基于模糊熵的核电站瞬态识别方法

为保障核电站安全经济运行，需及时准确地识别核电站出现的异常。本文通过处理关键变量的时间序列数据，对瞬态过程进行识别：利用模糊熵度量时间序列复杂度的能力，判断系统是否处于正常状态；进而利用互模糊熵度量两时间序列相似度的能力，对出现的瞬态进行类型识别。利用模块式高温气冷堆核电站仿真机的数据验证了本方法的可行性和有效性，结果表明本文方法可有效进行瞬态识别，且不需复杂的训练过程。

Fuzzy Entropy Based Transient Identification in Nuclear Power Plant

For safe and economical operation of nuclear power plants (NPPs), the occurring anomalies should be promptly and correctly identified. In this paper, the transients were identified by processing time series of critical variables. First, based on its ability to measure the complexity of time series, the fuzzy entropy (FuzzyEn) was used to determine whether the system was in normal state. Then cross fuzzy entropy was employed for classifying the occurring transients, using its ability to characterize the similarity between two time series. The feasibility and effectiveness were verified by simulator data of pebble-bed modular high temperature gas-cooled reactor nuclear power plant (HTR-PM). It is demonstrated that the proposed method is effective for transient identification and it dispenses with a complex training phase.