Browsing by Author "Mulye Getu"

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    TRANSMISSION LINE VOLTAGE PROFILE IMPROVEMENT AND POWER LOSS REDUCTION BY OPTIMAL PLACEMENT OF STATIC SYNCHRONOUS COMPENSATOR USING TEACHING LEARNING BASED ALGORITHMS
    (Hawassa University, 2022-03-18) Mulye Getu
    The power transmission system transports electric power generated at generation plant to distribution system. The increasing power demand of customers causes the power transmission system to become stressed when connected to distribution system. This leads to voltage instability and, further to, transmission power loss, which can lead to power system malfunction and system collapse. Most bus voltages are not within acceptable limits, and the voltage index of the buses indicates that the network is prone to voltage instability issues. The objective of this thesis is to determine the best placement for a static synchronous compensator (STATCOM), which is one of the Flexible AC Transmission Systems (FACTS) devices, on the Alaba to Bukuluguma Transmission System network to minimize transmission line loss, improve voltage profile, and enhance power transfer capacity. The power flow analysis by Newton Raphson algorithm in MATLAB environment is used and Teaching Learning-based optimization techniques (TLBO) are adopted for optimal sizing and location of the device. The obtained results were compared to those reported in the literature for conventional optimization techniques. The optimal location and size of STATCOM for the Alaba to Bukuluguma transmission network were identified using the genetic algorithm (GA) and particle swarm optimization (PSO) methods accordingly, 25.8MVar at bus 6 and 25.5 MVar, at bus 5 respectively. According to the TLBO technique, bus 4 with 25MVar is the best placement and size of STATCOM in the network. The TLBO approach, as previously indicated, performs better in terms of reducing real and reactive power losses. The test system's real power loss reduction is 39.8 percent, while the reactive power loss reduction is 49.3 percent. In addition, the worst-case minimum voltage level has been enhanced from 0.878pu to 0.953pu. STATCOM control is established in this study utilizing artificial intelligence (AI) and an artificial neural network (ANN), which is based on TLBO's optimal values. In general, simulation results demonstrate that the suggested approach is effective in keeping all bus voltage magnitudes within the IEEE permissible limit while also drastically reducing power losses.