期刊名称：International Journal of Hybrid Information Technology
印刷版ISSN：1738-9968
出版年度：2015
卷号：8
期号：9
页码：171-184
DOI：10.14257/ijhit.2015.8.9.17
出版社：SERSC
摘要：Allocation of optimum active power is a backbone of power system generation planning and its high impact contribution is the need of current electrical utilities and power engineers need to browse this area in short and long term planning scenarios. Power demand requirements mapped to economic feasible solutions matching voltage profile, power demand, minimization of losses, voltage stability and improve the capacity of the system is the need of the hour. Modern techniques based on evolutionary computing, artificial intelligence, search method find their objectives in the area of economic load dispatch planning to reach global optimal solution for this multi-decision, multi-objective combinatorial problem subjected to different constraints. Many algorithms suffer from global convergence problem. To vanish this drawback, neuro inspired genetic hybrid algorithm (NIGHA) has been proposed in this paper to solve economic dispatch problem. Unlike other algorithms, NIGHA utilizes the weights of Neural Network to explore information and knowledge to train GA parameters to search for feasible region where optimal global solution converges. The suggested technique is tested on IEEE 25 bus system. Test results are compared with other techniques presented in literature. Proposed technique has outperformed other methods in terms of cost, computation time.
