一种配电网基于模型诊断的最小碰集改进算法

An improved minimum set algorithm for model-based diagnosis of a distribution network

在配电网故障的基于模型诊断方法中,最小碰集计算对整个诊断过程具有较大影响。为了进一步提高基于模型诊断的效率和准确率,提出一种适合配电网拓扑结构的最小碰集改进算法。利用新的适应度函数保证粒子直接朝着最小碰集迭代,提高对有解空间的搜索效率。利用"特征学习"搜索策略减少对无解空间的搜索。在分析配电网各元件解析冗余关系之间联系的基础上,提出算法分层的理论依据和实现方法。算例表明,改进后最小碰集算法具有更短的求解时间和更高的准确率。将改进后的最小碰集算法应用到基于模型诊断中,故障诊断的效率和准确率得到一定提高。

In the model-based diagnosis method of distribution network faults, the minimum hitting set calculation has a great impact on the whole diagnostic process. A new minimum hitting set algorithm suitable for the distribution network topology is proposed to improve the efficiency and accuracy rate of model-based diagnosis. The new fitness function in the algorithm allows the particle iterating towards the minimum hitting set directly. As a result, the search efficiency for the solution space is improved correspondingly. A search strategy of "feature learning" is used to reduce the search for unsolved space. Analyzing the relation between analytic redundancy relations of distribution network components, the theoretical basis and implementation method of algorithm stratification are put forward. The example shows that the improved minimum hitting set algorithm has shorter solution time and higher accuracy. Applying the improved minimum hitting set algorithm to model-based diagnosis, the efficiency and accuracy of fault diagnosis are improved.