基于灰色理论和云模型的智能变电站二次设备状态评估方法

针对智能变电站二次设备故障机理的复杂多样性,传统的单一性指标评估难以有效挖掘出潜在的安全隐患。在相应研究基础上建立了二次设备状态评估递阶层次评估模型和指标体系,运用灰色理论中的灰色聚类建立二次设备的状态评估灰色聚类模型。为了节省人力同时兼顾状态评估的客观性,采用灰色预测GM(1,1)模型对专家评分进行离散建模以获得足够的评估样本。同时考虑到评估指标的不确定性,采用云模型构建灰色白化权函数,并结合层次分析法计算状态指标的组合权重。实际案例仿真结果表明,所提方法计算量小且操作简单,可以准确及时地为智能变电站二次设备和系统的状态检修工作提供科学指导。

A state assessment method for intelligent substation secondary equipment based on grey theory and cloud model

For the complex diversity of the secondary fault mechanism of the smart substation, the traditional single-factor assessment is difficult to effectively mine potential safety hazards. A hierarchical evaluation model and index system for the secondary equipment state assessment is established based on the corresponding research. And grey clustering in grey theory is used to establish a grey clustering model for the state assessment of secondary equipment. In order to save manpower and take into account the objectivity of the state assessment, the grey prediction GM (1,1) model is used to discretely model the expert scores to obtain enough evaluation samples. At the same time, taking into account the uncertainty of the evaluation index, a grey model is used to construct a grey white weight function, and combined with the analytic hierarchy process to calculate the combined weight of the state indicators. The actual case simulation results show that the method supposed in this paper is small in computation and simple in operation, and can provide scientific guidance for the state-repairing work of secondary equipment and systems in smart substations accurately and in a timely manner. This work is supported by Science and Technology Project of State Grid Corporation of China (No. 521200160021 and No. 521200170027).