Please use this identifier to cite or link to this item: http://localhost:8081/xmlui/handle/123456789/1915
Authors: Singh, Ashish
Issue Date: 2012
Abstract: Electrical systems are evolving from centralized bulk generating stations towards decentralized systems known as distributed generation (DG). With the impending deregulated environment and limiting fossil fuels, electric utilities are moving towards small non conventional generation technologies which produce energy with less environmental impact and are highly efficient. Wind based DG is an environmental attractive form of renewable energy source. However, the intermittent nature of wind has to be to be considered. The prime function of electrical power system is to meet load demand with an acceptable degree of reliability and quality. Load demand in any time period is stochastic process, so there is need to model the load with considering its randomness. Distribution Systems causes a loss about 3-13% of the total power generation in developing nations. Hence there is an urgent need to explore the ways for reduction of losses. Implementation of DG by proper allocation into existing distribution system results in reduction of power losses. The problem of loss minimization is usually formulated as a single objective optimization problem with several constraints. The resulting problem is a complex and non-linear optimization problem. So, proper optimization techniques are needed to be applied for obtaining solution. In. this thesis, therefore, a genetic algorithm (GA) based technique has been proposed for the optimal placement of multiple DGs in distribution network for minimizing the power losses of the system. Constraints on bus voltage magnitudes, line loadings and DG capacity have been taken into consideration. The proposed algorithm is coded using MATLAB. The proposed method has been tested on a 33-bus IEEE distribution system to check its effectiveness and validity. The obtained results reveal that the proposed method is promising in optimally locating DG in the system.
Other Identifiers: M.Tech
Research Supervisor/ Guide: Khatod, D. K.
metadata.dc.type: M.Tech Dessertation
Appears in Collections:MASTERS' THESES (HRED)

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