{"id":707,"date":"2015-05-06T11:14:52","date_gmt":"2015-05-06T11:14:52","guid":{"rendered":"http:\/\/www.etfos.unios.hr\/ijeces\/?p=707"},"modified":"2017-03-23T08:04:46","modified_gmt":"2017-03-23T08:04:46","slug":"improving-the-conditions-in-a-radial-distribution-feeder-by-implementing-distributed-generation","status":"publish","type":"post","link":"http:\/\/www.etfos.unios.hr\/ijeces\/vol-6-no-1-2015\/improving-the-conditions-in-a-radial-distribution-feeder-by-implementing-distributed-generation\/","title":{"rendered":"Improving the Conditions in a Radial Distribution Feeder by Implementing Distributed Generation"},"content":{"rendered":"
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Marko Vukobratovi\u0107, Srete Nikolovski, Predrag Mari\u0107<\/h3>
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Abstract<\/strong><\/div>\n
Distribution feeder is the final stage in the delivery of electricity to consumers. The feeder can be radial or networked. Radial feeders leave the power station towards the consumers without any connection to other power supply. Networked feeders have multiple connections to other supply points. It is common for long radial feeders for voltage to drop along the way and for losses to increase with increasing consumer\u2019s power or the number of consumers. In order to minimize feeder losses and improve voltage profile distributed generation (DG) can be implemented. It is important to define the optimal location and power of distributed generation in a specific feeder to obtain its maximum potential benefits. This paper presents a solution for optimal DG placement by selecting the right terminal and power of DG using the Genetic Algorithm (GA) and the Artificial Neural Network (ANN) hybrid method. The method is tested on a part of Croatian distribution network and verified by DIgSILENT PowerFactory software and the analytical approach. The results and comparison thereof and presented in clear and legible form.\n<\/div>\n
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Keywords<\/strong><\/div>\n
artificial neural network, distribution feeder, distributed generation, genetic algorithm.<\/div>\n
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