China Safety Science Journal ›› 2024, Vol. 34 ›› Issue (9): 121-130.doi: 10.16265/j.cnki.issn1003-3033.2024.09.0960

• Safety engineering technology • Previous Articles     Next Articles

Research on power plant dust monitoring node coverage control based on improved genetic algorithm

WANG Bo(), SHANG Yuhang, YAO Lichao, JIANG Yongqing**()   

  1. School of Measurement and Control Technology and Communication Engineering, Harbin University of Science and Technology, Harbin Heilongjiang 150080, China
  • Received:2024-03-14 Revised:2024-06-19 Online:2024-09-28 Published:2025-03-28
  • Contact: JIANG Yongqing

Abstract:

In order to effectively reduce the risk of blind zones and lack of control in dust environment monitoring, optimize the node coverage control of the dust environment monitoring system in thermal power plants, and prolong the lifetime of WSN, an energy-saving optimization method based on improved genetic algorithms was proposed. Firstly, based on node coverage, total energy consumption of node deployment and total energy consumption of node communication and transmission, the network coverage quality objective function was constructed. Then, aiming at the problems of the local optimization and coding duplication existing in traditional genetic algorithms, the chromosome combination scheme of integer coding, the adaptive adjustment method of crossover and mutation probability and the elite retention strategy were proposed. Finally, the simulation comparison and analysis were performed to determine the optimized node number and distribution scheme. The results show that the improved genetic algorithm significantly improves the convergence speed. The number of iterations required is reduced to 20, and the fitness value is optimized by 52.18%. In the node deployment and coverage study, the optimized number of nodes is 42, the coverage rate is 97.28%, and the node dormancy rate is 76.19%, which effectively improves the energy-saving effect of the dust environmental monitoring system in the thermal power plant.

Key words: improved genetic algorithm, power plant dust, environment monitoring, node coverage control, wireless sensor network (WSN), elite retention strategy

CLC Number: