

Tuning of SVC damping controllers over a wide range of load models using an artificial neural network

Design of a static voltampere reactive (VAr) controller (SVC) using a novel adaptive scheme is investigated. The proposed controller, which makes use of self-tuning proportional-integral-derivative (PID) control, can provide better damping characteristics than a fixed-gain PID control SVC over a wide range of operating conditions. The system considered is a synchronous generator connected to an infinite bus through a double circuit transmission line. Digital simulations of the system following a three-phase fault under different loading conditions were performed to demonstrate the robustness and effectiveness of the controller. >

Chin-Hsing Cheng

Papers

Damping of generator oscillations using an adaptive static VAR compensator