考虑故障状态劣化的并行多个电力设备的预测性维护调度
Predictive Maintenance Scheduling of Parallel Multiple Power Equipment Considering Fault State Deterioration
耿苏杰 1王秀利2
作者信息
- 1. 江苏大学管理学院,江苏镇江 212000
- 2. 南京理工大学经济与管理学院,江苏南京 210094
- 折叠
摘要
为解决电厂中多个并联设备的维护问题,提出一种基于故障状态的预测性维护调度方法.首先,结合设备当前状态和故障自身劣化特点的影响,改进时变函数预测故障的劣化状态;依据故障状态划分维护优先级,考虑维护资源、供电持续性和电能产量等因素的约束,以总维护成本的最小化为目标构建调度模型;考虑可维护时间的连续性特点,提出两阶段算法对模型求解,先划分维护时间窗将该问题转化为时间段的组合优化问题,再结合全枚法和经济性原则确定最优维护方案.最后,以云南电网中多台变压器的维护为例,对该方法的有效性进行测试和分析.结果显示,相较于传统的基于先故障先维修原则的事后维修模式,本文以故障状态为基础的预测性维护方法在降低维护成本和提高系统稳定性方面具有明显的优越性.
Abstract
In order to solve the maintenance problem of multiple parallel equipment in power plant,a predictive maintenance scheduling method is proposed based on fault state.Firstly,combined with the influence analysis of the current status of equipment and the deterioration characteristics of fault itself,a time-varying function is improved to predict the state deterioration of fault.Then,the maintenance priority is divided based on fault state,considering the constraints of maintenance resources,power supply continuity and power output,the scheduling model is constructed with the objective of minimizing the total maintenance cost.Considering the continuity of maintenance time,a two-stage algorithm is proposed to solve the model,and this problem is transformed into a combinatorial optimization problem of time period by the division of maintenance time windows,and then the optimal maintenance scheme is selected by combining the full enumeration method and the principle of economy.Finally,taking the maintenance of multiple transformers in Yunnan power grid for example,the effectiveness of the proposed method is tested and analyzed.Compared with the traditional maintenance mode,this method has obvious advantages in reducing maintenance cost and improving system stability.
关键词
并行多个设备/预测性维护调度/故障状态预测/维护时间窗Key words
Parallel Multiple Equipment/Predictive Maintenance Scheduling/Fault State Prediction/Maintenance Time Window引用本文复制引用
基金项目
国家自然科学基金面上项目(71871118)
江苏省高等学校基础科学(自然科学)研究面上项目(23KJB30004)
江苏省高校哲学社会科学研究一般项目(2021SJA2054)
出版年
2024
NETL
NSTL
万方数据