Power System Operation Status Based on MRMR Algorithm and Multiple ELM

Abstract

To validate the operation status of the power system after encountering faults and restoring it to equilibrium, an efficient and accurate evaluation method is raised to promote the accuracy and efficiency of operation status evaluation model. The study first introduced the minimum redundancy maximum correlation algorithm and multiple extreme learning machine, and then constructed a multi-layer evaluation model grounded on multiple extreme learning machine. The experiment findings indicated that 1225 samples were sent to the second layer after the first evaluation layer, and 531 samples were sent to the third layer after the second evaluation layer. Only 10 samples could not be evaluated at the fifth level. Moreover, there were only 2 cases of missed judgments in the fifth layer. The experiment data indicated that the probability of missed judgments in the hierarchical evaluation model was very small, and it could evaluate almost all samples. This demonstrates that the power system operation state evaluation method based on the minimum redundancy maximum correlation algorithm and multiple extreme learning machine proposed by the research can timely and effectively evaluate feature samples, providing strong support for the stable operation of the power system.

Keywords

• MRMR
• ELM
• Power system
• Operating status