|Title:||Reducing under-frequency load shedding in isolated power systems using neural networks. Gran Canaria : a case study||Authors:||Padron, S.
|UNESCO Clasification:||120304 Inteligencia artificial||Keywords:||Artificial intelligence
Isolated power systems
Neural network, et al
|Issue Date:||2016||Project:||Framework Para la Simulación de la Gestión de Mercado y Técnica de Redes Eléctricas Insulares Basado en Agentes Inteligentes. Caso de la Red Eléctrica de Gran Canaria.||Journal:||IEEE Transactions on Power Systems||Abstract:||Small isolated power systems often experience generator outages, which are responsible for the activation of the under-frequency load shedding scheme with the corresponding negative impact on electricity consumers and, hence, market loss. There are three main causes of this problem: the power system's low inertia, the speed governors' low capacity, and a poor size-ratio between generator and system. The most extensive research line in this area is focused on the optimization of the load shedding scheme, which is a partial solution. Another research line is presented to solve the problem from the point of view of the system operator. This paper proposes an online method to predict and correct possible load shedding by redistributing load dispatching. This proposal uses artificial intelligence techniques, in particular neural networks, and a special-purpose power system simulator. In order to evaluate the proposal, the achieved solution is applied to a real case study: the island of Gran Canaria. This application shows the improvement that might be achieved by implementing this simple method. The method proposed in this paper is strongly recommended for regions that have suitable geographical sites as well as energy problems similar to those of the Canary Islands (see tech. rep. “Map of the Canary Islands Power Systems” by Red Electrica de Espana).||URI:||http://hdl.handle.net/10553/52069||ISSN:||0885-8950||DOI:||10.1109/TPWRS.2015.2395142||Source:||IEEE Transactions on Power Systems [ISSN 0885-8950], v. 31 (1), p. 63-71|
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