JavaScript is disabled for your browser. Some features of this site may not work without it.
| dc.contributor.author | YOSEF, BIRARA WUBET | |
| dc.date.accessioned | 2024-05-20T08:11:12Z | |
| dc.date.available | 2024-05-20T08:11:12Z | |
| dc.date.issued | 2023-06 | |
| dc.identifier.uri | http://ir.bdu.edu.et/handle/123456789/15806 | |
| dc.description.abstract | The stability of a power system is essential to maintain an uninterrupted and uniform supply of electricity to consumers. Various disturbances in the power system, such as threephase to-ground faults, sudden load changes, and transmission line outages, can cause blackouts and synchronous generator instability. Tana Beles Hydropower Plant (TBHPP), being the base load hydropower plant for Ethiopia, must be precisely analyzed and its transient stability be enhanced following large disturbances to maintain power system stability continually. For this reason, this thesis focuses on these issues and uses an Artificial Neural Network (ANN) for transient stability assessment (TSA) and an Interline Power Flow Controller (IPFC) with an ANN controller for transient stability enhancement in TBHPP. The ANN was trained using 80% of the extracted data, and the remaining 20% was used for testing. 9828 data was extracted from different fault types, locations, duration times, impedances, and load variations. For transient stability enhancement, the performance of IPFC with ANN controller has been evaluated by considering a three-phase to ground fault, outage of a transmission line, and a sudden load change. For instance, the transient stability is studied at a three-phase to ground fault, the ANN-based IPFC has a better performance to reduce the settling time and overshoot percentage when compared to a system without controller and PI-based IPFC. The ANN-based IPFC reduced the settling time of rotor angle, rotor speed, output active power, and electromagnetic torque by 65.251%, 65.190%, 59.542%, and 61.377% respectively, compared to a system without a controller and by 59.358%, 57.729%, 51.382%, and 54.771%, respectively, compared to a system with PI-based IPFC. The overshoot percentage of rotor angle, rotor speed, output active power, and electromagnetic torque with ANN-based IPFC was reduced by 17.264%, 41.667%, 50.090%, and 85.099%, respectively, compared to a system without a controller and by 7.914%, 64.646%, 42.798%, and 36.885%, respectively, compared to a system with PI-based IPFC. The ANN demonstrates high performance in assessing transient stability and ANN-based IPFC provides better results for enhancing transient stability compared to a PI-based IPFC and without a controller. The program was developed using MATLAB script, and the simulation was executed using MATLAB/Simulink environment. Keywords: Transient stability assessment (TSA), TSI, transient stability enhancement (TSE), ANN, IPFC, TBHPP, MATLAB/Simulink environment | en_US |
| dc.language.iso | en_US | en_US |
| dc.subject | Electrical and Computer Engineering | en_US |
| dc.title | TRANSIENT STABILITY ASSESSMENT AND ENHANCEMENT OF HYDROPOWER PLANT USING ARTIFICIAL NEURAL NETWORK [CASE STUDY: TANA-BELES HYDRO POWER PLANT] | en_US |
| dc.type | Thesis | en_US |
