For improving the energy-saving power generation scheduling in the electric power industry energy saving and emission reduction capability and enhance the safety and economy of power energy structure, we based on neighborhood opportunity learning multi intelligent power quickly swarm intelligent control algorithm is presented in this paper. Firstly, the proposed algorithm is based on the characteristic value of each neighborhood characteristic value which is obtained by chance mining. The system can obtain the real-time sensing data of each neighborhood power station. This algorithm establishes a neighborhood modular architecture. The algorithm in the opportunity to automate the operation of the module information on the opportunity to learn. Secondly, based on the opportunity to learn and fast variation of the power vector, the algorithm is derived to control the power generation fast group. The algorithm of the multiple power generation control system through the opportunity to control and quickly set the mapping link. Finally, the experimental results show that the proposed algorithm has a significant advantage in real-time, reliability and cost of intelligent management and fast control of power generation to adapt to a variety of power generation devices.
Multi intelligent fast power Neighborhood set Opportunistic learning Crowd control
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This work is supported in part by the National Natural Science Foundation of China (51405498) and Natural Science Foundation of Shaanxi Province, China (2013JQ8023).
Compliance with ethical standards
Conflict of interest
The authors declare that there is no conflict of interests regarding the publication of this paper.
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