基于大数据的电力企业资产组维修策略优化研究

Research on maintenance strategy optimization of power enterprise asset group based on big data

在汇总过去若干年的电力设备故障数据的基础上,运用大数据分析方法,把故障预测技术引入到预防性维修的实践中,提出一种基于大数据的预防性维修策略。首先,根据由状态检测信息得到剩余寿命的预测结果,以预防性维修时的剩余寿命为阀值制定预防性维修策略。然后,根据更新过程理论,建立以电力设备的预防性维修阀值和预测间隔期为优化变量,综合考虑电力设备维修成本、客户满意度、电量销售、停电损失、维修时机选择等约束条件呢,以电力设备平均维修费用最小和电力设备可用度最大为优化目标的预防性维修优化模型。采用人群搜索算法进行优化求解,得到系统最佳的预防性维修阀值和维修预测间隔期。最后,通过引入算例,对所建模型优化仿真求解,得到电力设备最佳的预测周期,在保证电力设备可用度的同时,使电力设备的平均维修费用最小,验证了所建模型的可行性和有效性,从而提高电力企业的整体效益。

The fault data of power equipment in the past several years are summarized, and the method of large data analysis is used to introduce the fault prediction technology into the practice of preventive maintenance, and a preventive maintenance strategy based on large data is proposed. First, the residual life prediction results are obtained from the state detection infformation, and the preventive maintenance strategy is established based on the remaining life of preventive maintenance. Then, the optimal preventive maintenance model of the power equipment, such as the cost of maintenance, the sale of electricity and the loss of the power outage, is taken into consideration, and the optimal preventive maintenance threshold and the interval of maintenance prediction are obtained. Finally, by introducing an example, the optimal prediction period of the power equipment is obtained by the optinfization simulation of the model. The average maintenance cost of the power equipment is minimized, the feasibility and effectiveness of the model are verified, thus the overall benefit of the power enterprise is improved.