基于群体智能算法的电力设备现场智能化运维检修研究

Research on on-site intelligent operation and maintenance of power equipment based on swarm intelligence algorithms

常规的电力设备运维检修方法以故障特征分析处理为主,但是多源数据融合降低了运维检修效率,不利于智能化运检场景的应用。因此,文章设计了基于群体智能算法的电力设备现场智能化运维检修方法。此方法基于群体智能算法构建电力设备运维检修模型,利用狼群、蚁群等群体智能算法,均衡检修工作量、设备可靠性、经济性三者之间的关系,生成最优目标函数。在求解电力设备智能化运维检修全局更新参数时,利用电力设备现场智能化运维检修的正反馈方式,寻求运维检修最优解,从而满足智能化运维检修需求。通过实例分析,验证了该方法的运维检修效率更高,经济性更高并能够应用于实际生活中。

Conventional maintenance methods for power equipment mainly focus on fault feature analysis and processing,but multi-source data fusion reduces maintenance efficiency and is not conducive to the application of intelligent maintenance scenarios.Therefore,this article designs an intelligent on-site operation and maintenance method for power equipment based on swarm intelligence algorithms.This method is based on swarm intelligence algorithms to construct a power equipment operation and maintenance model.It utilizes swarm intelligence algorithms such as wolf colony and ant colony to balance the relationship between maintenance workload,equipment reliability,and economy,and generate the optimal objective function.When solving the global update parameters for intelligent operation and maintenance of power equipment,the positive feedback method of on-site intelligent operation and maintenance of power equipment is used to seek the optimal solution for operation and maintenance,thereby meeting the needs of intelligent operation and maintenance.By analyzing examples,it has been verified that this method has higher efficiency and economy in operation and maintenance,and can be applied in practical life.