Improved cluster fault detection of cloud data in ant colony-guided power system

Improved cluster fault detection of cloud data in ant colony-guided power system

Zhang Xianfong

The traditional ant colony algorithm produces the reversal mutation in the iterative process, and the new node and link may join in the cloud at any time, which brings great difficulty to cloud computing and fault data prediction and detection of the cloud data of the power grid system, and the congestion control results in the poor clustering effect. Combining the characteristics of data processing in cloud computing, to improve the traditional ant colony algorithm, an improved cloud data clustering and fault detection algorithm for ant colony-guided power system is proposed, which determines the accuracy of the output probability according to the size of the random number of the gene, updates the full statistic of the State class, and obtains the probability and the initial probability of the fault characteristic. Perform cluster center update rules. Set up the Hadoop Cluster Cloud computing prototype system, in the open source cloud computing platform framework and the HBase power grid system database carries on the data acquisition and the algorithm realization. The simulation results show that the algorithm has good application performance in data clustering and fault detection.

Improved cluster fault detection of cloud data in ant colony-guided power system