摘要
针对传统专家系统在电网故障诊断应用中的局限性,提出一种基于模型诊断的电网故障最优诊断的查询方法。该方法按照基于因果关系的诊断思想,得到预设故障输出对应的预备候选诊断,然后根据故障后的电气信息从匹配的预设故障输出中确定候选诊断,最后,运用贝叶斯理论计算候选诊断的故障概率,并查询最大概率的候选诊断作为电网故障的最优诊断。该方法通过离线获得预备候选诊断,在线确认候选诊断的手段,缩减了诊断的时间,在利用贝叶斯定理处理诊断的不确定性时,将告警信息引入到模型诊断逻辑框架内计算元件的实际故障概率,提高了诊断的准确性。算例分析结果验证了所提方法的有效性和可行性。
In order to overcome the limitation of the traditional expert system applied in fault diagnosis of power system, a query method for optimal diagnosis of power system faults is proposed, which is based on model-based diagnosis. With the diagnosis acquiring method based on the causal relation, the preparation candidate diagnoses are obtained according to presupposed fault output. Then the candidate diagnoses are determined by the matched presupposed fault output accord- ing to electric data after faults occurring. Finally, the fault probabilities of candidate diagnoses are obtained by applying the Bayesian theory. Based on those probabilities, the optimal diagnosis is queried from the candidate diagnoses. The goal of reducing the diagnosis time is achieved by getting ready candidate diagnosis offline and getting candidate diagnostic online, and in order to improve the diagnosis accuracy, alarm data are introduced to logic framework of model-based di- agnosis for computing the actual probability of fault about power system element. The simulation results show that the proposed method is effective and practical.
出处
《高电压技术》
EI
CAS
CSCD
北大核心
2017年第4期1311-1316,共6页
High Voltage Engineering
基金
国家自然科学基金(61433004)
中央高校基本科研业务费专项资金(N130604001)~~
关键词
基于模型的诊断
电网故障诊断
最优诊断
因果关系
电气信息
告警信息
贝叶斯理论
model-based diagnosis
power system fault diagnosis
optimal diagnosis
causal relation
electrical data
alarm data
Bayesian theory
