SOURCE:

Faculty: Engineering
Department: Electrical Engineering

CONTRIBUTORS:

Okolo, C.C;
Ezechukwu, O. A;

ABSTRACT:

Enhancement of the dynamic response of generators, within a power system, when subjected to various disturbances, has been a major challenge to power system researchers and engineers for the past decades. This work presents the application of intelligent Voltage Source Converter – High Voltage Direct Current(VSC-HVDC) for improvement of the transient stability of the Nigerian 330kV transmission system. The system was modeled in Power System Analysis Toolbox (PSAT) environment and the system load flow test was done. The eigenvalue analysis of the system buses was performed to determine the critical buses. A balanced three-phase fault was then applied to some of these critical buses and lines of the transmission network in other establish the existing transient stability situation of the grid through the observation of the dynamic responses of the generators in the case network when the fault was applied. To this effect, VSC-HVDC was installed along to those critical lines. The inverter and the converter parameters of the HVDC were controlled by the conventional proportional integral (PI) method and artificial neural network. The generalized swing equations for a multi-machine power system are presented. MATLAB/PSAT software was employed as the tool for the simulations. . This shows clearly that there exist three most critical buses which are Makurdi, Ajaokuta and Benin buses and critical transmission lines (which include Jos – Makurdi Transmission line, Ajaokuta - Benin and Ikeja West – Benin Transmission line) within the network. The load flow analysis also revealed that the system loses synchronism when the balanced three-phase fault was applied to these identified critical buses and lines. This implies that the Nigeria 330-kV transmission network is on a red-alert, and requires urgent control measures with the aim of enhancing the stability margin of the network to avoid system collapse. The results obtained showed that 33.33% transient stability improvement was achieved when the HVDC was controlled with the artificial neural network when compared to the PI controllers as can be seen by observing the dynamic response of the generators in the network. Also when compared with the results of other similar works (especially Hazra, Phulpin and Ernst, 2009), there is about 20% transient stability improvement. The voltage profile result and the damping were improved when the ANN was installed.