基于AFWA的二次系统故障关联分析参数优化

Parameter Optimization of Secondary System Fault Correlation Analysis Based on AFWA

智能站二次系统出现故障时,可以利用关联分析法挖掘历史数据来得到故障分析结果,传统关联分析算法应用在智能站二次故障数据处理的过程时,需要设置支持度、置信度等指标来筛选合适的规则,指标需要人为设置,这个过程较为繁琐,同时还会产生大量冗余规则,分析时需要耗费大量人力。基于自适应烟花算法,提出了一种AFWA结合ITL-mine算法的关联优化方法,借助历史数据,利用目标函数实现优化参数,实现了对关联规则的优化,提高了规则的可靠性,同时还可以保证分析结果覆盖整个数据,最后引入规则筛选策略,设置最小相似度指标,对分析结果进行筛选,最大程度上排除冗余规则,排除衍生信号,提高规则的可阅读性,确保结果容易理解。经过验证,该方法相比于其它传统优化算法,收敛性能更好,在一定程度上节约了人力,同时保证了分析结果准确可靠。

When the secondary system of the intelligent station fails, the association analysis method can be used to mine the historical data to get the fault analysis results. When the traditional association analysis algorithm is applied to process the secondary fault data of the intelligent station, indicators such as support degree and confidence level need to be set to screen the appropriate rules. The setting of indicators is manual, causing the process to be cumbersome. At the same time, a large number of redundant rules will be generated so a lot of manpower will be consumed. Based on the adaptive fireworks algorithm, this paper proposes an association optimization method in which AFWA is combined with ITL mine algorithm. With the help of historical data, the objective function was used to optimize the parameters, which improves the reliability of the rules and ensures that the analysis results cover the whole data. Finally, the rule screening strategy was introduced to set the minimum similarity index. The analysis results were screened to eliminate redundant rules and derivative signals to the greatest extent, so as to improve the readability of rules and ensure that the results are easy to understand. The result verifies that, compared with other traditional optimization algorithms, this method has better convergence performance so that it can save manpower to a certain extent and ensure the accuracy and reliability of the analysis results.