Investigation of advanced control for the unified power flow controller (UPFC)

The Unified Power Flow Controller (UPFC) is the versatile FACTS controller that can control up to three transmission system parameters individually or simultaneously in appropriate combinations. The work presented in this thesis is concentrated on the modelling and control of the UPFC. The overall aim is to provide effective tools for optimising the impact of the UPFC in the reinforcement of a transmission system. Existing modelling techniques for the UPFC together with the associated control strategies have been systematically reviewed. An exact power injection model is proposed which is based on the polar representation of the UPFC parameters and includes the reactive power capability of the shunt inverter. In addition, a steady-state model based on an ideal controlled voltage source has been developed using MATLAB/SIMULINK which provides a useful tool to analyse and develop the UPFC control system. The UPFC internal limits have been identified and accordingly, the feasible operating area of a transmission system incorporating a UPFC has been determined based on the UPFC maximum limits. The influence of both the series and shunt inverters on this controlled area has been analysed. The impact of a change in the system short circuit level on the UPFC operation and the size of the feasible area has also been investigated. Three modern controllers have been designed and tested for controlling the UPFC in a power flow mode for the series part and a voltage control mode for the shunt part. These controllers are: a fuzzy knowledge based controller, an artificial neural network based controller and a neuro-fuzzy based controller. For the former, the fuzzy rules are deduced from the relationship between the controlled power system parameters and the UPFC control variables. The second is a simple RBFNN controller which is constructed from a single neuron and trained on-line by a gradient descent algorithm. The third controller is designed using the adaptive capabilities of neural networks to estimate and tune the fuzzy rules. Computer simulation and experimental implementation of a UPFC using DS 1103 data acquisition board have been used to verify the proposed control strategies. In the experimental lab model, two 6-pulse inverters implementing the SPWM technique have been used to realise the UPFC system