@inproceedings{scholars1558,
            note = {cited By 34; Conference of 2011 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2011 ; Conference Date: 17 December 2011 Through 18 December 2011; Conference Code:88620},
            year = {2011},
         address = {Jakarta},
           title = {A comparative study of HNN and hybrid HNN-PSO techniques in the optimization of distributed generation (DG) power systems},
         journal = {ICACSIS 2011 - 2011 International Conference on Advanced Computer Science and Information Systems, Proceedings},
           pages = {195--199},
        keywords = {Alternative energy source; Comparative studies; Design constraints; DG system; Distributed generations; Multi objective; Optimization goals; Pollutant emission; Power balance; Storage battery, Computer science; Distributed power generation; Electric batteries; Electric power supplies to apparatus; Hopfield neural networks; Information systems; Multiobjective optimization; Photovoltaic cells, Particle swarm optimization (PSO)},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84857352345&partnerID=40&md5=58719eba784f7a55abfed09f9a836c09},
        abstract = {One of the major advances in recent years is the integration of multiple alternative energy sources, e.g., wind turbine generators, photovoltaic cell panels and fuel-fired generators, equipped with storage batteries to form a distributed generation (DG) power system. Nevertheless, cost effectiveness, reliability and pollutant emissions are still major issues with DG systems. The optimization goal was to minimize cost, maximize reliability and minimize emissions (multi-objective) subject to the constraints (power balance and design constraints). This paper discusses the optimization that was performed using Hopfield Neural Networks (HNN), and the Hybrid Hopfield Neural Network-PSO (HNN-PSO) algorithms. {\^A}{\copyright} 2011 Universitas Indonesia.},
          author = {Elamvazuthi, I. and Ganesan, T. and Vasant, P.},
            isbn = {9789791421119}
}