CAJ | 학술논문

讨论了大型电站具有随机时变特性过程的状态监测与故障诊断.基于多变量统计过程(MSPC)理论的主元素分析(PCA)技术,分辨故障事件的原因与结果.为适应随机时变特性的过程特点,将典型的PCA与改进的递推和快速滑动窗型PCA算法进行了比较.给出了递推快速滑动窗型算法(MWPCA),通过与典型的PCA算法在故障诊断应用中的比较,表明了新算法对过程特性变化的自适应能力和计算效率.给出了在现代电站过程中应用FMWPCA算法将诊断与过程特性变量结合的图示方法应用实例.
토론료대형전참구유수궤시변특성과정적상태감측여고장진단.기우다변량통계과정(MSPC)이론적주원소분석(PCA)기술,분변고장사건적원인여결과.위괄응수궤시변특성적과정특점,장전형적PCA여개진적체추화쾌속활동창형PCA산법진행료비교.급출료체추쾌속활동창형산법(MWPCA),통과여전형적PCA산법재고장진단응용중적비교,표명료신산법대과정특성변화적자괄응능력화계산효솔.급출료재현대전참과정중응용FMWPCA산법장진단여과정특성변량결합적도시방법응용실례.
The monitoring and fault diagnosis of large-scale power plant processes that exhibit non-stationary and/or time-varying behavior were discussed.The work considered statistically-based monitoring technique, which was related to the multivariate statistical process control (MSPC) framework. Particular focus is on principal component analysis (PCA),as this technique allows distinguishing between cause and effect variables consequently. To demonstrate the difficulties of monitoring processes with non-stationary and time-varying behavior, the use of conventional PCA was compared with its recursive and fast moving-window counterparts.A recently proposed recursive moving window technique was employed because of its ability in adapting to process changes and its computational efficiency.The advance in fault detection was demonstrated by comparing fast moving-window PCA (MWPCA) with the conventional PCA.In addition, this paper proposes to plot the scaled variables in conjunction with fast MWPCA for fault diagnosis, which proves to be effective in power utility process application.