Research on Algorithm of Distributed Reactive Power Optimization Based on Cloud Computing and Improved NSGA-II

Abstract:

A distributed reactive power optimization algorithm is put forward based on cloud computing and improved NSGA-II (fast non-dominated sorting genetic algorithm) in this paper. It is designed to solve problem of multi-objective reactive power optimization with huge amounts of data in power grid, whose difficulties lie in local optimum and slow processing speed. First, NSGA-II's crossover and mutation operator are improved based on Cloud Model, so as to satisfy the adaptive characteristics. In this way, we improved global optimization ability and convergence speed when dealing with large-scale reactive power optimization. Second, we introduced cloud computing, parallelized the proposed algorithm based on MapReduce programming framework. In this way, we achieved distributed improved NSGA-II algorithm, effectively improved the calculation speed of handling massive high-dimensional reactive power optimization. Through theoretical study demonstrated the superiority of the algorithm to solve the Multi-Objective reactive power optimization.

• Periodical: Applied Mechanics and Materials (Volumes 644-650)
• Pages: 1927-1930
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.644-650.1927
• Online since: September 2014
• Authors: Bao Yi Wang*, Hao Yin, Shao Min Zhang
• Keywords: Cloud Computing, Cloud Model, NSGA-II, Parallelization, Reactive Power Optimization

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