| dc.contributor.author | Ssempala, Denis | |
| dc.date.accessioned | 2021-02-26T08:17:50Z | |
| dc.date.available | 2021-02-26T08:17:50Z | |
| dc.date.issued | 2020-12-18 | |
| dc.identifier.citation | Ssempala, D. (2020). Optimal capacitor placement using genetic Algorithm on a Radial distribution feeder. (Unpublished undergraduate dissertation) Makerere University: Kampala, Uganda | en_US |
| dc.identifier.uri | http://hdl.handle.net/20.500.12281/9033 | |
| dc.description | The analysis of a distribution system is an important activity, as distribution system provide a link between the generation centers and consumers. A distribution network normally consists of the main feeders and the lateral distributors. main feeders originate from substations and pass through major load centers in the different areas. Lateral distributors connect to individual transformers at their ends from which consumers are connected. Many distribution systems used in practice are defined as radial distribution systems. Radial distribution systems are popular because of their easy design and generally low cost.
The power distribution system is constantly faced with an ever growing load demand which is mainly inductive, this increasing load is resulting into an increased burden and reduced voltages on the network. These voltages at the different nodes or buses reduce as the distance of the distribution network increases from the substations. This decrease in voltage is mainly due to insufficient amount of reactive power and thus causing power losses on the network. These losses are more significant in distribution networks than in a transmission system. Studies have indicated that much of the total power generated is wasted as I2R losses at the distribution level.
Thus to avoid voltage collapse and improve the overall efficiency of power delivery, reactive compensation is required. Several arrangements have been tried out to reduce these losses like network reconfiguration and shunt capacitor placement. The commonly used method being capacitor placement. The shunt capacitors supply part of the reactive power demand, thereby reducing the current on the network, conventional models and techniques for capacitor placement have been used, but these are time consuming, increase the complexity of the systemand do not yield the best results in terms of voltage improvement and loss reduction. Hence the urgent need for capacitor optimization. | en_US |
| dc.description.abstract | The power generated by generation centers is transferred to load centers through transmission or distribution systems and it is not meeting the load demand mainly due to the losses and poor voltage regulation occurring in the networks. One of the most efficient methods of reduction of power losses on the power network is placement of capacitors. Capacitor banks have previously been installed on the network, however conventional methods have been employed achieving little or no technical loss reduction which calls for optimization of capacitors in terms of location and size. The project is carried out with an objective of identifying the optimal location and sizes of the capacitors in a radial distribution network to have a reduction in power cost and capacitor costs. The network data is modelled and a load flow before optimization is carried out. The results are used to compute the loss sensitivity factors which identify the potential locations for capacitor placement. The objective function to be minimized is the sum of power loss cost function and the capacitor cost function which is formulated using the network constraints. genetic algorithm is developed and implemented to determine the sizes of capacitors while minimizing power loss cost and investment cost. Using the genetic algorithm technique, the technical performance of the network is improved, there is a significant improvement in the voltage regulation on the network and a significant reduction in the network power losses and the operating cost. Implementation aspects and important results have been presented to highlight the working of the algorithm. The algorithm is effective in determining optimal locations and sizes of capacitors as compared to results obtained from the conventional methods being used. | en_US |
| dc.language.iso | en | en_US |
| dc.subject | Optimal capacitor placement | en_US |
| dc.subject | Genetic Algorithm | en_US |
| dc.subject | Radial distribution feeder. | en_US |
| dc.title | Optimal capacitor placement using genetic Algorithm on a Radial distribution feeder. | en_US |
| dc.type | Thesis | en_US |
